Flooding and high ground-water levels after large or frequent rainstorms have occurred in an area of about 30 square miles along the eastern bank of the Cedar River from Cedar Falls in northwest Black Hawk County to Janesville in southwest Bremer County, Iowa. The U.S. Geological Survey, in cooperation with Black Hawk County, conducted a hydrologic study of the Cedar River alluvium in the northwest Black Hawk and southwest Bremer Counties, to improve understanding of the ground-water flow system and evaluate the effects of hypothetical variations in recharge and discharge conditions.
\nA steady-state ground-water flow model was constructed for the area using November 2001 hydrologic conditions. The model was discretized into an 83-row by 47-column grid of cells measuring approximately 500 feet by 500 feet. Two model layers, one for the alluvium and one for the underlying bedrock units, were used to represent flow in the area.
\nPrecipitation during 2001 was similar to historical normals. Precipitation during 1999, especially during the summer when flooding occurred, was well above the historical normals. Borings in the unconsolidated deposits in the study area confirmed the presence of a bedrock valley dipping to the south in the central part of the study area. Water-level measurements in 2001 indicate that ground-water flow in much of the alluvial aquifer parallels the direction of flow in the Cedar River toward the south rather than following shorter flow paths to the west toward the Cedar River.
\nUnder steady-state conditions and 2001 pumpage, primary sources of inflow to the ground-water flow system are the Cedar River (65.5 percent), recharge through infiltration of precipitation and upland runoff (31.4 percent), and subsurface flow across the lateral boundaries (3.1 percent). The primary components of outflow from the ground-water flow system are intermittent streams (56.0 percent) and the Cedar River (43.7 percent).
\nTwo hypothetical scenarios were used to assess the potential effects of higher river levels and increased recharge compared to the steadystate conditions. For one scenario, river levels were set to bankfull conditions, and a recharge of 1.2 times the steady-state rate was applied. This simulation was used to evaluate the effects of wet conditions. This scenario led to increased water levels, in general, and large areas of shallow (0 to 10 feet) depths to water along the eastern part of the model area near Highway 218. For the second scenario, conditions were the same as for the first scenario, but streambed conductance of intermittent streams modeled as drains was increased to 10 times the steady-state value to simulate increased flow of water from the shallow groundwater flow system. The area with depth to water of 0 to 10 feet along the eastern part of the model area was substantially smaller than that of the first scenario.
\nIn general, once high ground-water levels occur, either because of high Cedar River water Abstract levels or above normal local precipitation or both, ground-water in the central part of the study area along Highway 218 flows toward the south rather than following shorter flow paths to the Cedar River. Intermittent streams in the study area discharge substantial amounts of water from the ground-water flow system.
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Flow System, Nevada and California, 1913-98","indexId":"wri20034245","publicationYear":"2003","noYear":false,"title":"Estimated Ground-Water Withdrawals from the Death Valley Regional Flow System, Nevada and California, 1913-98"},"predicate":"SUPERSEDED_BY","object":{"id":85817,"text":"ds340 - 2008 - Update to the Ground-Water Withdrawals Database for the Death Valley Regional Ground-Water Flow System, Nevada and California, 1913-2003","indexId":"ds340","publicationYear":"2008","noYear":false,"title":"Update to the Ground-Water Withdrawals Database for the Death Valley Regional Ground-Water Flow System, Nevada and California, 1913-2003"},"id":1}],"supersededBy":{"id":85817,"text":"ds340 - 2008 - Update to the Ground-Water Withdrawals Database for the Death Valley Regional Ground-Water Flow System, Nevada and California, 1913-2003","indexId":"ds340","publicationYear":"2008","noYear":false,"title":"Update to the Ground-Water Withdrawals Database for the Death Valley Regional Ground-Water 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Withdrawal locations and depths of production intervals were estimated and associated errors were reported for 9,300 wells. Withdrawals were grouped into three categories: mining, public-supply, and commercial water use; domestic water use; and irrigation water use. In this report, groupings were based on the method used to estimate pumpage.\r\n\r\nCumulative ground-water withdrawals from 1913 through 1998 totaled 3 million acre-feet, most of which was used to irrigate alfalfa. Annual withdrawal for irrigation ranged from 80 to almost 100 percent of the total pumpage. About 75,000 acre-feet was withdrawn for irrigation in 1998. Annual irrigation withdrawals generally were estimated as the product of irrigated acreage and application rate.\r\n\r\nAbout 320 fields totaling 11,000 acres were identified in six hydrographic areas. Annual application rates for high water-use crops ranged from 5 feet in Penoyer Valley to 9 feet in Pahrump Valley. The uncertainty in the estimates of ground-water withdrawals was attributed primarily to the uncertainty of application rate estimates. Annual ground-water withdrawal was estimated at about 90,000 acre-feet in 1998 with an assigned uncertainty bounded by 60,000 to 130,000 acre-feet.","language":"ENGLISH","publisher":"Geological Survey (U.S.)","doi":"10.3133/wri20034245","usgsCitation":"Moreo, M.T., Halford, K.J., La Camera, R.J., and Laczniak, R.J., 2003, Estimated Ground-Water Withdrawals from the Death Valley Regional Flow System, Nevada and California, 1913-98 (Superseded by DS 340): U.S. Geological Survey Water-Resources Investigations Report 2003-4245, 28 p., https://doi.org/10.3133/wri20034245.","productDescription":"28 p.","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":3970,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034245/","linkFileType":{"id":5,"text":"html"}},{"id":168890,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"edition":"Superseded by DS 340","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4aa8e4b07f02db66796a","contributors":{"authors":[{"text":"Moreo, Michael T. 0000-0002-9122-6958 mtmoreo@usgs.gov","orcid":"https://orcid.org/0000-0002-9122-6958","contributorId":2363,"corporation":false,"usgs":true,"family":"Moreo","given":"Michael","email":"mtmoreo@usgs.gov","middleInitial":"T.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":235830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":235829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Camera, Richard J.","contributorId":52212,"corporation":false,"usgs":true,"family":"La Camera","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":235831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laczniak, Randell J.","contributorId":90687,"corporation":false,"usgs":true,"family":"Laczniak","given":"Randell","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":235832,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156743,"text":"70156743 - 2003 - The National Map: from geography to mapping and back again","interactions":[],"lastModifiedDate":"2015-08-27T11:21:03","indexId":"70156743","displayToPublicDate":"2003-10-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The National Map: from geography to mapping and back again","docAbstract":"When the means of production for national base mapping were capital intensive, required large production facilities, and had ill-defined markets, Federal Government mapping agencies were the primary providers of the spatial data needed for economic development, environmental management, and national defense. With desktop geographic information systems now ubiquitous, source data available as a commodity from private industry, and the realization that many complex problems faced by society need far more and different kinds of spatial data for their solutions, national mapping organizations must realign their business strategies to meet growing demand and anticipate the needs of a rapidly changing geographic information environment. The National Map of the United States builds on a sound historic foundation of describing and monitoring the land surface and adds a focused effort to produce improved understanding, modeling, and prediction of land-surface change. These added dimensions bring to bear a broader spectrum of geographic science to address extant and emerging issues. Within the overarching construct of The National Map, the U.S. Geological Survey (USGS) is making a transition from data collector to guarantor of national data completeness; from producing paper maps to supporting an online, seamless, integrated database; and from simply describing the Nation’s landscape to linking these descriptions with increased scientific understanding. Implementing the full spectrum of geographic science addresses a myriad of public policy issues, including land and natural resource management, recreation, urban growth, human health, and emergency planning, response, and recovery. Neither these issues nor the science and technologies needed to deal with them are static. A robust research agenda is needed to understand these changes and realize The National Map vision. Initial successes have been achieved. These accomplishments demonstrate the utility of The National Map to the Nation and give confidence in evolving its future applications.
