An empirical calibration of the shear stress model for bedrock incision is presented, using field and hydrologic data from a series of small, coastal drainage basins near the Mendocino triple junction in northern California. Previous work comparing basins from the high uplift zone (HUZ, uplift rates around 4 mm/year) to ones in the low uplift zone (LUZ, ∼0.5 mm/year) indicates that the HUZ channels are about twice as steep for a given drainage area. This observation suggests that incision processes are more effective in the HUZ. It motivates a detailed field study of channel morphology in the differing tectonic settings to test whether various factors that are hypothesized to influence incision rates (discharge, channel width, lithology, sediment load) change in response to uplift or otherwise differ between the HUZ and LUZ. Analysis of regional stream gaging data for mean annual discharge and individual floods yields a linear relationship between discharge and drainage area. Increased orographic precipitation in the HUZ accounts for about a twofold increase in discharge in this area, corresponding to an assumed increase in the erosional efficiency of the streams. Field measurements of channel width indicate a power-law relationship between width and drainage area with an exponent of ∼0.4 and no significant change in width between the uplift rate zones, although interpretation is hampered by a difference in land use between the zones. The HUZ channel width dataset reveals a scaling break interpreted to be the transition between colluvial- and fluvial-dominated incision processes. Assessments of lithologic resistance using a Schmidt hammer and joint surveys show that the rocks of the study area should be fairly similar in their susceptibility to erosion. The HUZ channels generally have more exposed bedrock than those in the LUZ, which is consistent with protection by sediment cover inhibiting incision in the LUZ. However, this difference is likely the result of a recent pulse of sediment due to land use in the LUZ. Therefore, the role of sediment flux in setting incision rates cannot be constrained with any certainty. To summarize, of the four response mechanisms analyzed, the only factor that demonstrably varies between uplift rate zones is discharge, although this change is likely insufficient to explain the relationship between channel slope and uplift rate. The calibrated model allows us to make a prediction of channel concavity that is consistent with a previous estimate from slope–drainage area data. We show that the inclusion of nonzero values of critical shear stress in the model has important implications for the theoretical relationship between steady-state slope and uplift rate and might provide an explanation for the observations. This analysis underscores the importance of further work to constrain quantitatively threshold shear stress for bedrock incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/S0169-555X(02)00349-5","usgsCitation":"Snyder, N.P., Whipple, K.X., Tucker, G., and Merritts, D., 2003, Channel response to tectonic forcing: field analysis of stream morphology and hydrology in the Mendocino triple junction region, northern California: Geomorphology, v. 53, no. 1-2, p. 97-127, https://doi.org/10.1016/S0169-555X(02)00349-5.","productDescription":"31 p.","startPage":"97","endPage":"127","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":357789,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mendocino triple junction region","volume":"53","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10ecabe4b034bf6a803822","contributors":{"authors":[{"text":"Snyder, Noah P.","contributorId":198029,"corporation":false,"usgs":false,"family":"Snyder","given":"Noah","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":746395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whipple, Kelin X.","contributorId":138503,"corporation":false,"usgs":false,"family":"Whipple","given":"Kelin","email":"","middleInitial":"X.","affiliations":[{"id":12431,"text":"ASU","active":true,"usgs":false}],"preferred":false,"id":746396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Gregory E.","contributorId":39280,"corporation":false,"usgs":true,"family":"Tucker","given":"Gregory E.","affiliations":[],"preferred":false,"id":746397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merritts, D.J.","contributorId":73766,"corporation":false,"usgs":true,"family":"Merritts","given":"D.J.","affiliations":[],"preferred":false,"id":746398,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170382,"text":"70170382 - 2003 - Predicting rare plant occurrence in Great Smoky Mountains National Park, USA","interactions":[],"lastModifiedDate":"2022-07-21T16:03:44.478368","indexId":"70170382","displayToPublicDate":"2003-07-01T01:15:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2821,"text":"Natural Areas Journal","active":true,"publicationSubtype":{"id":10}},"title":"Predicting rare plant occurrence in Great Smoky Mountains National Park, USA","docAbstract":"We investigated the applicability of biometric habitat modeling to rare plant inventory and conservation by developing and field testing a geographically explicit model for Cardamine clematitis Shuttleworth ex A. Gray (mountain bittercress), an endemic plant of the southern Blue Ridge Mountains, USA. For each of 187 confirmed coordinates for C. clematitis in Great Smoky Mountains National Park, 13 habitat variables were measured with a geographic information system. These data were used to calculate Mahalanobis distances for each 30-m x 30-m pixel within the study area; small values of Mahalanobis distance represented site conditions similar to those of known locations of C. clematitis, whereas larger distance values represented dissimilar conditions. Following model development, we tested model performance by sampling 120 randomly distributed plots for C. clematitis presence. Logistic regression showed that Mahalanobis distance values were strongly related to C. clematitis occurrence (P = 0.039). Overall, 75% of all known occurrences of C. clematitis had associated Mahalanobis distance values below 17.7, and 95% of all occurrences were below 33.8; the median Mahalanobis distance value for the study area as a whole was 40.0. A habitat suitability cutoff value was defined which identified roughly 23,640 ha (19.5% of the study area) as suitable habitat. Although the model successfully predicted species absence in test plots with high Mahalanobis distance values, many sites with low values did not contain C. clematitis. Only 16.2% of test plots below the habitat suitability cutoff contained C. clematitis. The absence of C. clematitis from sites with low Mahalanobis distance values (low specificity) is not necessarily indicative of a poor model; metapopulation processes (e.g., recolonizations, local extinctions) have been shown to play a major role in presence or absence of many plant species. That may be partially the case with our model as evidenced by a relationship between C. clematitis presence and habitat patch size.
Muskellunge Lake is a productive, eutrophic lake because of high nutrient loading. Historical data indicate that water quality has only slightly degraded since the early 1970s, possibly because of phosphorus input from effluent from septic systems. A detailed phosphorus budget for the lake indicated that most of the phosphorus comes from natural sources?ground water and surface water flowing through relatively undeveloped areas surrounding the lake. Modeling results indicated that the natural input of phosphorus was sufficient to maintain the lake's eutrophic condition. Analysis of sediment cores confirmed that only small changes in nutrient and algal concentrations have occurred over the past 100 years; however, the analysis indicated that the macrophyte community has increased over this time period. The aeration system, installed to alleviate winter anoxia, maintains aerobic conditions throughout the main bays of the lake.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri034011","collaboration":"Prepared in cooperation with the Muskellunge Lake Association","usgsCitation":"Robertson, D.M., Rose, W., and Saad, D.A., 2003, Water quality and the effects of changes in phosphorus loading to Muskellunge Lake, Vilas County, Wisconsin: U.S. Geological Survey Water-Resources Investigations Report 2003-4011, vi, 18 p., https://doi.org/10.3133/wri034011.","productDescription":"vi, 18 p.","numberOfPages":"26","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":3989,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034011/","linkFileType":{"id":5,"text":"html"}},{"id":311313,"rank":101,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wri/wri034011/pdf/03-4011_Musky_Lake.pdf"},{"id":171681,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Vilas County","otherGeospatial":"Muskellunge Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.41669464111328,\n 45.92775190489084\n ],\n [\n -89.41669464111328,\n 45.97155499289284\n ],\n [\n -89.33378219604492,\n 45.97155499289284\n ],\n [\n -89.33378219604492,\n 45.92775190489084\n ],\n [\n -89.41669464111328,\n 45.92775190489084\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a07e4b07f02db5f9b3d","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596 dzrobert@usgs.gov","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":150760,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"dzrobert@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":236213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, William J. wjrose@usgs.gov","contributorId":2182,"corporation":false,"usgs":true,"family":"Rose","given":"William J.","email":"wjrose@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":236215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saad, David A. dasaad@usgs.gov","contributorId":121,"corporation":false,"usgs":true,"family":"Saad","given":"David","email":"dasaad@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":236214,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":50869,"text":"wri20034060 - 2003 - Availability and distribution of base flow in lower Honokohau Stream, Island of Maui","interactions":[],"lastModifiedDate":"2024-01-09T19:59:05.694862","indexId":"wri20034060","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4060","title":"Availability and distribution of base flow in lower Honokohau Stream, Island of Maui","docAbstract":"Honokohau Stream is one of the few perennial streams in the Lahaina District of West Maui. Current Honokohau water-use practices often lead to conflicts among water users, which are most evident during periods of base flow. To better manage the resource, data are needed that describe the availability and distribution of base flow in lower Honokohau Stream and how base flow is affected by streamflow diversion and return-flow practices. Flow-duration discharges for percentiles ranging from 50 to 95 percent were estimated at 13 locations on lower Honokohau Stream using data from a variety of sources. These sources included (1) available U.S. Geological Survey discharge data, (2) published summaries of Maui Land & Pineapple Company, Inc. diversion and water development-tunnel data, (3) seepage run and low-flow partial-record discharge measurements made for this study, and (4) current (2003) water diversion and return-flow practices. These flow-duration estimates provide a detailed characterization of the distribution and availability of base flow in lower Honokohau Stream.\r\n\r\nEstimates of base-flow statistics indicate the significant effect of Honokohau Ditch diversions on flow in the stream. Eighty-six percent of the total flow upstream from the ditch is diverted from the stream. Immediately downstream from the diversion dam there is no flow in the stream 91.2 percent of the time, except for minor leakage through the dam. Flow releases at the Taro Gate, from Honokohau Ditch back into the stream, are inconsistent and were found to be less than the target release of 1.55 cubic feet per second on 9 of the 10 days on which measurements were made. Previous estimates of base-flow availability downstream from the Taro Gate release range from 2.32 to 4.6 cubic feet per second (1.5 to 3.0 million gallons per day). At the two principal sites where water is currently being diverted for agricultural use in the valley (MacDonald's and Chun's Dams), base flows of 2.32 cubic feet per second (1.5 million gallons per day) are available more than 95 percent of the time at MacDonald's Dam and 80 percent of the time at Chun's Dam. Base flows of 4.6 cubic feet per second (3.0 million gallons per day) are available 65 and 56 percent of the time, respectively.\r\n\r\nA base-flow water-accounting model was developed to estimate how flow-duration discharges for 13 sites on Honokohau Stream would change in response to a variety of flow release and diversion practices. A sample application of the model indicates that there is a 1 to 1 relation between changes in flow release rates at the Taro Gate and base flow upstream from MacDonald's Dam. At Chun's Dam the relation between Taro Gate releases and base flow varies with flow-duration percentiles. At the 95th and 60th percentiles, differences in base flow at Chun's Dam would equal about 50 and 90 percent of the change at the Taro Gate.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri20034060","collaboration":"Prepared in cooperation with the State of Hawaii Office of Hawaiian Affairs","usgsCitation":"Fontaine, R.A., 2003, Availability and distribution of base flow in lower Honokohau Stream, Island of Maui: U.S. Geological Survey Water-Resources Investigations Report 2003-4060, vi, 37 p., https://doi.org/10.3133/wri20034060.","productDescription":"vi, 37 p.","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":424233,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_54914.htm","linkFileType":{"id":5,"text":"html"}},{"id":4638,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/wri/wri034060/","linkFileType":{"id":5,"text":"html"}},{"id":178396,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Maui, lower Honokohau Stream","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.6883851856813,\n 21.048412044465138\n ],\n [\n -156.6883851856813,\n 20.760626004410966\n ],\n [\n -156.47231226942318,\n 20.760626004410966\n ],\n [\n -156.47231226942318,\n 21.048412044465138\n ],\n [\n -156.6883851856813,\n 21.048412044465138\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4aa8e4b07f02db66799a","contributors":{"authors":[{"text":"Fontaine, Richard A. rfontain@usgs.gov","contributorId":2379,"corporation":false,"usgs":true,"family":"Fontaine","given":"Richard","email":"rfontain@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":242510,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":51399,"text":"ofr03253 - 2003 - Structural plays in Ellesmerian sequence and correlative strata of the National Petroleum Reserve, Alaska","interactions":[],"lastModifiedDate":"2023-06-23T14:04:19.42066","indexId":"ofr03253","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","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":"2003-253","title":"Structural plays in Ellesmerian sequence and correlative strata of the National Petroleum Reserve, Alaska","docAbstract":"Reservoirs in deformed rocks of the Ellesmerian sequence in southern NPRA are assigned to two hydrocarbon plays, the Thrust-Belt play and the Ellesmerian Structural play. The two plays differ in that the Thrust-Belt play consists of reservoirs located in allochthonous strata in the frontal part of the Brooks Range fold-and-thrust belt, whereas those of the Ellesmerian Structural play are located in autochthonous or parautochthonous strata at deeper structural levels north of the Thrust-Belt play. Together, these structural plays are expected to contain about 3.5 TCF of gas but less than 6 million barrels of oil.
