s the climate changes, drought may reduce tree productivity and survival across many forest ecosystems; however, the relative influence of specific climate parameters on forest decline is poorly understood. We derive a forest drought-stress index (FDSI) for the southwestern United States using a comprehensive tree-ring data set representing AD 1000-2007. The FDSI is approximately equally influenced by the warm-season vapour-pressure deficit (largely controlled by temperature) and cold-season precipitation, together explaining 82% of the FDSI variability. Correspondence between the FDSI and measures of forest productivity, mortality, bark-beetle outbreak and wildfire validate the FDSI as a holistic forest-vigour indicator. If the vapour-pressure deficit continues increasing as projected by climate models, the mean forest drought-stress by the 2050s will exceed that of the most severe droughts in the past 1,000 years. Collectively, the results foreshadow twenty-first-century changes in forest structures and compositions, with transition of forests in the southwestern United States, and perhaps water-limited forests globally, towards distributions unfamiliar to modern civilization.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/nclimate1693","usgsCitation":"Williams, A.P., Allen, C.D., Macalady, A.K., Griffin, D., Woodhouse, C.A., Meko, D.M., Swetnam, T.W., Rauscher, S.A., Seager, R., Grissino-Mayer, H.D., Dean, J.S., Cook, E.R., Gangodagamage, C., Cai, M., and McDowell, N., 2012, Temperature as a potent driver of regional forest drought stress and tree mortality: Nature Climate Change, v. 3, p. 292-297, https://doi.org/10.1038/nclimate1693.","productDescription":"6 p.","startPage":"292","endPage":"297","ipdsId":"IP-040354","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":474335,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7916/d8-b9ec-8z87","text":"External Repository"},{"id":268101,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"3","noUsgsAuthors":false,"publicationDate":"2012-09-30","publicationStatus":"PW","scienceBaseUri":"512b44c5e4b0523e997a81e5","contributors":{"authors":[{"text":"Williams, A. Park","contributorId":200207,"corporation":false,"usgs":false,"family":"Williams","given":"A.","email":"","middleInitial":"Park","affiliations":[{"id":27369,"text":"Lamont-Doherty Earth Observatory at Columbia University","active":true,"usgs":false}],"preferred":false,"id":725628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Craig D. 0000-0002-8777-5989 craig_allen@usgs.gov","orcid":"https://orcid.org/0000-0002-8777-5989","contributorId":2597,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"craig_allen@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":725629,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Macalady, Alison K.","contributorId":69855,"corporation":false,"usgs":true,"family":"Macalady","given":"Alison","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":725630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffin, Daniel","contributorId":69026,"corporation":false,"usgs":true,"family":"Griffin","given":"Daniel","affiliations":[],"preferred":false,"id":725631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":725632,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meko, David M.","contributorId":145887,"corporation":false,"usgs":false,"family":"Meko","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":725633,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Swetnam, Thomas W.","contributorId":191872,"corporation":false,"usgs":false,"family":"Swetnam","given":"Thomas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":725634,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rauscher, Sara A.","contributorId":47653,"corporation":false,"usgs":true,"family":"Rauscher","given":"Sara","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":725635,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Seager, Richard","contributorId":102758,"corporation":false,"usgs":true,"family":"Seager","given":"Richard","email":"","affiliations":[],"preferred":false,"id":725636,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Grissino-Mayer, Henri D.","contributorId":88624,"corporation":false,"usgs":true,"family":"Grissino-Mayer","given":"Henri","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":725637,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dean, Jeffrey S.","contributorId":39258,"corporation":false,"usgs":true,"family":"Dean","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":725638,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cook, Edward R.","contributorId":37611,"corporation":false,"usgs":true,"family":"Cook","given":"Edward","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":725639,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gangodagamage, Chandana","contributorId":60922,"corporation":false,"usgs":true,"family":"Gangodagamage","given":"Chandana","email":"","affiliations":[],"preferred":false,"id":725640,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cai, Michael","contributorId":52848,"corporation":false,"usgs":true,"family":"Cai","given":"Michael","email":"","affiliations":[],"preferred":false,"id":725641,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"McDowell, Nathan G.","contributorId":9176,"corporation":false,"usgs":true,"family":"McDowell","given":"Nathan G.","affiliations":[],"preferred":false,"id":725642,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70255732,"text":"70255732 - 2012 - Role of remote sensing for land-use and land-cover change