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The elastic moduli of hydrate/sediment mixtures depend on the elastic properties of the individual sedimentary particles and the manner in which they are arranged. In this chapter, we present some rock physics data currently available from literature. The unreferenced values in Table I were not measured directly, but were derived from other values in Tables I and II using standard relationships between elastic properties for homogeneous, isotropic material. These derivations allow us to extend the list of physical property estimates, but at the expense of introducing uncertainties due to combining property values measured under different physical conditions. This is most apparent in the case of structure II (sII) hydrate for which very few physical properties have been measured under identical conditions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Natural gas hydrate in oceanic and permafrost environments","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-94-011-4387-5_20","usgsCitation":"Dvorkin, J., Helgerud, M.B., Waite, W., Kirby, S.H., and Nur, A., 2003, Introduction to physical properties and elasticity models, chap. 20 of Natural gas hydrate in oceanic and permafrost environments, v. 5, p. 245-260, https://doi.org/10.1007/978-94-011-4387-5_20.","productDescription":"16 p.","startPage":"245","endPage":"260","costCenters":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":293892,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54195142e4b091c7ffc8e735","contributors":{"authors":[{"text":"Dvorkin, Jack","contributorId":51221,"corporation":false,"usgs":true,"family":"Dvorkin","given":"Jack","email":"","affiliations":[],"preferred":false,"id":501229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helgerud, Michael B.","contributorId":59361,"corporation":false,"usgs":true,"family":"Helgerud","given":"Michael","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":501230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":501226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirby, Stephen H. 0000-0003-1636-4688 skirby@usgs.gov","orcid":"https://orcid.org/0000-0003-1636-4688","contributorId":2752,"corporation":false,"usgs":true,"family":"Kirby","given":"Stephen","email":"skirby@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":501227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nur, Amos","contributorId":34444,"corporation":false,"usgs":true,"family":"Nur","given":"Amos","email":"","affiliations":[],"preferred":false,"id":501228,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70039858,"text":"70039858 - 2003 - The use of traditional Hawaiian knowledge in the contemporary management of marine resources","interactions":[],"lastModifiedDate":"2022-06-06T14:00:41.458983","indexId":"70039858","displayToPublicDate":"2003-09-11T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1658,"text":"Fisheries Center Research Reports","active":true,"publicationSubtype":{"id":10}},"title":"The use of traditional Hawaiian knowledge in the contemporary management of marine resources","docAbstract":"It is traditional for Hawaiians to \"consult nature\" so that fishing is practiced at times and places, and with gear that causes minimum disruption of natural biological and ecological processes. The Ho'olehua Hawaiian Homestead continues this tradition in and around Mo'omomi Bay on the northwest coast of the island of Moloka'i. This community relies heavily on inshore marine resources for subsistence and consequently, has an intimate knowledge of these resources. The shared knowledge, beliefs, and values of the community are culturally channeled to promote proper fishing behavior. This informal system brings more knowledge, experience, and moral commitment to fishery conservation than more centralized government management. Community-based management in the Mo'omomi area involves observational processes and problem-solving strategies for the purpose of conservation. The system is not articulated in the manner of Western science, but relies instead on mental models. These models foster a practical understanding of local inshore resource dynamics by the fishing community and, thus, lend credibility to unwritten standards for fishing conduct. The \"code of conduct\" is concerned with how people fish rather than how much they catch.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Putting fishers’ knowledge to work: Conference proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"First international conference on the management value of the resource knowledge of small scale, indigenous and commercial fishers","conferenceDate":"August 27-30, 2001","language":"English","publisher":"The University of British Columbia","publisherLocation":"Vancouver, BC","usgsCitation":"Poepoe, K.K., Bartram, P.K., and Friedlander, A.M., 2003, The use of traditional Hawaiian knowledge in the contemporary management of marine resources: Fisheries Center Research Reports, v. 11, no. 1, p. 328-339.","productDescription":"12 p.","startPage":"328","endPage":"339","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":261834,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":319832,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://open.library.ubc.ca/soa/cIRcle/collections/facultyresearchandpublications/52383/items/1.0074793"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Moloka'i","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -157.33333333333334,21.033333333333335 ], [ -157.33333333333334,21.233333333333334 ], [ -156.7,21.233333333333334 ], [ -156.7,21.033333333333335 ], [ -157.33333333333334,21.033333333333335 ] ] ] } } ] }","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bb19ee4b08c986b325370","contributors":{"editors":[{"text":"Haggan, Nigel","contributorId":292287,"corporation":false,"usgs":false,"family":"Haggan","given":"Nigel","email":"","affiliations":[],"preferred":false,"id":844212,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Brignall, Claire","contributorId":292288,"corporation":false,"usgs":false,"family":"Brignall","given":"Claire","email":"","affiliations":[],"preferred":false,"id":844213,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Wood, Louisa E.","contributorId":292289,"corporation":false,"usgs":false,"family":"Wood","given":"Louisa","middleInitial":"E.","affiliations":[],"preferred":false,"id":844214,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Poepoe, Kelson K.","contributorId":78976,"corporation":false,"usgs":true,"family":"Poepoe","given":"Kelson","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":467080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartram, Paul K.","contributorId":25024,"corporation":false,"usgs":true,"family":"Bartram","given":"Paul","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":467078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedlander, Alan M. afriedlander@usgs.gov","contributorId":53079,"corporation":false,"usgs":true,"family":"Friedlander","given":"Alan","email":"afriedlander@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":false,"id":467079,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159930,"text":"70159930 - 2003 - Reproductive maturation and senescence in the female brown bear","interactions":[],"lastModifiedDate":"2021-02-08T16:38:28.751633","indexId":"70159930","displayToPublicDate":"2003-09-07T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3671,"text":"Ursus","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive maturation and senescence in the female brown bear","docAbstract":"Changes in age-specific reproductive rates can have important implications for managing populations, but the number of female brown (grizzly) bears (Ursus arctos) observed in any one study is usually inadequate to quantify such patterns, especially for older females and in hunted areas. We examined patterns of reproductive maturation and senescence in female brown bears by combining data from 20 study areas from Sweden, Alaska, Canada, and the continental United States. We assessed reproductive performance based on 4,726 radiocollared years for free-ranging female brown bears (age ≥3); 482 of these were for bears ≥20 years of age. We modeled age-specific probability of litter production using extreme value distributions to describe probabilities for young- and old-age classes, and a power distribution function to describe probabilities for prime-aged animals. We then fit 4 models to pooled observations from our 20 study areas. We used Akaike's Information Criterion (AIC) to select the best model. Inflection points suggest that major shifts in litter production occur at 4-5 and 28-29 years of age. The estimated model asymptote (0.332, 95% CI = 0.319-0.344) was consistent with the expected reproductive cycle of a cub litter every 3 years (0.333). We discuss assumptions and biases in data collection relative to the shape of the model curve. Our results conform to senescence theory and suggest that female age structure in contemporary brown bear populations is considerably younger than would be expected in the absence of modern man. This implies that selective pressures today differ from those that influenced brown bear evolution.