\nThese two plays are analyzed using a two-stage deformational model. The first stage of deformation occurred during the Neocomian, when distal strata of the Ellesmerian sequence were imbricated and assembled into deformational wedges emplaced northward onto regionally south-dipping authochon at 140-120 Ma. In the mid-Cretaceous following cessation of the deformation, the Colville basin, the foreland basin to the orogen, was filled with a thick clastic succession. During the second stage of deformation at about 60 Ma (early Tertiary), the combined older orogenic belt-foreland basin system was involved in another episode of north-vergent contractional deformation that deformed pre-existing stratigraphic and structurally trapped reservoir units, formed new structural traps, and caused significant amounts of uplift, although the amount of shortening was relatively small in comparison to the first episode of deformation.
\nHydrocarbon generation from source strata (Shublik Formation, Kingak Shale, and Otuk Formation) and migration into stratigraphic traps occurred primarily by sedimentary burial principally between 100-90 Ma, between the times of the two episodes of deformation. Subsequent burial caused deep stratigraphic traps to become overmature, cracking oil to gas, and some new generation to begin progressively higher in the section. Structural disruption of the traps in the Early Tertiary is hypothesized to have released sequestered hydrocarbons and caused remigration into newly formed structural traps formed at higher structural levels. Because of the generally high maturation of the Colville basin at the time of the deformation and remigration, most of the hydrocarbons available to fill traps were gas.
\nIn the the Thrust-Belt play, the primary reservoir lithology is expected to be dolomitic carbonate rocks of the Lisburne Group, which contain up to 15% porosity. Antiformal stacks of imbricated Lisburne Group strata form the primary trapping configuration, with chert and shale of the overlying Etivluk Group forming seals on closures. Traps are expected to have been charged primarily with remigrated gas, but oil generated from local sources in the Otuk Formation may have filled some traps at high structural levels. The timing for migration of gas into traps is excellent, but only moderate for oil because peak oil generation for the play as a whole occurred 30 to 40 m.y. before trap formation. Reservoir and seal quality in the play are questionable, reducing the likelyhood of hydrocarbon accumulations being present in the play. Our analysis suggests that the play will hold 5.7 million barrels of technically recoverable oil and 1.5 TCF gas (mean values).
\nIn the Ellesmerian Stuctural play, the primary reservoir lithologies will be dolomitic carbonate rocks of the Lisburne Group and, less likely, clastic units in the Ellesmerian sequence. Traps in the play are anticlinal closures caused by small amounts of strain in the footwall below the basal detachment for most early Tertiary thrusting. Because these traps lie beneath the main source rock units (Shublik, Kingak, lower Brookian sequence), reservoirs that are juxtaposed by faulting against source-rock units are expected to have the most favorable migration pathways. The charge will be primarily remigrated gas; no oil is expected because of the great depths (15,000 to 26,000 ft) and consequent high thermal maturity of this play. Although the the probability of charge and timeliness of trap formation and gas remigration are excellent, seal and reservoir qualities are anticipated to be poor. Our analysis suggests that about 2.0 TCF of techncially recoverable gas can be expected in the play.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr03253","usgsCitation":"Moore, T.E., and Potter, C.J., 2003, Structural plays in Ellesmerian sequence and correlative strata of the National Petroleum Reserve, Alaska: U.S. Geological Survey Open-File Report 2003-253, 40 p., https://doi.org/10.3133/ofr03253.","productDescription":"40 p.","numberOfPages":"58","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":178739,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr03253.jpg"},{"id":285706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2003/0253/pdf/of03-253.pdf"},{"id":4406,"rank":3,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2003/0253/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alaska","otherGeospatial":"National Petroleum Reserve","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -160.0,68.0 ], [ -160.0,71.0 ], [ -150.0,71.0 ], [ -150.0,68.0 ], [ -160.0,68.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b13e4b07f02db6a37bd","contributors":{"authors":[{"text":"Moore, Thomas E. 0000-0002-0878-0457 tmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-0878-0457","contributorId":1033,"corporation":false,"usgs":true,"family":"Moore","given":"Thomas","email":"tmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":243460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Potter, Christopher J. 0000-0002-2300-6670 cpotter@usgs.gov","orcid":"https://orcid.org/0000-0002-2300-6670","contributorId":1026,"corporation":false,"usgs":true,"family":"Potter","given":"Christopher","email":"cpotter@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":243459,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":53121,"text":"wri034159 - 2003 - Aquifer tests and simulation of ground-water flow in Triassic sedimentary rocks near Colmar, Bucks and Montgomery Counties, Pennsylvania","interactions":[],"lastModifiedDate":"2018-02-26T15:37:36","indexId":"wri034159","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4159","title":"Aquifer tests and simulation of ground-water flow in Triassic sedimentary rocks near Colmar, Bucks and Montgomery Counties, Pennsylvania","docAbstract":"This report presents the results of a study by the U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency to evaluate ground-water flow in Triassic sedimentary rocks near Colmar, in Bucks and Montgomery Counties, Pa. The study was conducted to help the U.S. Environmental Protection Agency evaluate remediation alternatives at the North Penn Area 5 Superfund Site near Colmar, where ground water has been contaminated by volatile organic solvents (primarily trichloroethene). The investigation focused on determining the (1) drawdown caused by separately pumping North PennWater Authority wells NP–21 and NP–87, (2) probable paths of groundwater movement under present-day (2000) conditions (with NP–21 discontinued), and (3) areas contributing recharge to wells if pumping from wells NP-21 or NP–87 were restarted and new recovery wells were installed. Drawdown was calculated from water levels measured in observation wells during aquifer tests of NP–21 and NP–87. The direction of ground-water flow was estimated by use of a three-dimensional ground-water-flow model.
Aquifer tests were conducted by pumping NP–21 for about 7 days at 257 gallons per minute in June 2000 and NP–87 for 3 days at 402 gallons per minute in May 2002. Drawdown was measured in 45 observation wells during the NP–21 test and 35 observation wells during the NP–87 test. Drawdown in observation wells ranged from 0 to 6.8 feet at the end of the NP–21 test and 0.5 to 12 feet at the end of the NP–87 test. The aquifer tests showed that ground-water levels declined mostly in observation wells that were completed in the geologic units penetrated by the pumped wells. Because the geologic units dip about 27 degrees to the northwest, shallow wells up dip to the southeast of the pumped well showed a good hydraulic connection to the geologic units stressed by pumping. Most observation wells down dip from the pumping well penetrated units higher in the stratigraphic section that were not well connected to the units stressed by pumping. The best hydraulic connection to the pumped wells was indicated by large drawdown in observation wells that penetrate the water-bearing unit encountered below 400 feet below land surface in wells NP–21 and NP–87. The hydraulic connection between wells NP–21 (or NP–87) and observation wells in the southern area of ground-water contamination near the BAE Systems facility is good because the observation wells probably penetrate this water-bearing unit.
A 3-dimensional, finite-difference, groundwater- flow model was used to simulate flow paths and areas contributing recharge to wells for current (2000) conditions of pumping in the Colmar area and for hypothetical situations of pumping suggested by the U.S. Environmental Protection Agency that might be used for remediation. Simulations indicate that under current conditions, ground water in the northern area of contamination near the former Stabilus facility moves to the northwest and discharges mostly to West Branch Neshaminy Creek; in the southern area of contamination near BAE Systems facility, ground water probably moves west and discharges to a tributary of West Branch Neshaminy Creek near well NP–21. Model simulations indicate that if NP–21 or NP–87 are pumped at 400 gallons per minute, groundwater recharge is likely captured from the southern area of contamination, but ground-water recharge from the northern area of contamination is less likely to be captured by the pumping. Simulations also indicate that pumping of a new recovery well near BAE Systems facility at 8 gallons per minute and two new recovery wells near the former Stabilus facility at a total of about 30 gallons per minute probably would capture most of the ground-water recharge in the areas where contamination is greatest.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri034159","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Risser, D.W., and Bird, P.H., 2003, Aquifer tests and simulation of ground-water flow in Triassic sedimentary rocks near Colmar, Bucks and Montgomery Counties, Pennsylvania: U.S. Geological Survey Water-Resources Investigations Report 2003-4159, viii, 73 p., https://doi.org/10.3133/wri034159.","productDescription":"viii, 73 p.","onlineOnly":"Y","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":124877,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/wri/2003/4159/coverthb.jpg"},{"id":87114,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wri/2003/4159/wri20034159.pdf","text":"Report","size":"8.84 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director, Pennsylvania Water Science Center U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The Ogallala and Arikaree aquifers are important water resources in the Rosebud Indian Reservation area and are used extensively for irrigation, municipal, and domestic water supplies. Continued or increased withdrawals from the Ogallala and Arikaree aquifers in the Rosebud Indian Reservation area have the potential to affect water levels in these aquifers. This report describes a conceptual model of ground-water flow in these aquifers and documents the development and calibration of a numerical model to simulate ground-water flow. Data for a twenty-year period (water years 1979 through 1998) were analyzed for the conceptual model and included in steady-state and transient numerical simulations of ground-water flow for the same 20-year period.