modeling","interactions":[],"lastModifiedDate":"2024-07-03T13:28:46.614651","indexId":"70255732","displayToPublicDate":"2012-09-30T08:16:40","publicationYear":"2012","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"15","title":"Role of remote sensing for land-use and land-cover change modeling","docAbstract":"As the impacts of land-use and land-cover (LULC) change on carbon dynamics, climate change, hydrology, and biodiversity have been recognized, modeling of this transformational force has become increasingly important. Given the wide variety of applications that rely on the availability of LULC projections, modeling approaches have originated from a variety of disciplines, including geography, landscape ecology, economics, biology, and others. Initial modeling was often isolated within each discipline, but multidisciplinary modeling frameworks were developed as LULC modelers began to integrate the socioeconomic and biophysical components of LULC change. The empirical and theoretical basis for this work falls within land-use science, and this ‡eld documents both land-use and land-cover change, explains the coupled human-environment dynamics that produce the changes, and provides tools for producing spatially explicit LULC models (Mertens and Lambin, 1999; Rindfuss et al., 2004).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Remote sensing of land use and land cover","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/b11964-21","usgsCitation":"Sohl, T., and Sleeter, B., 2012, Role of remote sensing for land-use and land-cover change modeling, chap. 15 of Remote sensing of land use and land cover, p. 225-242, https://doi.org/10.1201/b11964-21.","productDescription":"18 p.","startPage":"225","endPage":"242","ipdsId":"IP-025704","costCenters":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"links":[{"id":430756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sohl, Terry 0000-0002-9771-4231","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":339876,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":905497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":339877,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin M.","affiliations":[],"preferred":true,"id":905498,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70161820,"text":"70161820 - 2012 - To burn or not to burn Oriental bittersweet: A fire manager's conundrum","interactions":[],"lastModifiedDate":"2022-09-02T18:14:16.43111","indexId":"70161820","displayToPublicDate":"2012-09-30T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"To burn or not to burn Oriental bittersweet: A fire manager's conundrum","docAbstract":"Oriental bittersweet (Celastrus orbiculatus) is an introduced liana (woody vine) that has invaded much of the Eastern United States and is expanding west into the Great Plains. In forests, it can girdle and damage canopy trees. At Indiana Dunes, we have discovered that it is invading non-forested dune habitats as well. Anecdotal evidence suggests that fire might facilitate its spread, but the relationship between fire and this aggressive invader is poorly understood. We investigated four areas important to fire management of oriental bittersweet, each of which we will briefly summarize here.
1) What fire temperatures cause seed mortality? For seeds, temperatures above 140°C for three minute or more kills the embryo. For fruits, temperatures above 140°C kill the seeds inside after five minutes. While oriental bittersweet fruits ripen in October and November, the seeds are not dispersed until later in the early to mid December. Thus fall fires will not have any impact on the seeds unless perhaps if they are near the ground. Late winter and early spring fires are likely to kill seeds in the top litter at least. Thus spring fire can reduce the pool of seeds available to germinate.
2) Does fire modify habitat susceptibility to invasion? We found that post fire environment had no effect on the emergence and survival of oriental bittersweet, except that the tallest plants, after two years since sowing, were in the control plots. Highest establishment occurred in mesic silt loam prairie and oak forest. Survival was greatest in mesic prairie and greatest biomass occurred in the oak forest.
3) Both fire and cutting can cause oriental bittersweet to resprout and root sucker. Does the resprouting response differ between these two treatments and can a combination of cutting and pre- or post-fire treatment facilitate its removal? Cutting sometimes increased stem density between one and two times, but burning increased density by two or more times depending on the maximum fire temperature and duration. Cutting in early July reduced total nonstructural carbohydrates by 50% from normal July levels and 75% below dormant season levels. Thus burning established populations will only serve to increase their local density.
4) How does oriental bittersweet abundance vary with fire regime and can we predict the abundance of this species in a fire mosaic landscape based on fire return interval and time since last fire? At the landscape scale, we can predict the presence and abundance of oriental bittersweet, but have less success predicting its cover and distribution. The presence of oriental bittersweet was significantly negatively influenced by canopy closure, burn frequency, and distance to roads and railroads. In plots where C. orbiculatus was present, abundance was significantly greater in plots with low to moderate burn frequency, and marginally (p = 0.056) lower in plots with greater canopy cover. Both cover and distribution of C. orbiculatus was not significantly affected by the measured variables. These results suggest the frequent fire may be effective in preventing the establishment of oriental bittersweet.