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E.","contributorId":45518,"corporation":false,"usgs":false,"family":"Swenson","given":"J.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":581125,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Sterling D.","contributorId":7205,"corporation":false,"usgs":true,"family":"Miller","given":"Sterling","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":581126,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McLellan, B. N.","contributorId":82929,"corporation":false,"usgs":false,"family":"McLellan","given":"B.","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":581127,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Keay, Jeffrey A. jkeay@usgs.gov","contributorId":331,"corporation":false,"usgs":true,"family":"Keay","given":"Jeffrey","email":"jkeay@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":581128,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McCann, Robert","contributorId":150232,"corporation":false,"usgs":false,"family":"McCann","given":"Robert","email":"","affiliations":[],"preferred":false,"id":581129,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gibeau, Michael","contributorId":150233,"corporation":false,"usgs":false,"family":"Gibeau","given":"Michael","email":"","affiliations":[{"id":6658,"text":"Parks Canada","active":true,"usgs":false}],"preferred":false,"id":581130,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wakkinen, Wayne F.","contributorId":150234,"corporation":false,"usgs":false,"family":"Wakkinen","given":"Wayne","email":"","middleInitial":"F.","affiliations":[{"id":16279,"text":"Idaho Department of Fish & Game","active":true,"usgs":false}],"preferred":false,"id":581131,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mace, Richard D.","contributorId":150235,"corporation":false,"usgs":false,"family":"Mace","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":5099,"text":"Montana Department of Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":581132,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Kasworm, Wayne","contributorId":150237,"corporation":false,"usgs":false,"family":"Kasworm","given":"Wayne","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":581133,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Smith, Rodger","contributorId":150238,"corporation":false,"usgs":false,"family":"Smith","given":"Rodger","email":"","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":581134,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Herrero, Steven","contributorId":150239,"corporation":false,"usgs":false,"family":"Herrero","given":"Steven","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":581135,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70216682,"text":"70216682 - 2003 - Modeling paleoclimates","interactions":[],"lastModifiedDate":"2020-11-27T20:36:44.563313","indexId":"70216682","displayToPublicDate":"2003-09-02T14:34:45","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":"Modeling paleoclimates","docAbstract":"Paleoclimatic data and climate models play a complimentary role in understanding climate change. This chapter provides an overview of the process of climate-system modeling, presents the taxonomy of the models recently applied in the study of Quaternary climate change and variation, and discusses the development of climate modeling since the 1965 International Union for Quaternary Science (INQUA) volume and its companions are published. Models based on physical principles do have the potential to provide mechanistic explanations of past climatic variations, provided they are known to work, are applied in an appropriately designed experiment, and explicitly account for all of the components of the climate system that are involved in a particular climate change. Climate models can be classified according to the applications to which they are put, which include simulating the temporal evolution and spatial patterns of the climate system and the attendant responses of environmental subsystems. The objective of paleoclimate modeling is to quantify the behavior and variations of the components that describe the climate system.
","language":"English","publisher":"Elsevier","doi":"10.1016/S1571-0866(03)01027-3","usgsCitation":"Bartlein, P.J., and Hostetler, S.W., 2003, Modeling paleoclimates: Developments in Quaternary Sciences, p. 564-584, https://doi.org/10.1016/S1571-0866(03)01027-3.","productDescription":"21 p.","startPage":"564","endPage":"584","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":380866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bartlein, Patrick J","contributorId":194325,"corporation":false,"usgs":false,"family":"Bartlein","given":"Patrick","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":805879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":805880,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156746,"text":"70156746 - 2003 - Carbon dynamics and land-use choices: building a regional-scale multidisciplinary model","interactions":[],"lastModifiedDate":"2015-08-27T11:33:47","indexId":"70156746","displayToPublicDate":"2003-09-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Carbon dynamics and land-use choices: building a regional-scale multidisciplinary model","docAbstract":"Policy enabling tropical forests to approach their potential contribution to global-climate-change mitigation requires forecasts of land use and carbon storage on a large scale over long periods. In this paper, we present an integrated modeling methodology that addresses these needs. We model the dynamics of the human land-use system and of C pools contained in each ecosystem, as well as their interactions. The model is national scale, and is currently applied in a preliminary way to Costa Rica using data spanning a period of over 50 years. It combines an ecological process model, parameterized using field and other data, with an economic model, estimated using historical data to ensure a close link to actual behavior. These two models are linked so that ecological conditions affect land-use choices and vice versa. The integrated model predicts land use and its consequences for C storage for policy scenarios. These predictions can be used to create baselines, reward sequestration, and estimate the value in both environmental and economic terms of including C sequestration in tropical forests as part of the efforts to mitigate global climate change. The model can also be used to assess the benefits from costly activities to increase accuracy and thus reduce errors and their societal costs.
","language":"English","publisher":"Elsevier","doi":"10.1016/S0301-4797(03)00106-3","usgsCitation":"Kerr, S., Liu, S., Pfaff, A.S., and Hughes, R., 2003, Carbon dynamics and land-use choices: building a regional-scale multidisciplinary model: Journal of Environmental Management, v. 69, no. 1, p. 25-37, https://doi.org/10.1016/S0301-4797(03)00106-3.","productDescription":"13 p.","startPage":"25","endPage":"37","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":478342,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://ageconsearch.umn.edu/record/293008","text":"External Repository"},{"id":307615,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e034b2e4b0f42e3d040df2","contributors":{"authors":[{"text":"Kerr, Suzi","contributorId":147107,"corporation":false,"usgs":false,"family":"Kerr","given":"Suzi","email":"","affiliations":[],"preferred":false,"id":570347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Shu-Guang sliu@usgs.gov","contributorId":984,"corporation":false,"usgs":true,"family":"Liu","given":"Shu-Guang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":570348,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pfaff, Alexander S.P.","contributorId":77492,"corporation":false,"usgs":true,"family":"Pfaff","given":"Alexander","email":"","middleInitial":"S.P.","affiliations":[],"preferred":false,"id":570349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, R. Flint","contributorId":111314,"corporation":false,"usgs":true,"family":"Hughes","given":"R. Flint","affiliations":[],"preferred":false,"id":570350,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70156737,"text":"70156737 - 2003 - Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices","interactions":[],"lastModifiedDate":"2015-08-27T10:54:10","indexId":"70156737","displayToPublicDate":"2003-09-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices","docAbstract":"The Normalized Difference Vegetation Index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR) has been widely used to monitor moisture-related vegetation condition. The relationship between vegetation vigor and moisture availability, however, is complex and has not been adequately studied with satellite sensor data. To better understand this relationship, an analysis was conducted on time series of monthly NDVI (1989–2000) during the growing season in the north and central U.S. Great Plains. The NDVI was correlated to the Standardized Precipitation Index (SPI), a multiple-time scale meteorological-drought index based on precipitation. The 3-month SPI was found to have the best correlation with the NDVI, indicating lag and cumulative effects of precipitation on vegetation, but the correlation between NDVI and SPI varies significantly between months. The highest correlations occurred during the middle of the growing season, and lower correlations were noted at the beginning and end of the growing season in most of the area. A regression model with seasonal dummy variables reveals that the relationship between the NDVI and SPI is significant in both grasslands and croplands, if this seasonal effect is taken into account. Spatially, the best NDVI–SPI relationship occurred in areas with low soil water-holding capacity. Our most important finding is that NDVI is an effective indicator of vegetation-moisture condition, but seasonal timing should be taken into consideration when monitoring drought with the NDVI.