A three-dimensional ground-water flow model, with two layers, was used to simulate ground-water flow in the Ogallala and Arikaree aquifers. The upper layer represented the Ogallala aquifer, and the lower layer represented the Arikaree aquifer. The study area was divided into grid blocks 1,640 feet (500 meters) on a side, with 153 rows and 180 columns.
Areal recharge to the Ogallala and Arikaree aquifers occurs from precipitation on the outcrop areas. The recharge rate for the steady-state simulation was 3.3 inches per year for the Ogallala aquifer and 1.7 inches per year for the Arikaree aquifer for a total recharge rate of 266 cubic feet per second.
Discharge from the Ogallala and Arikaree aquifers occurs through evapotranspiration, discharge to streams, and well withdrawals. Discharge rates in cubic feet per second for the steady-state simulation were 184 for evapotranspiration, 46.8 and 19.7 for base flow to the Little White and Keya Paha Rivers, respectively, and 11.6 for well withdrawals from irrigation use. Estimated horizontal hydraulic conductivity used for the numerical model ranged from 0.2 to 120 feet per day in the Ogallala aquifer and 0.1 to 5.4 feet per day in the Arikaree aquifer. A uniform vertical hydraulic conductivity value of 6.6x10-4 feet per day was applied to the Ogallala aquifer. Vertical hydraulic conductivity was estimated for five zones in the Arikaree aquifer and ranged from 8.6x10-6 to 7.2x10-1 feet per day. Average rates of recharge, maximum evapotranspiration, and well withdrawals were included in the steady-state simulation, whereas the time-varying rates were included in the transient simulation.
Model calibration was accomplished by varying parameters within plausible ranges to produce the best fit between simulated and observed hydraulic heads and base-flow discharges from the Ogallala and Arikaree aquifers. For the steady-state simulation, the root mean square error for simulated hydraulic heads for all wells was 26.8 feet. Simulated hydraulic heads were within ±50 feet of observed values for 95 percent of the wells. For the transient simulation, the difference between the simulated and observed means for hydrographs was within ±40 feet for all observation wells. The potentiometric surfaces of the two aquifers calculated by the steady-state simulation established initial conditions for the transient simulation.
A sensitivity analysis was used to examine the response of the calibrated steady-state model to changes in model parameters including horizontal and vertical hydraulic conductivity, evapotranspiration, recharge, and riverbed conductance. The model was most sensitive to recharge and horizontal hydraulic conductivity.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri034043","usgsCitation":"Long, A.J., Putnam, L.D., and Carter, J.M., 2003, Simulated ground-water flow in the Ogallala and Arikaree aquifers, Rosebud Indian Reservation area, South Dakota: U.S. Geological Survey Water-Resources Investigations Report 2003-4043, vi, 69 p., https://doi.org/10.3133/wri034043.","productDescription":"vi, 69 p.","costCenters":[],"links":[{"id":178313,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":407732,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_55304.htm","linkFileType":{"id":5,"text":"html"}},{"id":4637,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034043/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"South Dakota","otherGeospatial":"Ogallala and Arikaree aquifers, Rosebud Indian Reservation area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.2833,\n 43.5\n ],\n [\n -100.25,\n 43.5\n ],\n [\n -100.25,\n 42.9569\n ],\n [\n -101.2833,\n 42.9569\n ],\n [\n -101.2833,\n 43.5\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b23e4b07f02db6ae0db","contributors":{"authors":[{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":242508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Putnam, Larry D. ldputnam@usgs.gov","contributorId":990,"corporation":false,"usgs":true,"family":"Putnam","given":"Larry","email":"ldputnam@usgs.gov","middleInitial":"D.","affiliations":[],"preferred":true,"id":242509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Janet M. 0000-0002-6376-3473 jmcarter@usgs.gov","orcid":"https://orcid.org/0000-0002-6376-3473","contributorId":339,"corporation":false,"usgs":true,"family":"Carter","given":"Janet","email":"jmcarter@usgs.gov","middleInitial":"M.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":242507,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":50760,"text":"wri024267 - 2003 - Analytical and numerical simulation of the steady-state hydrologic effects of mining aggregate in hypothetical sand-and-gravel and fractured crystalline-rock aquifers","interactions":[],"lastModifiedDate":"2012-02-02T00:11:20","indexId":"wri024267","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2002-4267","title":"Analytical and numerical simulation of the steady-state hydrologic effects of mining aggregate in hypothetical sand-and-gravel and fractured crystalline-rock aquifers","docAbstract":"Analytical solutions and numerical models were used to predict the extent of steady-state drawdown caused by mining of aggregate below the water table in hypothetical sand-and-gravel and fractured crystalline-rock aquifers representative of hydrogeologic settings in the Front Range area of Colorado. Analytical solutions were used to predict the extent of drawdown under a wide range of hydrologic and mining conditions that assume aquifer homogeneity, isotropy, and infinite extent. Numerical ground-water flow models were used to estimate the extent of drawdown under conditions that consider heterogeneity, anisotropy, and hydrologic boundaries and to simulate complex or unusual conditions not readily simulated using analytical solutions. Analytical simulations indicated that the drawdown radius (or distance) of influence increased as horizontal hydraulic conductivity of the aquifer, mine penetration of the water table, and mine radius increased; radius of influence decreased as aquifer recharge increased. Sensitivity analysis of analytical simulations under intermediate conditions in sand-and-gravel and fractured crystalline-rock aquifers indicated that the drawdown radius of influence was most sensitive to mine penetration of the water table and least sensitive to mine radius. Radius of influence was equally sensitive to changes in horizontal hydraulic conductivity and recharge. Numerical simulations of pits in sand-and- gravel aquifers indicated that the area of influence in a vertically anisotropic sand-and-gravel aquifer of medium size was nearly identical to that in an isotropic aquifer of the same size. Simulated area of influence increased as aquifer size increased and aquifer boundaries were farther away from the pit, and simulated drawdown was greater near the pit when aquifer boundaries were close to the pit. Pits simulated as lined with slurry walls caused mounding to occur upgradient from the pits and drawdown to occur downgradient from the pits. Pits simulated as refilled with water and undergoing evaporative losses had little hydro- logic effect on the aquifer. Numerical sensitivity analyses for simulations of pits in sand-and-gravel aquifers indicated that simulated head was most sensitive to horizontal hydraulic conductivity and the hydraulic conductance of general-head boundaries in the models. Simulated head was less sensitive to riverbed conductance and recharge and relatively insensitive to vertical hydraulic conductivity. Numerical simulations of quarries in fractured crystalline-rock aquifers indicated that the area of influence in a horizontally anisotropic aquifer was elongated in the direction of higher horizontal hydraulic conductivity and shortened in the direction of lower horizontal hydraulic conductivity compared to area of influence in a homogeneous, isotropic aquifer. Area of influence was larger in an aquifer with ground-water flow in deep, low-permeability fractures than in a homogeneous, isotropic aquifer. Area of influence was larger for a quarry intersected by a hydraulically conductive fault zone and smaller for a quarry intersected by a low-conductivity fault zone. Numerical sensitivity analyses for simulations of quarries in fractured crystalline-rock aquifers indicated simulated head was most sensitive to variations in recharge and horizontal hydraulic conductivity, had little sensitivity to vertical hydraulic conductivity and drain cells used to simulate valleys, and was relatively insensitive to drain cells used to simulate the quarry.","language":"ENGLISH","doi":"10.3133/wri024267","usgsCitation":"Arnold, L.R., Langer, W.H., and Paschke, S.S., 2003, Analytical and numerical simulation of the steady-state hydrologic effects of mining aggregate in hypothetical sand-and-gravel and fractured crystalline-rock aquifers: U.S. Geological Survey Water-Resources Investigations Report 2002-4267, vi, 56 p. : ill. (some col.), maps (some col.) ; 28 cm., https://doi.org/10.3133/wri024267.","productDescription":"vi, 56 p. : ill. (some col.), maps (some col.) ; 28 cm.","costCenters":[],"links":[{"id":126704,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/wri/2002/4267/report-thumb.jpg"},{"id":86342,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wri/2002/4267/report.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4acce4b07f02db67e561","contributors":{"authors":[{"text":"Arnold, L. R.","contributorId":92738,"corporation":false,"usgs":true,"family":"Arnold","given":"L.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":242252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langer, William H. blanger@usgs.gov","contributorId":1241,"corporation":false,"usgs":true,"family":"Langer","given":"William","email":"blanger@usgs.gov","middleInitial":"H.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":false,"id":242250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paschke, Suzanne Smith","contributorId":76812,"corporation":false,"usgs":true,"family":"Paschke","given":"Suzanne","email":"","middleInitial":"Smith","affiliations":[],"preferred":false,"id":242251,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":48847,"text":"wri034056 - 2003 - Comparison and continuous estimates of fecal coliform and Escherichia coli bacteria in selected Kansas streams, May 1999 through April 2002","interactions":[],"lastModifiedDate":"2012-02-02T00:10:05","indexId":"wri034056","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4056","title":"Comparison and continuous estimates of fecal coliform and Escherichia coli bacteria in selected Kansas streams, May 1999 through April 2002","docAbstract":"The sanitary quality of water and its use as a public-water supply and for recreational activities, such as swimming, wading, boating, and fishing, can be evaluated on the basis of fecal \r\ncoliform and Escherichia coli (E. coli) bacteria densities. This report describes the overall sanitary quality of surface water in selected Kansas streams, the relation between fecal coliform and \r\nE. coli, the relation between turbidity and bacteria densities, and how continuous bacteria estimates can be used to evaluate the water-quality conditions in selected Kansas streams.\r\n\r\nSamples for fecal coliform and E. coli were collected at 28 surface-water sites in Kansas. Of the 318 samples collected, 18 percent exceeded the current Kansas Department of Health and \r\nEnvironment (KDHE) secondary contact recreational, single-sample criterion for fecal coliform (2,000 colonies per 100 milliliters of water). Of the 219 samples collected during the recreation months (April 1 through October 31), 21 percent exceeded the current (2003) KDHE single-sample fecal coliform criterion for secondary contact rec-reation (2,000 colonies per 100 milliliters of water) and 36 percent exceeded the U.S. Environmental Protection Agency (USEPA) recommended single-sample primary contact recreational criterion for E. coli (576 colonies per 100 milliliters of water). Comparisons of fecal coliform and E. coli criteria indicated that more than one-half of the streams sampled could exceed USEPA recommended E. coli criteria more frequently than the current KDHE fecal coliform criteria. In addition, the ratios of E. coli to fecal coliform (EC/FC) were smallest for sites with slightly saline water (specific conductance greater than 1,000 microsiemens per centimeter at 25 degrees Celsius), indicating that E. coli may not be a good indicator of sanitary quality for those streams. Enterococci bacteria may provide a more accurate assessment of the potential for swimming-related illnesses in these streams. \r\n\r\nRatios of EC/FC and linear regression models were developed for estimating E. coli densities on the basis of measured fecal coliform densities for six individual and six groups of surface-water sites. Regression models developed for the six individual surface-water sites and six groups of sites explain at least 89 percent of the variability in E. coli densities. The EC/FC ratios and regression models are site specific and make it possible to convert historic fecal coliform bacteria data to estimated E. coli densities for the selected sites. The EC/FC ratios can be used to estimate E. coli for any range of historical fecal coliform densities, and in some cases with less error than the regression models. The basin- and statewide regression models explained at least 93 percent of the variance and best represent the sites where a majority of the data used to develop the models were collected (Kansas and Little Arkansas Basins).