","language":"English","publisher":"Joint fire Science Program","usgsCitation":"Leicht-Young, S.A., Pavlovic, N.B., Grundel, R., Weyenberg, S.A., and Mulconrey, N., 2012, To burn or not to burn Oriental bittersweet: A fire manager's conundrum, 18 p.","productDescription":"18 p.","ipdsId":"IP-017053","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":336287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":406145,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://www.firescience.gov/projects/08-1-2-10/project/08-1-2-10_final_report.pdf"}],"country":"United States","state":"Indiana","otherGeospatial":"Indiana Dunes National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.3248291015625,\n 41.62493858776385\n ],\n [\n -87.32585906982422,\n 41.60414765107674\n ],\n [\n -87.30113983154297,\n 41.59464832679814\n ],\n [\n -87.2647476196289,\n 41.5969591019372\n ],\n [\n -87.26337432861328,\n 41.57667280728488\n ],\n [\n -87.22732543945312,\n 41.58771550500517\n ],\n [\n -87.21324920654295,\n 41.59644560350027\n ],\n [\n -87.1826934814453,\n 41.601837133324395\n ],\n [\n -87.1490478515625,\n 41.601323673699724\n ],\n [\n -87.12982177734375,\n 41.60722821271717\n ],\n [\n -87.10750579833984,\n 41.60722821271717\n ],\n [\n -87.09514617919922,\n 41.61364557706411\n ],\n [\n -87.08553314208984,\n 41.624681950391306\n ],\n [\n -87.05463409423828,\n 41.63622962087031\n ],\n [\n -87.0347213745117,\n 41.63648621227501\n ],\n [\n -86.95026397705078,\n 41.68701479467118\n ],\n [\n -86.92245483398438,\n 41.69983299902508\n ],\n [\n -86.92279815673828,\n 41.70752269548981\n ],\n [\n -86.91181182861328,\n 41.713930073371294\n ],\n [\n -86.91387176513672,\n 41.72084932414073\n ],\n [\n -86.9296646118164,\n 41.71239236092484\n ],\n [\n -86.94854736328124,\n 41.70752269548981\n ],\n [\n -87.06905364990234,\n 41.66393558993828\n ],\n [\n -87.12158203125,\n 41.649057939704974\n ],\n [\n -87.12947845458984,\n 41.64957101933682\n ],\n [\n -87.13119506835938,\n 41.65162329700054\n ],\n [\n -87.14115142822266,\n 41.64777522274766\n ],\n [\n -87.14733123779297,\n 41.64828831259533\n ],\n [\n -87.1490478515625,\n 41.64290066539074\n ],\n [\n -87.1596908569336,\n 41.64264409952472\n ],\n [\n -87.1603775024414,\n 41.63648621227501\n ],\n [\n -87.176513671875,\n 41.63597302844412\n ],\n [\n -87.17823028564453,\n 41.63263723394463\n ],\n [\n -87.22320556640625,\n 41.6262217593042\n ],\n [\n -87.25273132324217,\n 41.62288546018218\n ],\n [\n -87.28809356689453,\n 41.62237216807198\n ],\n [\n -87.28878021240234,\n 41.6244253119973\n ],\n [\n -87.29839324951172,\n 41.62493858776385\n ],\n [\n -87.3248291015625,\n 41.62493858776385\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b548c4e4b01ccd54fddfe0","contributors":{"authors":[{"text":"Leicht-Young, Stacey A.","contributorId":80506,"corporation":false,"usgs":false,"family":"Leicht-Young","given":"Stacey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":587852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pavlovic, Noel B. 0000-0002-2335-2274 npavlovic@usgs.gov","orcid":"https://orcid.org/0000-0002-2335-2274","contributorId":1976,"corporation":false,"usgs":true,"family":"Pavlovic","given":"Noel","email":"npavlovic@usgs.gov","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":587854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":587855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weyenberg, Scott A.","contributorId":139026,"corporation":false,"usgs":false,"family":"Weyenberg","given":"Scott","email":"","middleInitial":"A.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":587856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mulconrey, Neal","contributorId":152092,"corporation":false,"usgs":false,"family":"Mulconrey","given":"Neal","email":"","affiliations":[{"id":18866,"text":"Indiana Dunes National Lakeshore","active":true,"usgs":false}],"preferred":false,"id":587857,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70040069,"text":"70040069 - 2012 - Relations between retired agricultural land, water quality, and aquatic-community health, Minnesota River Basin","interactions":[],"lastModifiedDate":"2012-10-03T17:16:15","indexId":"70040069","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","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":"Relations between retired agricultural land, water quality, and aquatic-community health, Minnesota River Basin","docAbstract":"The relative importance of agricultural land retirement on water quality and aquatic-community health was investigated in the Minnesota River Basin. Eighty-two sites, with drainage areas ranging from 4.3 to 2200 km2, were examined for nutrient concentrations, measures of aquatic-community health (e.g., fish index of biotic integrity [IBI] scores), and environmental factors (e.g., drainage area and amount of agricultural land retirement). The relation of proximity of agricultural