","language":"English","publisher":"Elsevier","doi":"10.1016/S0034-4257(03)00174-3","usgsCitation":"Ji, L., and Peters, A.J., 2003, Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices: Remote Sensing of Environment, v. 87, no. 1, p. 85-98, https://doi.org/10.1016/S0034-4257(03)00174-3.","productDescription":"14 p.","startPage":"85","endPage":"98","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":307607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"87","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e034b2e4b0f42e3d040dee","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":570316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peters, Albert J.","contributorId":92517,"corporation":false,"usgs":true,"family":"Peters","given":"Albert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":570317,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70156518,"text":"70156518 - 2003 - Vegetation greenness impacts on maximum and minimum temperatures in northeast Colorado","interactions":[],"lastModifiedDate":"2017-04-10T10:45:13","indexId":"70156518","displayToPublicDate":"2003-09-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3921,"text":"Meteorological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation greenness impacts on maximum and minimum temperatures in northeast Colorado","docAbstract":"The impact of vegetation on the microclimate has not been adequately considered in the analysis of temperature forecasting and modelling. To fill part of this gap, the following study was undertaken.
A daily 850–700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalised Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989–98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (r2 value) of surface maximum and minimum temperature by only the 850–700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850–700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the r2 values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March–October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.
Stream biogeochemistry is influenced by the physical and chemical processes that occur in the surrounding watershed. These processes include the mass loading of solutes from terrestrial and atmospheric sources, the physical transport of solutes within the watershed, and the transformation of solutes due to biogeochemical reactions. Research over the last two decades has identified the hyporheic zone as an important part of the stream system in which these processes occur. The hyporheic zone may be loosely defined as the porous areas of the stream bed and stream bank in which stream water mixes with shallow groundwater. Exchange of water and solutes between the stream proper and the hyporheic zone has many biogeochemical implications, due to differences in the chemical composition of surface and groundwater. For example, surface waters are typically oxidized environments with relatively high dissolved oxygen concentrations. In contrast, reducing conditions are often present in groundwater systems leading to low dissolved oxygen concentrations. Further, microbial oxidation of organic materials in groundwater leads to supersaturated concentrations of dissolved carbon dioxide relative to the atmosphere. Differences in surface and groundwater pH and temperature are also common. The hyporheic zone is therefore a mixing zone in which there are gradients in the concentrations of dissolved gasses, the concentrations of oxidized and reduced species, pH, and temperature. These gradients lead to biogeochemical reactions that ultimately affect stream water quality. Due to the complexity of these natural systems, modeling techniques are frequently employed to quantify process dynamics.
","language":"English","publisher":"Elsevier","doi":"10.1016/S0309-1708(03)00079-4","usgsCitation":"Runkel, R.L., McKnight, D.M., and Rajaram, H., 2003, Modeling hyporheic zone processes: Advances in Water Resources, v. 26, no. 9, p. 901-905, https://doi.org/10.1016/S0309-1708(03)00079-4.","productDescription":"5 p. ","startPage":"901","endPage":"905","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":337604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ca52d1e4b0849ce97c86d6","contributors":{"authors":[{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKnight, Diane M.","contributorId":59773,"corporation":false,"usgs":false,"family":"McKnight","given":"Diane","email":"","middleInitial":"M.","affiliations":[{"id":16833,"text":"INSTAAR, University of Colorado","active":true,"usgs":false}],"preferred":false,"id":684455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rajaram, Harihar","contributorId":61328,"corporation":false,"usgs":true,"family":"Rajaram","given":"Harihar","affiliations":[],"preferred":false,"id":684456,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":51437,"text":"wri034155 - 2003 - Comparison of Irrigation Water Use Estimates Calculated from Remotely Sensed Irrigated Acres and State Reported Irrigated Acres in the Lake Altus Drainage Basin, Oklahoma and Texas, 2000 Growing Season","interactions":[],"lastModifiedDate":"2012-02-02T00:11:30","indexId":"wri034155","displayToPublicDate":"2003-09-01T00:00:00","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":"2003-4155","title":"Comparison of Irrigation Water Use Estimates Calculated from Remotely Sensed Irrigated Acres and State Reported Irrigated Acres in the Lake Altus Drainage Basin, Oklahoma and Texas, 2000 Growing Season","docAbstract":"Increased demand for water in the Lake Altus drainage basin requires more accurate estimates of water use for irrigation. The U.S. Geological Survey, in cooperation with the U.S. Bureau of Reclamation, is investigating new techniques to improve water-use estimates for irrigation purposes in the Lake Altus drainage basin. Empirical estimates of reference evapotranspiration, crop evapotranspiration, and crop irrigation water requirements for nine major crops were calculated from September 1999 to October 2000 using a solar radiation-based evapotranspiration model. Estimates of irrigation water use were calculated using remotely sensed irrigated crop acres derived from Landsat 7 Enhanced Thematic Mapper Plus imagery and were compared with irrigation water-use estimates calculated from irrigated crop acres reported by the Oklahoma Water Resources Board and the Texas Water Development Board for the 2000 growing season. The techniques presented will help manage water resources in the Lake Altus drainage basin and may be transferable to other areas with similar water management needs.