\r\n\r\nComparison of the current (2003) KDHE geometric-mean primary contact criterion for fecal coliform bacteria of 200 col/100 mL to the 2002 USEPA recommended geometric-mean criterion of 126 col/100 mL for E. coli results in an EC/FC ratio of 0.63. The geometric-mean EC/FC ratio for all sites except Rattlesnake Creek (site 21) is 0.77, indicating that considerably more than 63 percent of the fecal coliform is E. coli. This potentially could lead to more exceedances of the recommended E. coli criterion, where the water now meets the current (2003) 200-col/100 mL fecal coliform criterion. \r\n\r\nIn this report, turbidity was found to be a reliable estimator of bacteria densities. Regression models are provided for estimating fecal coliform and E. coli bacteria densities using continuous \r\nturbidity measurements. Prediction intervals also are provided to show the uncertainty associated with using the regression models. Eighty percent of all measured sample densities and individual turbidity-based estimates from the regression models were in agreement as exceedi","language":"ENGLISH","doi":"10.3133/wri034056","usgsCitation":"Rasmussen, P.P., and Ziegler, A., 2003, Comparison and continuous estimates of fecal coliform and Escherichia coli bacteria in selected Kansas streams, May 1999 through April 2002: U.S. Geological Survey Water-Resources Investigations Report 2003-4056, 87 p., https://doi.org/10.3133/wri034056.","productDescription":"87 p.","costCenters":[],"links":[{"id":4067,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034056/","linkFileType":{"id":5,"text":"html"}},{"id":161562,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":13760,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://ks.water.usgs.gov/pubs/abstracts/wrir.abstract.03-4056.html","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b27e4b07f02db6b0db3","contributors":{"authors":[{"text":"Rasmussen, Patrick P. 0000-0002-3287-6010 pras@usgs.gov","orcid":"https://orcid.org/0000-0002-3287-6010","contributorId":3530,"corporation":false,"usgs":true,"family":"Rasmussen","given":"Patrick","email":"pras@usgs.gov","middleInitial":"P.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":238415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ziegler, Andrew C. aziegler@usgs.gov","contributorId":433,"corporation":false,"usgs":true,"family":"Ziegler","given":"Andrew C.","email":"aziegler@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":238414,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":54006,"text":"b2172F - 2003 - Graphic comparison of reserve-growth models for conventional oil and accumulation","interactions":[],"lastModifiedDate":"2012-02-02T00:11:40","indexId":"b2172F","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":306,"text":"Bulletin","code":"B","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2172","chapter":"F","title":"Graphic comparison of reserve-growth models for conventional oil and accumulation","docAbstract":"The U.S. Geological Survey (USGS) periodically assesses crude oil, natural gas, and natural gas liquids resources of the world. The assessment procedure requires estimated recover-able oil and natural gas volumes (field size, cumulative production\r\nplus remaining reserves) in discovered fields. Because initial reserves are typically conservative, subsequent estimates increase through time as these fields are developed and produced.\r\nThe USGS assessment of petroleum resources makes estimates, or forecasts, of the potential additions to reserves in discovered oil and gas fields resulting from field development, and it also estimates the potential fully developed sizes of undiscovered\r\nfields. The term ?reserve growth? refers to the commonly\r\nobserved upward adjustment of reserve estimates. Because such additions are related to increases in the total size of a field, the USGS uses field sizes to model reserve growth.\r\nFuture reserve growth in existing fields is a major component\r\nof remaining U.S. oil and natural gas resources and has therefore become a necessary element of U.S. petroleum resource assessments. Past and currently proposed reserve-growth models compared herein aid in the selection of a suitable set of forecast functions to provide an estimate of potential additions\r\nto reserves from reserve growth in the ongoing National Oil and Gas Assessment Project (NOGA). Reserve growth is modeled by construction of a curve that represents annual fractional\r\nchanges of recoverable oil and natural gas volumes (for fields and reservoirs), which provides growth factors. Growth factors are used to calculate forecast functions, which are sets of field- or reservoir-size multipliers.\r\nComparisons of forecast functions were made based on datasets used to construct the models, field type, modeling method, and length of forecast span. Comparisons were also made between forecast functions based on field-level and reservoir-\r\nlevel growth, and between forecast functions based on older and newer data.\r\nThe reserve-growth model used in the 1995 USGS National Assessment and the model currently used in the NOGA project provide forecast functions that yield similar estimates of potential\r\nadditions to reserves. Both models are based on the Oil and Gas Integrated Field File from the Energy Information Administration\r\n(EIA), but different vintages of data (from 1977 through 1991 and 1977 through 1996, respectively). The model based on newer data can be used in place of the previous model, providing\r\nsimilar estimates of potential additions to reserves. Fore-cast functions for oil fields vary little from those for gas fields in these models; therefore, a single function may be used for both oil and gas fields, like that used in the USGS World Petroleum Assessment 2000.\r\nForecast functions based on the field-level reserve growth model derived from the NRG Associates databases (from 1982 through 1998) differ from those derived from EIA databases (from 1977 through 1996). However, the difference may not be enough to preclude the use of the forecast functions derived from NRG data in place of the forecast functions derived from EIA data. Should the model derived from NRG data be used, separate forecast functions for oil fields and gas fields must be employed. The forecast function for oil fields from the model derived from NRG data varies significantly from that for gas fields, and a single function for both oil and gas fields may not be appropriate.","language":"ENGLISH","doi":"10.3133/b2172F","usgsCitation":"Klett, T., 2003, Graphic comparison of reserve-growth models for conventional oil and accumulation (Version 1.0): U.S. Geological Survey Bulletin 2172, 69 p., https://doi.org/10.3133/b2172F.","productDescription":"69 p.","costCenters":[],"links":[{"id":178209,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":4829,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/bul/b2172-f/","linkFileType":{"id":5,"text":"html"}}],"edition":"Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b12e4b07f02db6a2b28","contributors":{"authors":[{"text":"Klett, T. R. 0000-0001-9779-1168","orcid":"https://orcid.org/0000-0001-9779-1168","contributorId":83067,"corporation":false,"usgs":true,"family":"Klett","given":"T. R.","affiliations":[],"preferred":false,"id":248888,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":51537,"text":"ofr03200 - 2003 - Geophysical identification and geological Implications of the southern Alaska magnetic trough","interactions":[],"lastModifiedDate":"2025-08-19T21:07:22.635149","indexId":"ofr03200","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","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":"2003-200","title":"Geophysical identification and geological Implications of the southern Alaska magnetic trough","docAbstract":"The southern Alaska magnetic trough (SAMT) is one of the fundamental, crustal-scale, magnetic features of Alaska. It is readily recognized on 10 km upward-continued aeromagnetic maps of the state. The arcuate SAMT ranges from 30 to 100 km wide and extends in two separate segments along the southern Alaska margin for about 1200 km onshore (from near the Alaska/Canada border at about 60 degrees north latitude to the Bering Sea) and may continue an additional 500 km or more offshore (in the southern Bering Sea). The SAMT is bordered to the south by the southern Alaska magnetic high (SAMH) produced by strongly magnetic crust and to the north by a magnetically quiet zone that reflects weakly magnetic interior Alaska crust. Geophysically, the SAMT is more than just the north-side dipole low associated with the SAMH. Several modes of analysis, including examination of magnetic potential (pseudogravity) and profile modeling, indicate that the source of this magnetic trough is a discrete, crustal-scale body. Geologically, the western portion of the SAMT coincides to a large degree with collapsed Mesozoic Kahiltna flysch basin. This poster presents our geophysical evidence for the extent and geometry of this magnetic feature as well as initial geological synthesis and combined geologic/geophysical modeling to examine the implications of this feature for the broad scale tectonic framework of southern Alaska.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr03200","usgsCitation":"Saltus, R.W., Hudson, T.L., and Wilson, F.H., 2003, Geophysical identification and geological Implications of the southern Alaska magnetic trough (Version 1.0): U.S. Geological Survey Open-File Report 2003-200, Poster, 72 by 36 inches, https://doi.org/10.3133/ofr03200.","productDescription":"Poster, 72 by 36 inches","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":178022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":4554,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2003/ofr-03-200","linkFileType":{"id":5,"text":"html"}}],"edition":"Version 1.0","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac9e4b07f02db67c4bc","contributors":{"authors":[{"text":"Saltus, R. W.","contributorId":85588,"corporation":false,"usgs":true,"family":"Saltus","given":"R.","middleInitial":"W.","affiliations":[],"preferred":false,"id":243881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudson, T. L.","contributorId":13992,"corporation":false,"usgs":true,"family":"Hudson","given":"T.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":243879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Frederic H. 0000-0003-1761-6437 fwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1761-6437","contributorId":67174,"corporation":false,"usgs":true,"family":"Wilson","given":"Frederic","email":"fwilson@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":243880,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":51417,"text":"ofr03202 - 2003 - The Sidebar Computer Program, a seismic-shaking intensity meter: users' manual and software description","interactions":[],"lastModifiedDate":"2014-04-03T16:01:39","indexId":"ofr03202","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","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":"2003-202","title":"The Sidebar Computer Program, a seismic-shaking intensity meter: users' manual and software description","docAbstract":"The SideBar computer program provides a visual display of seismic shaking intensity as recorded at one specific seismograph. This software allows a user to tap into the seismic data recorded on that specific seismograph and to display the overall level of shaking at the single location where that seismograph resides (usually the same place the user is). From this shaking level, SideBar also estimates the potential for damage nearby. SideBar cannot tell you the “Richter magnitude” of the earthquake (see box), only how hard the ground shook locally and this estimate of how much damage is likely in the neighborhood. This combination of local effects is called the “seismic intensity”.
\nSideBar runs on a standard desktop or laptop PC, and is intended for the media, schools, emergency responders, and any other group hosting a seismograph and who want to know immediately after an earthquake the levels of shaking measured by that instrument. These local values can be used to inform the public and help initiate appropriate local emergency response activities in the minutes between the earthquake and availability of the broader coverage provided by the USGS over the Web, notably by ShakeMap. For example, for instruments installed in schools, the level of shaking and likely damage at the school could immediately be Web broadcast and parents could quickly determine the likely safety of their children—their biggest postearthquake concern. Also, in the event of a Web outage, SideBar may be a continuing primary source of local emergency response information for some additional minutes.
\nSpecifically, SideBar interprets the peak level of acceleration (that is, the force of shaking, as a percentage of the force of gravity) as well as the peak velocity, or highest speed, at which the ground moves. Using these two basic measurements, SideBar computes what is called Instrumental Intensity—a close approximation of the Modified Mercalli Intensity scale, or “MMI” (using the Wald et al., 1999a, relationships between acceleration, velocity, and shaking intensity). Intensity is a measure of local shaking strength and the potential for damage—of how bad the earthquake effects were locally. The intensity level is what SideBar displays most prominently on the PC monitor. Intensity is shown as a large, colored bar that gets taller and changes color up a rainbow from blues toward reds as the shaking level increases. As opposed to earthquake magnitudes, which are reported as decimal values (like “7.6”), intensity is traditionally given as a Roman numeral, with “I” to “X+” assigned to levels of potential damage and perceived shaking strength. For good measure, SideBar shows the actual values of the force of shaking (peak ground acceleration as a percentage of gravity) and the speed of ground motion (peak ground velocity in inches per second, by default, or in centimeters per second, if you wish), both these values as decimal numbers.
\nSideBar also remembers the most recent earthquakes (for up to one week), and can store as many of these previous earthquakes as the user allows (and as the user’s PC has room for)—typically thousands. SideBar also remembers forever the three largest earthquakes it has seen and all earthquakes over intensity IV so that one never loses particularly important events.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr03202","usgsCitation":"Evans, J.R., 2003, The Sidebar Computer Program, a seismic-shaking intensity meter: users' manual and software description (Release 3.0.TO): U.S. Geological Survey Open-File Report 2003-202, Report: 22 p.; SideBar 3.0TO, https://doi.org/10.3133/ofr03202.","productDescription":"Report: 22 p.; SideBar 3.0TO","numberOfPages":"22","additionalOnlineFiles":"Y","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":178352,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr03202.jpg"},{"id":4432,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2003/0202/","linkFileType":{"id":5,"text":"html"}},{"id":285661,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2003/0202/pdf/SideBarManual.pdf"},{"id":285662,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2003/0202/SideBar.zip"}],"edition":"Release 3.0.TO","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ac6e4b07f02db67a939","contributors":{"authors":[{"text":"Evans, John R. jrevans@usgs.gov","contributorId":529,"corporation":false,"usgs":true,"family":"Evans","given":"John","email":"jrevans@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":243518,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":50855,"text":"wri034040 - 2003 - Simulated effects of ground-water management scenarios on the Santa Fe group aquifer system, Middle Rio Grande Basin, New Mexico, 2001-40","interactions":[],"lastModifiedDate":"2012-02-02T00:11:30","indexId":"wri034040","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4040","title":"Simulated effects of ground-water management scenarios on the Santa Fe group aquifer system, Middle Rio Grande Basin, New Mexico, 2001-40","docAbstract":"Future conditions in the Santa Fe Group aquifer system through 2040 were simulated using the most recent revision of the U.S. Geological Survey groundwater- flow model for the Middle Rio Grande Basin. Three simulations were performed to investigate the likely effects of different scenarios of future groundwater pumping by the City of Albuquerque on the ground-water system. For simulation I, pumping was held constant at known year-2000 rates. For simulation II, pumping was increased to simulate the use of pumping to meet all projected city water demand through 2040. For simulation III, pumpingwas reduced in accordance with a plan by the City of Albuquerque to use surfacewater to meet most of the projectedwater demand. The simulations indicate that for each of the three pumping scenarios, substantial additional watertable declines would occur in some areas of the basin through 2040. However, the reduced pumping scenario of simulation III also results in water-table rise over a broad area of the city. All three scenarios indicate that the contributions of aquifer storage and river leakage to the ground-water system would change between 2000 and 2040. \r\n\r\n \r\n\r\nComparisons among the results for simulations I, II, and III indicate that the various pumping scenarios have substantially different effects on water-level declines in the Albuquerque area and on the contribution of each water-budget component to the total budget for the ground-water system. Between 2000 and 2040, water-level declines for continued pumping at year-2000 rates are as much as 120 feet greater than for reduced pumping; water-level declines for increased pumping to meet all projected city demand are as much as 160 feet greater. Over the same time period, reduced pumping results in retention in aquifer storage of about 1,536,000 acre-feet of ground water as compared with continued pumping at year- 2000 rates and of about 2,257,000 acre-feet as compared with increased pumping. The quantity of water retained in the Rio Grande as a result of reduced pumping and the associated decrease in induced recharge from the river is about 731,000 acre-feet as compared with continued pumping at year-2000 rates and about 872,000 acre-feet as compared with increased pumping. Reduced pumping results in slight increases in the quantity of water lost from the groundwater system to evapotranspiration and agriculturaldrain flow compared with the other pumping scenarios.","language":"ENGLISH","doi":"10.3133/wri034040","usgsCitation":"Bexfield, L.M., and McAda, D.P., 2003, Simulated effects of ground-water management scenarios on the Santa Fe group aquifer system, Middle Rio Grande Basin, New Mexico, 2001-40: U.S. Geological Survey Water-Resources Investigations Report 2003-4040, iv, 39 p. : col. ill., col maps ; 28 cm., https://doi.org/10.3133/wri034040.","productDescription":"iv, 39 p. : col. ill., col maps ; 28 cm.","costCenters":[],"links":[{"id":4624,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034040/","linkFileType":{"id":5,"text":"html"}},{"id":179651,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49b5e4b07f02db5cb252","contributors":{"authors":[{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":242460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAda, Douglas P. dpmcada@usgs.gov","contributorId":2763,"corporation":false,"usgs":true,"family":"McAda","given":"Douglas","email":"dpmcada@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":242461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":51445,"text":"ofr2003130 - 2003 - User's Guide for the Agricultural Non-Point Source (AGNPS) Pollution Model Data Generator","interactions":[],"lastModifiedDate":"2012-02-02T00:11:30","indexId":"ofr2003130","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","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":"2003-130","title":"User's Guide for the Agricultural Non-Point Source (AGNPS) Pollution Model Data Generator","docAbstract":"BACKGROUND\r\n\r\nThroughout this user guide, we refer to datasets that we used in conjunction with developing of this software for supporting cartographic research and producing the datasets to conduct research. However, this software can be used with these datasets or with more 'generic' versions of data of the appropriate type. For example, throughout the guide, we refer to national land cover data (NLCD) and digital elevation model (DEM) data from the U.S. Geological Survey (USGS) at a 30-m resolution, but any digital terrain model or land cover data at any appropriate resolution will produce results. Another key point to keep in mind is to use a consistent data resolution for all the datasets per model run.\r\n\r\nThe U.S. Department of Agriculture (USDA) developed the Agricultural Nonpoint Source (AGNPS) pollution model of watershed hydrology in response to the complex problem of managing nonpoint sources of pollution. AGNPS simulates the behavior of runoff, sediment, and nutrient transport from watersheds that have agriculture as their prime use. The model operates on a cell basis and is a distributed parameter, event-based model. The model requires 22 input parameters. Output parameters are grouped primarily by hydrology, sediment, and chemical output (Young and others, 1995.)\r\n\r\nElevation, land cover, and soil are the base data from which to extract the 22 input parameters required by the AGNPS. For automatic parameter extraction, follow the general process described in this guide of extraction from the geospatial data through the AGNPS Data Generator to generate input parameters required by the pollution model (Finn and others, 2002.)","language":"ENGLISH","publisher":"Geological Survey (U.S.)","doi":"10.3133/ofr2003130","usgsCitation":"Finn, M.P., Scheidt, D.J., and Jaromack, G.M., 2003, User's Guide for the Agricultural Non-Point Source (AGNPS) Pollution Model Data Generator: U.S. Geological Survey Open-File Report 2003-130, 21 p., https://doi.org/10.3133/ofr2003130.","productDescription":"21 p.","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":179156,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":10632,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://carto-research.er.usgs.gov/watershed/pdf/ADGen_uGuide.pdf","size":"1183","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a17e4b07f02db604039","contributors":{"authors":[{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":243594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheidt, Douglas J.","contributorId":20014,"corporation":false,"usgs":true,"family":"Scheidt","given":"Douglas","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":243595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaromack, Gregory M.","contributorId":53463,"corporation":false,"usgs":true,"family":"Jaromack","given":"Gregory","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":243596,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":50861,"text":"wri034084 - 2003 - Percentile distributions of median nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorus concentrations in Oklahoma streams, 1973-2001","interactions":[],"lastModifiedDate":"2023-01-05T21:32:49.300537","indexId":"wri034084","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4084","title":"Percentile distributions of median nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorus concentrations in Oklahoma streams, 1973-2001","docAbstract":"Nutrients are one of the primary causes of water-quality impairments in streams, lakes, reservoirs, and estuaries in the United States. The U.S. Environmental Protection Agency has developed regional-based nutrient criteria using ecoregions to protect streams in the United States from impairment. However, nutrient criteria were based on nutrient concentrations measured in large aggregated nutrient ecoregions with little relevance to local environmental conditions in states. The Oklahoma Water Resources Board is using a dichotomous process known as Use Support Assessment Protocols to define nutrient criteria in Oklahoma streams. The Oklahoma Water Resources Board is modifying the Use Support Assessment Protocols to reflect nutrient informa-tion and environmental characteristics relevant to Oklahoma streams, while considering nutrient information grouped by geographic regions based on level III ecoregions and state boundaries.