\r\n\r\nIrrigation water use calculated from the remotely sensed irrigated acres was estimated at 154,920 acre-feet; whereas, irrigation water use calculated from state reported irrigated crop acres was 196,026 acre-feet, a 23 percent difference. The greatest difference in irrigation water use was in Carson County, Texas. Irrigation water use for Carson County, Texas, calculated from the remotely sensed irrigated acres was 58,555 acrefeet; whereas, irrigation water use calculated from state reported irrigated acres was 138,180 acre-feet, an 81 percent difference. The second greatest difference in irrigation water use occurred in Beckham County, Oklahoma. Differences between the two irrigation water use estimates are due to the differences of irrigated crop acres derived from the mapping process and those reported by the Oklahoma Water Resources Board and Texas Water Development Board.","language":"ENGLISH","doi":"10.3133/wri034155","usgsCitation":"Masoner, J., Mladinich, C., Konduris, A., and Smith, S.J., 2003, Comparison of Irrigation Water Use Estimates Calculated from Remotely Sensed Irrigated Acres and State Reported Irrigated Acres in the Lake Altus Drainage Basin, Oklahoma and Texas, 2000 Growing Season: U.S. Geological Survey Water-Resources Investigations Report 2003-4155, 39 p., https://doi.org/10.3133/wri034155.","productDescription":"39 p.","costCenters":[],"links":[{"id":4447,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034155","linkFileType":{"id":5,"text":"html"}},{"id":178899,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b24e4b07f02db6ae42e","contributors":{"authors":[{"text":"Masoner, J.R.","contributorId":15690,"corporation":false,"usgs":true,"family":"Masoner","given":"J.R.","affiliations":[],"preferred":false,"id":243576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mladinich, C.S.","contributorId":61095,"corporation":false,"usgs":true,"family":"Mladinich","given":"C.S.","email":"","affiliations":[],"preferred":false,"id":243577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konduris, A.M.","contributorId":106567,"corporation":false,"usgs":true,"family":"Konduris","given":"A.M.","affiliations":[],"preferred":false,"id":243578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, S. Jerrod 0000-0002-9379-8167 sjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9379-8167","contributorId":981,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"sjsmith@usgs.gov","middleInitial":"Jerrod","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":243575,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":52905,"text":"ds80 - 2003 - Selected Images of the Pu'u 'O'o-Kupaianaha Eruption, 1983-1997","interactions":[],"lastModifiedDate":"2012-02-02T00:11:40","indexId":"ds80","displayToPublicDate":"2003-09-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"80","title":"Selected Images of the Pu'u 'O'o-Kupaianaha Eruption, 1983-1997","docAbstract":"The 100 images in this CD?ROM have been selected from the collections of the Hawaiian Volcano Observatory as enduring favorites of the staff, researchers, media, designers, and the public over time. They represent photographs of a variety of geological phenomena and eruptive events, chosen for their content, quality of exposure, and aesthetic appeal. The number was kept to 100 to maintain the high resolution desirable. Since 1997, digital imagery has been the predominant mode of photographically documenting the eruption. Many of these photos, from 1998 to the present, are viewable on the website: http://hvo.wr.usgs.gov/kilauea/update/archive/ \r\nEpisode numbers are given as E-numbers in parentheses before each caption that pertains to the Pu`u `O`o?Kupaianaha eruption; details of the episodes are given in table 1. Hawaiian words and place names are listed below to facilitate searching. All images included in this collection are owned by the U.S. Geological Survey, Hawaiian Volcano Observatory, and are in the public domain. Therefore, no permission or fee is required for their use. Please include photo credit for the photographer and the U.S. Geological Survey. We assume no responsibility for the modification of these images.","language":"ENGLISH","doi":"10.3133/ds80","isbn":"0607930616","usgsCitation":"Takahashi, T.J., Heliker, C.C., and Diggles, M.F., 2003, Selected Images of the Pu'u 'O'o-Kupaianaha Eruption, 1983-1997: U.S. Geological Survey Data Series 80, Online resource; 1 CD-ROM, https://doi.org/10.3133/ds80.","productDescription":"Online resource; 1 CD-ROM","costCenters":[],"links":[{"id":177135,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":4968,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/dds/dds-80/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b06e4b07f02db69a316","contributors":{"authors":[{"text":"Takahashi, Taeko Jane","contributorId":104049,"corporation":false,"usgs":true,"family":"Takahashi","given":"Taeko","email":"","middleInitial":"Jane","affiliations":[],"preferred":false,"id":246198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heliker, Christina C.","contributorId":68695,"corporation":false,"usgs":true,"family":"Heliker","given":"Christina","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":246197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diggles, Michael F. 0000-0002-9946-0247 mdiggles@usgs.gov","orcid":"https://orcid.org/0000-0002-9946-0247","contributorId":810,"corporation":false,"usgs":true,"family":"Diggles","given":"Michael","email":"mdiggles@usgs.gov","middleInitial":"F.","affiliations":[{"id":5053,"text":"IPDS Training","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"preferred":true,"id":246196,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":51976,"text":"wri034030 - 2003 - Simulation of streamflow and estimation of streamflow constituent loads in the San Antonio River watershed, Bexar County, Texas, 1997-2001","interactions":[],"lastModifiedDate":"2017-02-15T11:11:46","indexId":"wri034030","displayToPublicDate":"2003-09-01T00:00:00","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":"2003-4030","title":"Simulation of streamflow and estimation of streamflow constituent loads in the San Antonio River watershed, Bexar County, Texas, 1997-2001","docAbstract":"The U.S. Geological Survey developed watershed models (Hydrological Simulation Program—FORTRAN) to simulate streamflow and estimate streamflow constituent loads from five basins that compose the San Antonio River watershed in Bexar County, Texas. Rainfall and streamflow data collected during 1997–2001 were used to calibrate and test the model. The model was configured so that runoff from various land uses and discharges from other sources (such as wastewater recycling facilities) could be accounted for to indicate sources of streamflow. Simulated streamflow volumes were used with land-use-specific, water-quality data to compute streamflow loads of selected constituents from the various streamflow sources.
Model simulations for 1997–2001 indicate that inflow from the upper Medina River (originating outside Bexar County) represents about 22 percent of total streamflow. Recycled wastewater discharges account for about 20 percent and base flow (ground-water inflow to streams) about 18 percent. Storm runoff from various land uses represents about 33 percent.
Estimates of sources of streamflow constituent loads indicate recycled wastewater as the largest source of dissolved solids and nitrate plus nitrite nitrogen (about 38 and 66 percent, respectively, of the total loads) during 1997–2001. Stormwater runoff from urban land produced about 49 percent of the 1997–2001 total suspended solids load. Stormwater runoff from residential and commercial land (about 23 percent of the land area) produced about 70 percent of the total lead streamflow load during 1997–2001.