\r\n\r\nPercentile distributions of median nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorous concentrations were calculated from 563 sites in Oklahoma and 4 sites in Arkansas near the Oklahoma and Arkansas border to facilitate development of nutrient criteria for Oklahoma streams. Sites were grouped into four geographic regions and were categorized into eight stream categories by stream slope and stream order. The 50th percentiles of median nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorus concentrations were greater in the Ozark Highland ecoregion and were less in the Ouachita Mountains ecoregion when compared to other geographic areas used to group sites. The 50th percentiles of median concentrations of nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorus were least in first, second, and third order streams. The 50th percentiles of median nitrite plus nitrate as nitrogen, total nitrogen and total phosphorus concentrations in the Ozark Highland and Ouachita Mountains ecoregions were least in first, second, and third order streams with streams slopes greater than 17 feet per mile.\r\n\r\nNitrite plus nitrate as nitrogen and total nitrogen criteria determined by the U.S. Environmental Protection Agency for the Ozark Highland ecoregion were less than the 25th percentiles of median nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorus concentrations in the Ozark Highland ecoregion calculated for this report. Nitrite plus nitrate as nitrogen and total nitrogen criteria developed by the U.S. Environmental Protection Agency for the Ouachita Mountains ecoregion were similar to the 25th percentiles of median nitrite plus nitrate as nitrogen and total nitrogen concentrations in the Ouachita Mountains ecoregion calculated for this report. Nitrate as nitrogen and total phosphorus concentrations currently (2002) used in the Use Support Assessment Protocols for Oklahoma were greater than the 75th percentiles of median nitrite plus nitrate as nitrogen and total phosphorus concentrations calculated for this report.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri034084","usgsCitation":"Haggard, B.E., Masoner, J.R., and Becker, C., 2003, Percentile distributions of median nitrite plus nitrate as nitrogen, total nitrogen, and total phosphorus concentrations in Oklahoma streams, 1973-2001: U.S. Geological Survey Water-Resources Investigations Report 2003-4084, iv, 24 p., https://doi.org/10.3133/wri034084.","productDescription":"iv, 24 p.","costCenters":[],"links":[{"id":178582,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":411453,"rank":3,"type":{"id":36,"text":"NGMDB Index 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cjbecker@usgs.gov","orcid":"https://orcid.org/0000-0001-6652-4542","contributorId":2489,"corporation":false,"usgs":true,"family":"Becker","given":"Carol","email":"cjbecker@usgs.gov","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":242475,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":52913,"text":"wri024298 - 2003 - Development of regression equations to estimate flow durations and low-flow-frequency statistics in New Hampshire streams","interactions":[],"lastModifiedDate":"2012-02-02T00:11:45","indexId":"wri024298","displayToPublicDate":"2003-07-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2002-4298","title":"Development of regression equations to estimate flow durations and low-flow-frequency statistics in New Hampshire streams","docAbstract":"Regression equations and basin-characteristic digital datasets were developed to help water-resource managers estimate surface-water resources during periods of low flow in New Hampshire. The regression equations were developed to estimate statistics for the seasonal and annual low-flow-frequency and seasonal period-of-record and period-of-record flow durations. Because streamflow is maintained by ground-water discharge during periods of low flow, these equations also will aid in the assessment of ground-water availability. Ultimately, the equations and datasets developed herein can be combined with data on water withdrawals, discharges, and interbasin transfers in a geographic information system (GIS) to allow assessments of water use and water availability in any drainage basin in the State of New Hampshire. \r\n\r\nRegression equations developed in this study provide estimates of the seasonal (spring, summer, fall, and winter) and annual 7-day 2-year (7Q2) and 7-day 10-year (7Q10) low-flow-frequency values, as well as seasonal period-of-record and period-of-record flow durations (60-, 70-, 80-, 90-, 95-, and 98-percent exceedences) for ungaged reaches of unregulated New Hampshire streams. Regression equations were developed using seasonal and annual low-flow statistics from 58 to 60 continuous-record stream-gaging stations in New Hampshire and nearby areas in neighboring states, and measurements of various characteristics of the drainage basins that contribute flow to those stations. \r\n\r\nThe estimating equations for the seasonal and annual 7Q2 and 7Q10 values were developed using generalized-least-squares (GLS) regression analyses. The GLS equations developed for these flow statistics gave average prediction errors that ranged from 11 to 61 percent.\r\n\r\nThe estimating equations for flow-duration exceedence frequency values were developed using ordinary-least-squares (OLS) regression analysis. The OLS equations developed for these flow statistics gave average prediction errors ranging from 14 to 79 percent.\r\n\r\nA total of 93 measurable drainage-basin characteristics were selected as possible predictor variables. Of these 93 variables, the following 10 were determined to be statistically significant predictors for at least one of the dependent variables: drainage area, average basin slope, maximum basin elevation, average summer gage precipitation for 1961-90, average spring gage precipitation for 1961-90, average mean annual basin temperature for 1961-90, average mean summer basin temperature for 1961-90, average winter basin-centroid precipitation for 1961-90, percent of the basin that is coniferous, and percent of the basin that is mixed coniferous and deciduous. These 10 basin characteristics were selected because they were statistically significant based on several statistical parameters that evaluated which combination of characteristics contributed the most to the predictive accuracy of the regression-equation models. A GIS is required to measure the values of the predictor variables for the equations developed in this study.","language":"ENGLISH","doi":"10.3133/wri024298","usgsCitation":"Flynn, R.H., 2003, Development of regression equations to estimate flow durations and low-flow-frequency statistics in New Hampshire streams: U.S. Geological Survey Water-Resources Investigations Report 2002-4298, viii, 66 p. : col. maps ; 28 cm., https://doi.org/10.3133/wri024298.","productDescription":"viii, 66 p. : col. maps ; 28 cm.","costCenters":[],"links":[{"id":5003,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri024298/","linkFileType":{"id":5,"text":"html"}},{"id":124416,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/wri_2002_4298.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a9be4b07f02db65de2c","contributors":{"authors":[{"text":"Flynn, Robert H. rflynn@usgs.gov","contributorId":2137,"corporation":false,"usgs":true,"family":"Flynn","given":"Robert","email":"rflynn@usgs.gov","middleInitial":"H.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":246216,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70210763,"text":"70210763 - 2003 - Pleistocene glaciations of the Rocky Mountains","interactions":[],"lastModifiedDate":"2020-06-23T18:26:10.235233","indexId":"70210763","displayToPublicDate":"2003-06-23T13:15:30","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5919,"text":"Developments in Quaternary Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Pleistocene glaciations of the Rocky Mountains","docAbstract":"This chapter presents the status of Rocky Mountain glacial studies in 1965 and progress from that time to the present. The Rocky Mountains and the adjacent Basin and Range of the United States consist of about 100 ranges distributed in a northwest trending belt 2,000 km long and 200–800 km wide. In 1965, Rocky Mountain glacial subdivisions and correlations are closely linked with those of the mid-continent. Also, erratic boulders and diamictons well beyond or above moraines of Pinedale and Bull Lake age are noted at many sites in the Rocky Mountains and are attributed to an older glaciation, vastly more extensive than the Bull Lake or Pinedale. Global climate models suggest that glacial-anticyclonic circulation weaken westerly flow and results in air that is cooler and drier than present, particularly for the northern Rocky Mountains. More precisely dated, glacial and lacustrine records may reveal patterns in such nonparallelism from south to north (colder) or east to west (wetter) throughout the Rocky Mountains.
","language":"English","publisher":"Elsevier","doi":"10.1016/S1571-0866(03)01004-2","usgsCitation":"Pierce, K.L., 2003, Pleistocene glaciations of the Rocky Mountains: Developments in Quaternary Sciences, v. 1, p. 63-76, https://doi.org/10.1016/S1571-0866(03)01004-2.","productDescription":"14 p.","startPage":"63","endPage":"76","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":375824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho,Nevada New Mexico, Montana, Oregon, Utah, Washington, Wyoming","otherGeospatial":"Late Wisconsin glaciers in the Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.501953125,\n 46.98025235521883\n ],\n [\n -117.158203125,\n 47.989921667414194\n ],\n [\n -121.37695312499999,\n 47.27922900257082\n ],\n [\n -122.78320312499999,\n 47.21956811231547\n ],\n [\n -124.18945312500001,\n 47.57652571374621\n ],\n [\n -124.45312499999999,\n 43.51668853502906\n ],\n [\n -124.01367187499999,\n 41.44272637767212\n ],\n [\n -124.8046875,\n 40.245991504199026\n ],\n [\n -121.11328124999999,\n 34.74161249883172\n ],\n [\n -118.037109375,\n 33.797408767572485\n ],\n [\n -117.0703125,\n 32.54681317351514\n ],\n [\n -114.2578125,\n 32.62087018318113\n ],\n [\n -111.181640625,\n 31.653381399664\n ],\n [\n -102.83203125,\n 32.175612478499325\n ],\n [\n -103.271484375,\n 36.94989178681327\n ],\n [\n -101.865234375,\n 37.16031654673677\n ],\n [\n -102.216796875,\n 40.91351257612758\n ],\n [\n -104.150390625,\n 41.11246878918088\n ],\n [\n -104.0625,\n 46.73986059969267\n ],\n [\n -104.501953125,\n 46.98025235521883\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pierce, Kenneth L. kpierce@usgs.gov","contributorId":1609,"corporation":false,"usgs":true,"family":"Pierce","given":"Kenneth","email":"kpierce@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":791322,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70756,"text":"ofr03234 - 2003 - Raster dataset showing the probability of elevated concentrations of nitrate in ground water in Colorado, hydrogeomorphic regions and fertilizer use estimates included","interactions":[],"lastModifiedDate":"2013-08-28T10:40:18","indexId":"ofr03234","displayToPublicDate":"2003-06-23T00:00:00","publicationYear":"2003","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":"2003-234","title":"Raster dataset showing the probability of elevated concentrations of nitrate in ground water in Colorado, hydrogeomorphic regions and fertilizer use estimates included","docAbstract":"This dataset is one of eight datasets produced by this study.\nFour of the datasets predict the probability of detecting\natrazine and(or) desethyl-atrazine (a breakdown product of atrazine)\nin ground water in Colorado; the other four predict the probability\nof detecting elevated concentrations of nitrate in ground water in\nColorado. The four datasets that predict the probability of\natrazine and(or) desethyl-atrazine (atrazine/DEA) are differentiated\nby whether or not they incorporated atrazine use and whether\nor not they incorporated hydrogeomorphic regions. The four datasets\nthat predict the probability of elevated concentrations of nitrate\nare differentiated by whether or not they incorporated fertilizer\nuse and whether or not they incorporated hydrogeomorphic\nregions. Each of the eight datasets has its own unique strengths\nand weaknesses. The user is cautioned to read Rupert (2003, Probability\nof detecting atrazine/desethyl-atrazine and elevated concentrations\nof nitrate in ground water in Colorado: U.S. Geological Survey\nWater-Resources Investigations Report 02-4269, 35 p.,\nhttp://water.usgs.gov/pubs/wri/wri02-4269/) to determine if he(she)\nis using the most appropriate dataset for his(her) particular needs.