","language":"English","publisher":"U.S. Geological Survey ","doi":"10.3133/wri034030","collaboration":"In cooperation with the San Antonio Water System ","usgsCitation":"Ockerman, D.J., and McNamara, K.C., 2003, Simulation of streamflow and estimation of streamflow constituent loads in the San Antonio River watershed, Bexar County, Texas, 1997-2001: U.S. Geological Survey Water-Resources Investigations Report 2003-4030, HTML Document; Report: iv, 37 p., https://doi.org/10.3133/wri034030.","productDescription":"HTML Document; Report: iv, 37 p.","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":4534,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri03-4030/","linkFileType":{"id":5,"text":"html"}},{"id":178769,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":335481,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wri/wri03-4030/pdf/wri03-4030.pdf","text":"Report","size":"19.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Texas","county":"Bexar County","otherGeospatial":"San Antonio River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.50616455078125,\n 29.739339757443286\n ],\n [\n -98.58993530273438,\n 29.736954896290666\n ],\n [\n -98.72451782226562,\n 29.71548859443817\n ],\n [\n -98.778076171875,\n 29.67015577117534\n ],\n [\n -98.82476806640625,\n 29.621221113784504\n ],\n [\n -98.865966796875,\n 29.554345125748267\n ],\n [\n -98.88656616210938,\n 29.434813598289637\n ],\n [\n -98.87969970703125,\n 29.388158098102554\n ],\n [\n -98.86184692382812,\n 29.334298230315675\n ],\n [\n -98.83438110351562,\n 29.26124274448168\n ],\n [\n -98.77944946289062,\n 29.216904948184734\n ],\n [\n -98.734130859375,\n 29.178543264303006\n ],\n [\n -98.64349365234374,\n 29.156958511360703\n ],\n [\n -98.5693359375,\n 29.159357041355424\n ],\n [\n -98.46084594726562,\n 29.185737173254434\n ],\n [\n -98.36196899414061,\n 29.204918463909035\n ],\n [\n -98.31939697265625,\n 29.263638834879824\n ],\n [\n -98.28231811523438,\n 29.3642238956322\n ],\n [\n -98.3056640625,\n 29.44438130948883\n ],\n [\n -98.2891845703125,\n 29.534034720259523\n ],\n [\n -98.34686279296874,\n 29.62360872200976\n ],\n [\n -98.3990478515625,\n 29.682087444299334\n ],\n [\n -98.45947265625,\n 29.71071768156533\n ],\n [\n -98.50616455078125,\n 29.739339757443286\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b05e4b07f02db699e32","contributors":{"authors":[{"text":"Ockerman, Darwin J. 0000-0003-1958-1688 ockerman@usgs.gov","orcid":"https://orcid.org/0000-0003-1958-1688","contributorId":1579,"corporation":false,"usgs":true,"family":"Ockerman","given":"Darwin","email":"ockerman@usgs.gov","middleInitial":"J.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":244591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McNamara, Kenna C.","contributorId":51841,"corporation":false,"usgs":true,"family":"McNamara","given":"Kenna","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":244592,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":52915,"text":"wri034115 - 2003 - Patterns and sources of fecal coliform bacteria in three streams in Virginia, 1999-2000","interactions":[],"lastModifiedDate":"2012-02-02T00:11:45","indexId":"wri034115","displayToPublicDate":"2003-09-01T00:00:00","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":"2003-4115","title":"Patterns and sources of fecal coliform bacteria in three streams in Virginia, 1999-2000","docAbstract":"Surface-water impairment by fecal coliform bacteria is a water-quality issue of national scope and importance.\r\nIn Virginia, more than 175 stream segments are on the Commonwealth's 1998 303(d) list of impaired waters\r\nbecause of elevated concentrations of fecal coliform bacteria. These fecal coliform-impaired stream segments\r\nrequire the development of total maximum daily load (TMDL) and associated implementation plans, but accurate\r\ninformation on the sources contributing these bacteria usually is lacking. The development of defendable fecal\r\ncoliform TMDLs and management plans can benefit from reliable information on the bacteria sources that are\r\nresponsible for the impairment. Bacterial source tracking (BST) recently has emerged as a powerful tool for\r\nidentifying the sources of fecal coliform bacteria that impair surface waters. In a demonstration of BST\r\ntechnology, three watersheds on Virginia's 1998 303(d) list with diverse land-use practices (and potentially\r\ndiverse bacteria sources) were studied. Accotink Creek is dominated by urban land uses, Christians Creek by\r\nagricultural land uses, and Blacks Run is affected by both urban and agricultural land uses. During the 20-month\r\nfield study (March 1999?October 2000), water samples were collected from each stream during a range of flow\r\nconditions and seasons. For each sample, specific conductance, dissolved oxygen concentration, pH, turbidity,\r\nflow, and water temperature were measured. Fecal coliform concentrations of each water sample were determined\r\nusing the membrane filtration technique. Next, Escherichia coli (E. coli) were isolated from the fecal coliform\r\nbacteria and their sources were identified using ribotyping (a method of 'genetic fingerprinting'). \r\n\r\nStudy results provide enhanced understanding of the concentrations and sources of fecal coliform bacteria in\r\nthese three watersheds. Continuum sampling (sampling along the length of the streams) indicated that elevated\r\nconcentrations of fecal coliform bacteria (maximum observed concentration of 290,000 colonies/100 milliliters\r\n(col/100mL) could occur along the entire length of each stream, and that the samples collected at the downstream\r\nmonitoring station of each stream were generally representative of the entire upstream reach. Seasonal patterns\r\nwere observed in the base-flow fecal coliform concentrations of all streams; concentrations were typically highest\r\nin the summer and lowest in the winter. Fecal coliform concentrations were lowest during periods of base flow\r\n(typically 200?2,000 col/100mL) and increased by 3?4 orders of magnitude during storm events\r\n(as high as 700,000 col/100mL). Multiple linear regression models were developed to predict fecal coliform\r\nconcentrations as a function of streamflow and other water-quality parameters. The source tracking technique\r\nprovided identification of bacteria contributions from diverse sources that included (but were not limited to) humans,\r\ncattle, poultry, horses, dogs, cats, geese, ducks, raccoons, and deer. Seasonal patterns were observed in the\r\ncontributions of cattle and poultry sources. There were relations between the identified sources of fecal coliform\r\nbacteria and the land-use practices within each watershed. There were only minor differences in the distribution of\r\nbacteria sources between low-flow periods and high-flow periods. A coupled approach that utilized both a large\r\navailable source library and a smaller, location-specific source library provided the most success in identifying the\r\nunknown E. coli isolates. BST data should provide valuable support and guidance for producing more defendable and\r\nscientifically rigorous watershed models. Incorporation of these bacteria-source data into watershed management\r\nstrategies also should result in the selection of more efficient source-reduction scenarios for improving water quality.","language":"ENGLISH","doi":"10.3133/wri034115","usgsCitation":"Hyer, K., and Moyer, D., 2003, Patterns and sources of fecal coliform bacteria in three streams in Virginia, 1999-2000: U.S. Geological Survey Water-Resources Investigations Report 2003-4115, v, 76 p. : ill., maps. (some col.) ; 28 cm., https://doi.org/10.3133/wri034115.","productDescription":"v, 76 p. : ill., maps. (some col.) ; 28 cm.","costCenters":[],"links":[{"id":174057,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":5005,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/wri/wri034115/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b32e4b07f02db6b48f1","contributors":{"authors":[{"text":"Hyer, Kenneth kenhyer@usgs.gov","contributorId":2701,"corporation":false,"usgs":true,"family":"Hyer","given":"Kenneth","email":"kenhyer@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":246219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyer, Douglas 0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":2670,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":246218,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":51970,"text":"wri034077 - 2003 - Simulation of hydrodynamics, temperature, and dissolved oxygen in Bull Shoals Lake, Arkansas, 1994-1995","interactions":[],"lastModifiedDate":"2013-08-12T12:02:40","indexId":"wri034077","displayToPublicDate":"2003-09-01T00:00:00","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":"2003-4077","title":"Simulation of hydrodynamics, temperature, and dissolved oxygen in Bull Shoals Lake, Arkansas, 1994-1995","docAbstract":"Outflow from Bull Shoals Lake and other White River reservoirs supports a cold-water trout fishery of substantial economic yield in north-central Arkansas and south-central Missouri. The Arkansas Game and Fish Commission has requested an increase in existing minimum flows through the Bull Shoals Lake dam to increase the amount of fishable waters downstream. Information is needed to assess the impact of increased minimum flows on temperature and dissolved-oxygen concentrations of reservoir water and the outflow.A two-dimensional, laterally averaged, hydrodynamic,\ntemperature, and dissolved-oxygen model was developed and calibrated for Bull Shoals Lake, located on the Arkansas-Missouri State line. The model simulates\nwater-surface elevation, heat transport, and dissolved-\noxygen dynamics. The model was developed to assess the impacts of proposed increases in minimum flow from 4.6 cubic meters per second (the existing minimum flow) to 22.6 cubic meters per second (the increased minimum flow). Simulations included assessing the impact of (1) increased minimum flows and (2) increased minimum flows with increased initial water-surface elevation of 1.5 meters in Bull Shoals Lake on outflow temperatures and dissolved-oxygen concentrations.The increased minimum flow simulation (without\nincreasing initial water-surface elevation) increased the water temperature and dissolved-oxygen concentration\nin the outflow. Conversely, the increased minimum\nflow and increased initial water-surface elevation (1.5 meters) simulation decreased outflow water temperature\nand dissolved-oxygen concentration through time. However, results from both scenarios for water temperature and dissolved-oxygen concentration were within the boundaries of the error between measured and simulated water column profile values.","language":"ENGLISH","doi":"10.3133/wri034077","usgsCitation":"Galloway, J.M., and Green, W.R., 2003, Simulation of hydrodynamics, temperature, and dissolved oxygen in Bull Shoals Lake, Arkansas, 1994-1995: U.S. Geological Survey Water-Resources Investigations Report 2003-4077, v, 24 p. : ill., maps ; 28 cm., https://doi.org/10.3133/wri034077.","productDescription":"v, 24 p. : ill., maps ; 28 cm.","costCenters":[],"links":[{"id":4532,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.water.usgs.gov/wri034077/","linkFileType":{"id":5,"text":"html"}},{"id":179535,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/wri/2003/4077/report-thumb.jpg"},{"id":276439,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wri/2003/4077/report.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b05e4b07f02db699fc3","contributors":{"authors":[{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":244577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, W. Reed","contributorId":87886,"corporation":false,"usgs":true,"family":"Green","given":"W.","email":"","middleInitial":"Reed","affiliations":[],"preferred":false,"id":244578,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":51983,"text":"wri034031 - 2003 - Simulation of streamflow and water quality in the White Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98","interactions":[],"lastModifiedDate":"2018-02-26T15:35:46","indexId":"wri034031","displayToPublicDate":"2003-09-01T00:00:00","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":"2003-4031","title":"Simulation of streamflow and water quality in the White Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98","docAbstract":"The Christina River Basin drains 565 square miles (mi2) in Pennsylvania, Maryland, and Delaware. Water from the basin is used for recreation, drinking water supply, and to support aquatic life. The Christina River Basin includes the major subbasins of Brandywine Creek, White Clay Creek, and Red Clay Creek. The White Clay Creek is the second largest of the subbasins and drains an area of 108 mi2. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the streams. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in non point-source evaluation, four independent models, one for each of the three major subbasins and for the Christina River, were developed and calibrated using the model code Hydrological Simulation Program—Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in smaller subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base- flow samples were collected during 1998 at two sites in the White Clay Creek subbasin and at nine sites in the other subbasins.
The HSPF model for the White Clay Creek Basin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into 17 reaches draining areas that ranged from 1.37 to 13 mi2. Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the White Clay Creek Basin are agricultural, forested, residential, and urban.
The hydrologic component of the model was run at an hourly time step and primarily calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Additional calibration was done using data from two other USGS streamflow-measurement stations with periods of record shorter than the calibration period. Daily precipitation data from two National Oceanic and Atmospheric Administration (NOAA) gages and hourly precipitation and other meteorological data for one NOAA gage were used for model input. The difference between simulated and observed streamflow volume ranged from -0.9 to 1.8 percent for the 4-year period at the two calibration sites with 4-year records. Annual differences between observed and simulated streamflow generally were greater than the overall error. For example, at a site near the bottom of the basin (drainage area of 89.1 mi2), annual differences between observed and simulated streamflow ranged from -5.8 to 14.4 percent and the overall error for the 4-year period was -0.9 percent. Calibration errors for 36 storm periods at the two calibration sites for total volume, low-flowrecession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the hourly rainfall data.
The water-quality component of the model was calibrated using data collected by the USGS and state agencies at three USGS streamflow-measurement stations with variable water-quality monitoring periods ending October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended solids may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for up to five storms in 1998 at each of the two nonpoint-source monitoring sites in the White Clay Creek Basin indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual events. The accuracy of the water-quality calibration under stormflow conditions is limited by the relatively small amount of water-quality data available for the White Clay Creek Basin.
Users of the White Clay Creek HSPF model should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow and water quality for individual storm events may not be well simulated, but the model performance is reasonable when evaluated over longer periods of time; the observed flow-duration curve for the simulation period is similar to the long-term flow-duration curve at White Clay Creek near Newark, Del., indicating that the calibration period is representative of all but highest 0.1 percent and lowest 0.1 percent of flows at that site; relative errors in streamflow and water-quality simulations are greater for smaller drainage areas than for larger areas; and calibration for water-quality was based on sparse data.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri034031","collaboration":"Prepared in cooperation with the Delaware River Basin Commission, Delaware Department of Natural Resources and Environmental Control, and the Pennsylvania Department of Environmental Protection","usgsCitation":"Senior, L.A., and Koerkle, E.H., 2003, Simulation of streamflow and water quality in the White Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98: U.S. Geological Survey Water-Resources Investigations Report 2003-4031, x, 142 p., https://doi.org/10.3133/wri034031.","productDescription":"x, 142 p.","onlineOnly":"Y","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":179191,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/wri/2003/4031/coverthb.jpg"},{"id":4538,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/wri/2003/4031/wri20034031.pdf","text":"Report","size":"2.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"WRI 2003-4031"}],"contact":"Director, Pennsylvania Water Science Center U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
This report describes a two-dimensional regional screening model and two associated three-dimensional ground-water flow models that were developed to simulate the ground-water flow systems in La Crosse County, Wisconsin, and Pool 8 of the Mississippi River. Although the geographic extents of the three-dimensional models were slightly different, both were derived from the same geologic interpretation and regional screening model, and their calibrations were performed concurrently. The objectives of the La Crosse County (LCC) model were to assess the effects of recent (1990s) and potential future ground-water withdrawals and to provide a tool suitable to evaluate the effects of proposed water-management programs. The Pool 8 model objectives were to quantify the magnitude and distribution of ground-water flow into the Pool. The Wisconsin Geological and Natural History Survey and the U.S. Geological Survey developed the models cooperatively. The report describes: 1) the conceptual hydrogeologic model; 2) the methods used in simulating flow; 3) model calibration and sensitivity analysis; and 4) model results, such as simulation of predevelopment conditions and location and magnitude of ground-water discharge into Pool 8 of the Mississippi.