\nThis dataset specifically predicts the probability of detecting\nelevated concentrations of nitrate in ground water in Colorado with\nhydrogeomorphic regions and fertilizer use included. The following\ntext was extracted from Rupert (2003).\n\nDraft Federal regulations may require that each State develop a\nState Pesticide Management Plan for the herbicides atrazine,\nalachlor, metolachlor, and simazine. Maps were developed that the\nState of Colorado could use to predict the probability of detecting\natrazine/DEA in ground water in Colorado. These maps can be\nincorporated into the State Pesticide Management Plan and can help\nprovide a sound hydrogeologic basis for atrazine management in\nColorado. Maps showing the probability of detecting elevated nitrite\nplus nitrate as nitrogen (nitrate) concentrations in ground water in\nColorado also were developed because nitrate is a contaminant of\nconcern in many areas of Colorado.\n\nMaps showing the probability of detecting atrazine/DEA at or greater\nthan concentrations of 0.1 microgram per liter and nitrate\nconcentrations in ground water greater than 5 milligrams per liter\nwere developed as follows: (1) Ground-water quality data were overlaid\nwith anthropogenic and hydrogeologic data by using a geographic\ninformation system (GIS) to produce a dataset in which each well had\ncorresponding data on atrazine use, fertilizer use, geology,\nhydrogeomorphic regions, land cover, precipitation, soils, and well\nconstruction. These data then were downloaded to a statistical\nsoftware package for analysis by logistic regression. (2) Relations\nwere observed between ground-water quality and the percentage of\nland-cover categories within circular regions (buffers) around wells.\nSeveral buffer sizes were evaluated; the buffer size that provided\nthe strongest relation was selected for use in the logistic regression\nmodels. (3) Relations between concentrations of atrazine/DEA and\nnitrate in ground water and atrazine use, fertilizer use, geology,\nhydrogeomorphic regions, land cover, precipitation, soils, and\nwell-construction data were evaluated, and several preliminary\nmultivariate models with various combinations of independent variables\nwere constructed. (4) The multivariate models that best predicted\nthe presence of atrazine/DEA and elevated concentrations of nitrate\nin ground water were selected. (5) The accuracy of the multivariate\nmodels was confirmed by validating the models with an independent\nset of ground-water quality data. (6) The multivariate models were\nentered into a geographic information system and the probability\nGRIDS were constructed.","language":"ENGLISH","doi":"10.3133/ofr03234","usgsCitation":"Rupert, M.G., 2003, Raster dataset showing the probability of elevated concentrations of nitrate in ground water in Colorado, hydrogeomorphic regions and fertilizer use estimates included: U.S. Geological Survey Open-File Report 2003-234, 128 p., https://doi.org/10.3133/ofr03234.","productDescription":"128 p.","costCenters":[],"links":[{"id":193009,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":6651,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://water.usgs.gov/lookup/getspatial?nit_hyd_fert","linkFileType":{"id":5,"text":"html"}},{"id":272832,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/atra_hyd_use.xml"},{"id":272831,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/atra_hyd_nuse.xml"},{"id":272833,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/atra_nhyd_nus.xml"},{"id":272834,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/atra_nhyd_use.xml"},{"id":277089,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/nit_hyd_fert.xml"},{"id":277090,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/nit_hyd_nfert.xml"},{"id":277091,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/nit_nhyd_fert.xml"}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.80111111111111,36.41722222222222 ], [ -109.80111111111111,41.56722222222223 ], [ -101.4675,41.56722222222223 ], [ -101.4675,36.41722222222222 ], [ -109.80111111111111,36.41722222222222 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e47e2e4b07f02db4bae2c","contributors":{"authors":[{"text":"Rupert, Michael G. mgrupert@usgs.gov","contributorId":1194,"corporation":false,"usgs":true,"family":"Rupert","given":"Michael","email":"mgrupert@usgs.gov","middleInitial":"G.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":282984,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70142600,"text":"70142600 - 2003 - Trace-metal sources and their release from mine wastes: examples from humidity cell tests of hardrock mine waste and from Warrior Basin coal","interactions":[],"lastModifiedDate":"2015-03-09T09:50:04","indexId":"70142600","displayToPublicDate":"2003-06-06T11:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Trace-metal sources and their release from mine wastes: examples from humidity cell tests of hardrock mine waste and from Warrior Basin coal","docAbstract":"To assess the potential impact of metal and acid contamination from mine-waste piles, it is important to identify the mineralogic source of trace metals and their mode of occurrence. Microscopic analysis of mine-waste samples from both hard-rock and coalmine waste samples demonstrate a microstructural control, as well as mineralogic control, on the source and release of trace metals into local water systems. The samples discussed herein show multiple periods of sulfide mineralization with varying concentrations of trace metals. In the first case study, two proprietary hard-rock mine-waste samples exposed to a series of humidity cell tests (which simulate intense chemical weathering conditions) generated acid and released trace metals. Some trace elements of interest were: arsenic (45-120 ppm), copper (60-320 ppm), and zinc (30-2,500 ppm). Untested and humidity cell-exposed samples were studied by X-ray diffraction, scanning electron microscope with energy dispersive X-ray (SEM/EDX), and electron microprobe analysis. Studies of one sample set revealed arsenic-bearing pyrite in early iron- and magnesium-rich carbonate-filled microveins, and iron-, copper-, arsenic-, antimony-bearing sulfides in later crosscutting silica-filled microveins. Post humidity cell tests indicated that the carbonate minerals were removed by leaching in the humidity cells, exposing pyrite to oxidative conditions. However, sulfides in the silica-filled veins were more protected. Therefore, the trace metals contained in the sulfides within the silica-filled microveins may be released to the surface and (or) ground water system more slowly over a greater time period. In the second case study, trace metal-rich pyrite-bearing coals from the Warrior Basin, Alabama were analyzed. Arsenic-bearing pyrite was observed in a late-stage pyrite phase in microfaults and microveins that crosscut earlier arsenic.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Joint Conference of the Billings Land Reclamation Symposium and the Annual Meeting of the American Society of Mining and Reclamation","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Joint Conference of the Billings Land Reclamation Symposium and the Annual Meeting of the American Society of Mining and Reclamation","conferenceDate":"06/03/2003","conferenceLocation":"Billings, MT","language":"English","publisher":"American Society of Mining and Reclamation","publisherLocation":"Lexington, KY","usgsCitation":"Diehl, S.F., Smith, K.S., Desborough, G.A., White, W., Lapakko, K., Goldhaber, M.B., and Fey, D.L., 2003, Trace-metal sources and their release from mine wastes: examples from humidity cell tests of hardrock mine waste and from Warrior Basin coal, in Proceedings of the Joint Conference of the Billings Land Reclamation Symposium and the Annual Meeting of the American Society of Mining and Reclamation, Billings, MT, 06/03/2003, p. 232-253.","productDescription":"22 p.","startPage":"232","endPage":"253","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":298352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54fec43ee4b02419550debe8","contributors":{"authors":[{"text":"Diehl, S. F.","contributorId":84780,"corporation":false,"usgs":true,"family":"Diehl","given":"S.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":541987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kathleen S. 0000-0001-8547-9804 ksmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8547-9804","contributorId":182,"corporation":false,"usgs":true,"family":"Smith","given":"Kathleen","email":"ksmith@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":541988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Desborough, G. A.","contributorId":34527,"corporation":false,"usgs":true,"family":"Desborough","given":"G.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":541989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, W.W.","contributorId":13488,"corporation":false,"usgs":true,"family":"White","given":"W.W.","email":"","affiliations":[],"preferred":false,"id":541990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lapakko, K.A.","contributorId":139598,"corporation":false,"usgs":false,"family":"Lapakko","given":"K.A.","affiliations":[],"preferred":false,"id":541991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goldhaber, Martin B. 0000-0002-1785-4243 mgold@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-4243","contributorId":1339,"corporation":false,"usgs":true,"family":"Goldhaber","given":"Martin","email":"mgold@usgs.gov","middleInitial":"B.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":541992,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fey, David L. dfey@usgs.gov","contributorId":713,"corporation":false,"usgs":true,"family":"Fey","given":"David","email":"dfey@usgs.gov","middleInitial":"L.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":541993,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70182123,"text":"70182123 - 2003 - Identifying relationships between baseflow geochemistry and land use with synoptic sampling and R-mode factor analysis","interactions":[],"lastModifiedDate":"2021-08-08T18:47:39.809709","indexId":"70182123","displayToPublicDate":"2003-06-06T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Identifying relationships between baseflow geochemistry and land use with synoptic sampling and R-mode factor analysis","docAbstract":"The relationship between land use and stream chemistry is often explored through synoptic sampling of rivers at baseflow conditions. However, baseflow chemistry is likely to vary temporally and spatially with land use. The purpose of our study is to examine the usefulness of the synoptic sampling approach for identifying the relationship between complex land use configurations and stream water quality. This study compares biogeochemical data from three synoptic sampling events representing the temporal variability of baseflow chemistry and land use using R-mode factor analysis. Separate R-mode factor analyses of the data from individual sampling events yielded only two consistent factors. Agricultural activity was associated with elevated levels of Ca2+, Mg2+, alkalinity, and frequently K+, SO2-4, and NO-3. Urban areas were associated with higher concentrations of Na+, K+, and Cl-. Other retained factors were not consistent among sampling events, and some factors were difficult to interpret in the context of biogeochemical sources and processes. When all data were combined, further associations were revealed such as an inverse relationship between the proportion of wetlands and stream nitrate concentrations. We also found that barren lands were associated with elevated sulfate levels. This research suggests that an individual sampling event is unlikely to characterize adequately the complex processes controlling interactions between land use and stream chemistry. Combining data collected over two years during three synoptic sampling events appears to enhance our ability to understand processes linking stream chemistry and land use.