\nThree aquifer units underlie the model area: 1) a shallow unconsolidated sand and gravel aquifer; 2) an upper bedrock aquifer, composed of Cambrian and Ordovician sandstone and dolomite; and 3) a lower bedrock aquifer composed of Cambrian sandstone of the Eau Claire Formation and the Mount Simon Formation. A shale layer that is part of the Eau Claire Formation forms a confining unit separating the upper and lower bedrock aquifers. This confining unit is absent in the Black River and parts of the La Crosse and Mississippi River valleys. Precambrian crystalline basement rock forms the lower base of the ground-water flow system.
\nThe U.S. Geological Survey ground-water flow model code, MODFLOW, was used to develop the La Crosse County (LCC) and Pool 8 ground-water flow models. Boundary conditions for the MODFLOW model were extracted from an analytic element screening model of the regional flow system surrounding La Crosse County. Model input was obtained from previously published and unpublished geologic and hydrologic data. Pumpages from municipal and high-capacity wells were also simulated.
\nModel calibration included a comparison of modeled and field-measured water levels and field-measured base flows to simulated stream flows. At calibration, most measured water levels compared favorably to model-calculated water levels. Simulated streamflows at two targets were within 3 percent of estimated measured base flows. Mass balance results from the LCC and Pool 8 models indicated that 63 to 74 percent of ground water was from recharge and 19 to 26 percent was from surface-water sources. Ground-water flow out of the model was to rivers and streams (85 to 87 percent) and pumping wells (11 and 13 percent).
\nThe model demonstrates the effects of development on ground water in the study area. The maximum simulated water-level decline in the city of La Crosse metropolitan area is 9.3 feet. Simulated stream losses are similar to the amount of ground water pumped by wells. This indicates that ground water withdrawn by La Crosse County wells is water that under predevelopment conditions discharged to streams and lakes.
\nThe models provide estimates of the locations and amount of ground-water flow into Pool 8 and the southern portion of Pool 7 of the Mississippi River. Ground-water discharges into all areas of the pools, except along the eastern shore in the vicinity of the city of La Crosse and immediately downgradient from lock and dam 7 and 8. Ground-water flow into the pools is generally greatest around the perimeter with decreasing amounts away from the perimeter. An area of relatively high ground-water discharge extends out towards the center of Pool 7 from the upper reaches of the pool and may
","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/wri034154","collaboration":"Prepared in cooperation with La Crosse County, Wisconsin Department of Natural Resources, and Wisconsin Geological and Natural History Survey","usgsCitation":"Hunt, R.J., Saad, D.A., and Chapel, D.M., 2003, Numerical simulation of ground-water flow in La Crosse County, Wisconsin, and into nearby pools of the Mississippi River: U.S. Geological Survey Water-Resources Investigations Report 2003-4154, vi, 36 p., https://doi.org/10.3133/wri034154.","productDescription":"vi, 36 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":677,"text":"Wisconsin Water Science 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Crosse\",\"state\":\"WI\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a8fe4b07f02db6554de","contributors":{"authors":[{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":245882,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":245881,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapel, Dawn M.","contributorId":66782,"corporation":false,"usgs":true,"family":"Chapel","given":"Dawn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":245883,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197198,"text":"70197198 - 2003 - Typing mineral deposits using their grades and tonnages in an artificial neural network","interactions":[],"lastModifiedDate":"2018-05-21T16:47:23","indexId":"70197198","displayToPublicDate":"2003-09-01T00:00:00","publicationYear":"2003","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Typing mineral deposits using their grades and tonnages in an artificial neural network","docAbstract":"A test of the ability of a probabilistic neural network to classify deposits into types on the basis of deposit tonnage and average Cu, Mo, Ag, Au, Zn, and Pb grades is conducted. The purpose is to examine whether this type of system might serve as a basis for integrating geoscience information available in large mineral databases to classify sites by deposit type. Benefits of proper classification of many sites in large regions are relatively rapid identification of terranes permissive for deposit types and recognition of specific sites perhaps worthy of exploring further.
Total tonnages and average grades of 1,137 well-explored deposits identified in published grade and tonnage models representing 13 deposit types were used to train and test the network. Tonnages were transformed by logarithms and grades by square roots to reduce effects of skewness. All values were scaled by subtracting the variable's mean and dividing by its standard deviation. Half of the deposits were selected randomly to be used in training the probabilistic neural network and the other half were used for independent testing. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class (type) and each variable (grade or tonnage).
Deposit types were selected to challenge the neural network. For many types, tonnages or average grades are significantly different from other types, but individual deposits may plot in the grade and tonnage space of more than one type. Porphyry Cu, porphyry Cu-Au, and porphyry Cu-Mo types have similar tonnages and relatively small differences in grades. Redbed Cu deposits typically have tonnages that could be confused with porphyry Cu deposits, also contain Cu and, in some situations, Ag. Cyprus and kuroko massive sulfide types have about the same tonnages. Cu, Zn, Ag, and Au grades. Polymetallic vein, sedimentary exhalative Zn-Pb, and Zn-Pb skarn types contain many of the same metals. Sediment-hosted Au, Comstock Au-Ag, and low-sulfide Au-quartz vein types are principally Au deposits with differing amounts of Ag.
Given the intent to test the neural network under the most difficult conditions, an overall 75% agreement between the experts and the neural network is considered excellent. Among the largestclassification errors are skarn Zn-Pb and Cyprus massive sulfide deposits classed by the neuralnetwork as kuroko massive sulfides—24 and 63% error respectively. Other large errors are the classification of 92% of porphyry Cu-Mo as porphyry Cu deposits. Most of the larger classification errors involve 25 or fewer training deposits, suggesting that some errors might be the result of small sample size. About 91% of the gold deposit types were classed properly and 98% of porphyry Cu deposits were classes as some type of porphyry Cu deposit. An experienced economic geologist would not make many of the classification errors that were made by the neural network because the geologic settings of deposits would be used to reduce errors. In a separate test, the probabilistic neural network correctly classed 93% of 336 deposits in eight deposit types when trained with presence or absence of 58 minerals and six generalized rock types. The overall success rate of the probabilistic neural network when trained on tonnage and average grades would probably be more than 90% with additional information on the presence of a few rock types.
","language":"English","publisher":"Springer","doi":"10.1023/A:1025128021384","usgsCitation":"Singer, D.A., and Kouda, R., 2003, Typing mineral deposits using their grades and tonnages in an artificial neural network: Natural Resources Research, v. 12, no. 3, p. 201-208, https://doi.org/10.1023/A:1025128021384.","productDescription":"8 p.","startPage":"201","endPage":"208","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":354374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b158505e4b092d9651e2115","contributors":{"authors":[{"text":"Singer, Donald A. dsinger@usgs.gov","contributorId":5601,"corporation":false,"usgs":true,"family":"Singer","given":"Donald","email":"dsinger@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":735971,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kouda, Ryoichi","contributorId":198036,"corporation":false,"usgs":false,"family":"Kouda","given":"Ryoichi","email":"","affiliations":[],"preferred":false,"id":735972,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}