","language":"English","publisher":"American Society of Agronomy","doi":"10.2134/jeq2003.1800","usgsCitation":"Wayland, K.G., Long, D.T., Hyndman, D.W., Pijanowski, B.C., Woodhams, S.M., and Haak, S.K., 2003, Identifying relationships between baseflow geochemistry and land use with synoptic sampling and R-mode factor analysis: Journal of Environmental Quality, v. 32, p. 180-190, https://doi.org/10.2134/jeq2003.1800.","productDescription":"11 p.","startPage":"180","endPage":"190","costCenters":[],"links":[{"id":387752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","city":"Traverse City","otherGeospatial":"Grand Traverse Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.87487792968749,\n 45.37916094640917\n ],\n [\n -85.3143310546875,\n 45.471688258104614\n ],\n [\n -85.4791259765625,\n 45.298075138707965\n ],\n [\n -85.78125,\n 45.22074260255366\n ],\n [\n -85.9295654296875,\n 44.94536144236941\n ],\n [\n -85.814208984375,\n 44.797428998555645\n ],\n [\n -85.5230712890625,\n 44.69989765840318\n ],\n [\n -85.220947265625,\n 44.78963254761407\n ],\n [\n -84.87487792968749,\n 45.37916094640917\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a6c83be4b025c4642862de","contributors":{"authors":[{"text":"Wayland, Karen G.","contributorId":181831,"corporation":false,"usgs":false,"family":"Wayland","given":"Karen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":669704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, David T.","contributorId":20364,"corporation":false,"usgs":true,"family":"Long","given":"David","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":669705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyndman, David W.","contributorId":7868,"corporation":false,"usgs":true,"family":"Hyndman","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":669706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pijanowski, Bryan C.","contributorId":35654,"corporation":false,"usgs":true,"family":"Pijanowski","given":"Bryan","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":669707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodhams, Sarah M.","contributorId":181832,"corporation":false,"usgs":false,"family":"Woodhams","given":"Sarah","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":669708,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haak, Sheridan K.","contributorId":181833,"corporation":false,"usgs":false,"family":"Haak","given":"Sheridan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":669709,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201525,"text":"70201525 - 2003 - Modeling mangrove forest migration along the southwest coast of Florida under climate change","interactions":[],"lastModifiedDate":"2018-12-17T08:55:53","indexId":"70201525","displayToPublicDate":"2003-06-01T08:55:22","publicationYear":"2003","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":" Modeling mangrove forest migration along the southwest coast of Florida under climate change","docAbstract":"Mangrove forests dominate in the intertidal zones of the tropical extent of the coast about the Gulf of Mexico, USA. Global climate change forecasts suggest that these coastal forests will be among those ecosystems most immediately threatened by projected increases in sea level and hurricanes. The interactive effects of environmental conditions that prevail in these forests and the changes that are likely to occur in a global warming climate may lead to major shifts in forest composition, structure, and function of mangrove ecosystems.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Integrated Assessment of the Climate Change Impacts on the Gulf Coast Region","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Gulf Coast Climate Change Assessment Council (GCRCC); Louisiana State University Graphic Services","usgsCitation":"Doyle, T.W., Girod, G.F., and Books, M.A., 2003, Modeling mangrove forest migration along the southwest coast of Florida under climate change, chap. 12 of Integrated Assessment of the Climate Change Impacts on the Gulf Coast Region, p. 211-222.","productDescription":"12 p.","startPage":"211","endPage":"222","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":360353,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360352,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.climateimpacts.org/us-climate-assess-2000/regions/gulf-coast/gulfcoast-reports.htm#integrated"}],"country":"United States","state":"Florida","otherGeospatial":"Mangrove Forest","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c18c426e4b006c4f856ace9","contributors":{"authors":[{"text":"Doyle, Thomas W. 0000-0001-5754-0671 doylet@usgs.gov","orcid":"https://orcid.org/0000-0001-5754-0671","contributorId":703,"corporation":false,"usgs":true,"family":"Doyle","given":"Thomas","email":"doylet@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":754387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Girod, Garrett F.","contributorId":211561,"corporation":false,"usgs":true,"family":"Girod","given":"Garrett","email":"","middleInitial":"F.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":754388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Books, Mark A.","contributorId":211562,"corporation":false,"usgs":true,"family":"Books","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":754389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201524,"text":"70201524 - 2003 - Predicting coastal retreat in the Florida Big Bend region of the Gulf Coast under climate change induced sea-level rise","interactions":[],"lastModifiedDate":"2018-12-17T08:58:09","indexId":"70201524","displayToPublicDate":"2003-06-01T08:39:38","publicationYear":"2003","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"11","title":"Predicting coastal retreat in the Florida Big Bend region of the Gulf Coast under climate change induced sea-level rise","docAbstract":"Many wildlife preserves and refuges in coastal areas of our nation are slowly being inundated by rising sea-level. Land elevation and tidal flooding are key factors controlling the extent and zonation of coastal habitats. Warming of our global environment threatens to speed the rate of sea-level rise and perhaps further amplify the detrimental effects of tropical storms, droughts, and lightning fires. A field and modeling study was conducted to determine the current status of emergent vegetation and surficial hydrology and to predict marsh transgression under rising sea-level. Field surveys were conducted to relate vegetation cover and ecotones to surface elevation and tidal inundation. A regional site application of a GIS-based simulation model, WETLANDS, was developed to predict ecosystem response to changing sea-level conditions on a coastal reach of the Big Bend region in northwest Florida. The WETLANDS model contains functional probabilities of community tolerance to flooding conditions that dictate the rate and process of ecological succession and coastal retreat. Map information of hypsography and bathymetry of the study area were digitized and interpolated to construct a digital elevation model. Classified thematic mapper imagery of aquatic and terrestrial habitat at a community level was used to initialize model simulation by vegetative type. Model simulations were generated to predict a likelihood index of habitat change and conversion under different scenarios of sea-level rise. The WETLANDS model was applied to track the process and pattern of coastal inundation over space and time for low, mid, and high sea-level rise projections of 15, 50, and 95 cm over the next century. Model results indicated that major portions of this coastal zone will be permanently inundated by 2100, bringing about a combined migration of marsh habitat and displacement of forest habitat. Results show that lowland pine forests will undergo retreat on the order of thousands of hectares over the 21st century. Coastal marsh extent may actually increase slightly as a function of the low lying topography. Socioeconomic implications may be nominal for this area given its remote and fairly undeveloped and protected coast-202 line. The model offers a technological tool for research and policy purposes that allows for effective land and water management, risk assessment, and cumulative impact analysis of wetland systems and landscapes.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Integrated Assessment of the Climate Change Impacts on the Gulf Coast Region","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Gulf Coast Climate Change Assessment Council (GCRCC); Louisiana State University Graphic Services","usgsCitation":"Doyle, T.W., Day, R.H., and Biagas, J.M., 2003, Predicting coastal retreat in the Florida Big Bend region of the Gulf Coast under climate change induced sea-level rise, chap. 11 of Integrated Assessment of the Climate Change Impacts on the Gulf Coast Region, p. 201-209.","productDescription":"9 p.","startPage":"201","endPage":"209","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":360351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":360350,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.climateimpacts.org/us-climate-assess-2000/regions/gulf-coast/gulfcoast-reports.htm#integrated"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Big Bend Region; Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.51507568359375,\n 29.8382614512946\n ],\n [\n -83.9300537109375,\n 29.8382614512946\n ],\n [\n -83.9300537109375,\n 30.20211367909724\n ],\n [\n -84.51507568359375,\n 30.20211367909724\n ],\n [\n -84.51507568359375,\n 29.8382614512946\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c18c426e4b006c4f856acec","contributors":{"authors":[{"text":"Doyle, Thomas W. 0000-0001-5754-0671 doylet@usgs.gov","orcid":"https://orcid.org/0000-0001-5754-0671","contributorId":703,"corporation":false,"usgs":true,"family":"Doyle","given":"Thomas","email":"doylet@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":754384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Richard H. 0000-0002-5959-7054 dayr@usgs.gov","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":2427,"corporation":false,"usgs":true,"family":"Day","given":"Richard","email":"dayr@usgs.gov","middleInitial":"H.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":754385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biagas, Janelda M. 0000-0001-5548-1970 biagasj@usgs.gov","orcid":"https://orcid.org/0000-0001-5548-1970","contributorId":4613,"corporation":false,"usgs":true,"family":"Biagas","given":"Janelda","email":"biagasj@usgs.gov","middleInitial":"M.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":754386,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":51419,"text":"ofr03192 - 2003 - Generation and migration of petroleum in Iraq: a 21/2D and 3D modeling study of Jurassic source rocks : compiled PowerPoint Slides","interactions":[],"lastModifiedDate":"2012-02-02T00:11:22","indexId":"ofr03192","displayToPublicDate":"2003-06-01T00:00:00","publicationYear":"2003","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":"2003-192","title":"Generation and migration of petroleum in Iraq: a 21/2D and 3D modeling study of Jurassic source rocks : compiled PowerPoint Slides","language":"ENGLISH","doi":"10.3133/ofr03192","usgsCitation":"Pitman, J.K., Steinshouer, D.W., and Lewan, M., 2003, Generation and migration of petroleum in Iraq: a 21/2D and 3D modeling study of Jurassic source rocks : compiled PowerPoint Slides: U.S. Geological Survey Open-File Report 2003-192, 17 slides, https://doi.org/10.3133/ofr03192.","productDescription":"17 slides","costCenters":[],"links":[{"id":179030,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":4434,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2003/ofr-03-192/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b24e4b07f02db6aebda","contributors":{"authors":[{"text":"Pitman, Janet K. 0000-0002-0441-779X jpitman@usgs.gov","orcid":"https://orcid.org/0000-0002-0441-779X","contributorId":767,"corporation":false,"usgs":true,"family":"Pitman","given":"Janet","email":"jpitman@usgs.gov","middleInitial":"K.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":243521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steinshouer, Douglas W.","contributorId":54628,"corporation":false,"usgs":true,"family":"Steinshouer","given":"Douglas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":243523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lewan, Michael D. mlewan@usgs.gov","contributorId":940,"corporation":false,"usgs":true,"family":"Lewan","given":"Michael D.","email":"mlewan@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":243522,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":50857,"text":"wri034094 - 2003 - Water-quality trend analysis and sampling design for streams in North Dakota, 1971-2000","interactions":[],"lastModifiedDate":"2022-12-23T19:45:44.245526","indexId":"wri034094","displayToPublicDate":"2003-06-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":342,"text":"Water-Resources Investigations Report","code":"WRI","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2003-4094","title":"Water-quality trend analysis and sampling design for streams in North Dakota, 1971-2000","docAbstract":"This report presents the results of a study conducted by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, to analyze historical water-quality trends in selected dissolved major ions, nutrients, and dissolved trace metals for 10 streams in southwestern and eastern North Dakota and to develop an efficient sampling design to monitor future water-quality trends. A time-series model for daily streamflow and constituent concentration was used to identify significant concentration trends, separate natural hydroclimatic variability in concentration from variability that could have resulted from anthropogenic causes, and evaluate various sampling designs to monitor future water-quality trends.
The interannual variability in concentration as a result of variability in streamflow, referred to as the annual concentration anomaly, generally was high for all constituents and streams used in the trend analysis and was particularly sensitive to the severe drought that occurred in the late 1980's and the very wet period that began in 1993 and has persisted to the present (2002). Although climatic conditions were similar across North Dakota during the trend-analysis period (1971-2000), significant differences occurred in the annual concentration anomalies from constituent to constituent and location to location, especially during the drought and the wet period.
Numerous trends were detected in the historical constituent concentrations after the annual concentration anomalies were removed. The trends within each of the constituent groups (major ions, nutrients, and trace metals) showed general agreement among the streams. For most locations, the largest dissolved major-ion concentrations occurred during the late 1970's and concentrations in the mid- to late 1990's were smaller than concentrations during the late 1970's. However, the largest concentrations for three of the Missouri River tributaries and one of the Red River of the North tributaries occurred during the mid- to late 1990's.
Concentration trends for total ammonia plus organic nitrogen showed close agreement among the streams for which that constituent was evaluated. The largest concentrations occurred during the early 1980's, and the smallest concentrations occurred during the early 1990's. Nutrient data were not available for the early 1970's or late 1990's. Although a detailed analysis of the causes of the trends was beyond the scope of this report, a preliminary analysis of cropland, livestock-inventory, and oil-production data for 1971-2000 indicated the concentration trends may be related to the livestock-inventory and oil-production activities in the basins.
Dissolved iron and manganese concentrations for the southwestern North Dakota streams generally remained stable during 1971-2000. However, many of the recorded concentrations for those streams were less than the detection limit, and trends that were masked by censoring may have occurred. Several significant trends were detected in dissolved iron and manganese concentrations for the eastern North Dakota streams. Concentrations for those streams either remained stable or increased during most of the 1970's and then decreased rapidly for about 2 years beginning in the late 1970's. The concentrations were relatively stable from the early 1980's to 2000 except at two locations where dissolved iron concentrations increased during the early 1990's.
The most efficient overall sampling designs for the detection of annual trends (that is, trends that occur uniformly during the entire year) consisted of balanced designs in which the sampling dates and the number of samples collected remained fixed from year to year and in which the samples were collected throughout the year rather than in a short timespan. The best overall design for the detection of annual trends consisted of three samples per year, with samples collected near the beginning of December, April, and August. That design had acceptable sensitivity for the detection of trends in most constituents at all locations. Little improvement in sensitivity was achieved by collecting more than three samples per year.
The sampling designs that were first evaluated for annual trends also were evaluated with regard to their sensitivity to detect seasonal trends that occurred during three seasons--April through August, August through December, and December through April. Design results indicated that an average of one extra sample per station per year resulted in an efficient design for detecting seasonal trends. However, allocation of the extra samples varied depending on the station, month, and constituent group (major ions, nutrients, and trace metals).
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