land retirement to the stream was determined by calculating the land retirement percent in various riparian zones. Spearman's rho results indicated that IBI score was not correlated to the percentage of agricultural land retirement at the basin scale (p = 0.070); however, IBI score was correlated to retired land percentage in the 50- to 400-m riparian zones surrounding the streams (p < 0.05), indicating that riparian agricultural land retirement may have more influence on aquatic-community health than does agricultural land retirement in upland areas. Multivariate analysis of covariance and analysis of covariance models indicated that other environmental factors (such as drainage area and lacustrine and palustrine features) commonly were correlated to aquatic-community health measures, as were in-stream factors (standard deviation of water depth and substrate type). These results indicate that although agricultural land retirement is significantly related to fish communities as measured by the IBI scores, a combination of basin, riparian, and in-stream factors act together to influence IBI scores.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Environmental Quality","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Agronomy","publisherLocation":"Madison, WI","doi":"10.2134/jeq2011.0468","usgsCitation":"Christensen, V.G., Lee, K., McLees, J.M., and Niemela, S.L., 2012, Relations between retired agricultural land, water quality, and aquatic-community health, Minnesota River Basin: Journal of Environmental Quality, v. 41, no. 5, p. 1459-1472, https://doi.org/10.2134/jeq2011.0468.","productDescription":"14 p.","startPage":"1459","endPage":"1472","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":262169,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262161,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2134/jeq2011.0468"}],"country":"United States","state":"Minnesota","otherGeospatial":"Minnesota River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.9,43.5 ], [ -96.9,46.25 ], [ -93.1,46.25 ], [ -93.1,43.5 ], [ -96.9,43.5 ] ] ] } } ] }","volume":"41","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50662514e4b053bff18e1c07","contributors":{"authors":[{"text":"Christensen, Victoria G. 0000-0003-4166-7461 vglenn@usgs.gov","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":2354,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","email":"vglenn@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":467668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Kathy 0000-0002-7683-1367 klee@usgs.gov","orcid":"https://orcid.org/0000-0002-7683-1367","contributorId":2538,"corporation":false,"usgs":true,"family":"Lee","given":"Kathy","email":"klee@usgs.gov","affiliations":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":467669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLees, James M.","contributorId":21406,"corporation":false,"usgs":true,"family":"McLees","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":467670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niemela, Scott L.","contributorId":64936,"corporation":false,"usgs":true,"family":"Niemela","given":"Scott","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":467671,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173582,"text":"70173582 - 2012 - Semi-discrete biomass dynamic modeling: an improved approach for assessing fish stock responses to pulsed harvest events","interactions":[],"lastModifiedDate":"2016-06-09T16:00:13","indexId":"70173582","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Semi-discrete biomass dynamic modeling: an improved approach for assessing fish stock responses to pulsed harvest events","docAbstract":"Continuous harvest over an annual period is a common assumption of continuous biomass dynamics models (CBDMs); however, fish are frequently harvested in a discrete manner. We developed semidiscrete biomass dynamics models (SDBDMs) that allow discrete harvest events and evaluated differences between CBDMs and SDBDMs using an equilibrium yield analysis with varying levels of fishing mortality (F). Equilibrium fishery yields for CBDMs and SDBDMS were similar at low fishing mortalities and diverged as F approached and exceeded maximum sustained yield (FMSY). Discrete harvest resulted in lower equilibrium yields at high levels of Frelative to continuous harvest. The effect of applying harvest continuously when it was in fact discrete was evaluated by fitting CBDMs and SDBDMs to time series data generated from a hypothetical fish stock undergoing discrete harvest and evaluating parameter estimates bias. Violating the assumption of continuous harvest resulted in biased parameter estimates for CBDM while SDBDM parameter estimates were unbiased. Biased parameter estimates resulted in biased biological reference points derived from CBDMs. Semidiscrete BDMs outperformed continuous BDMs and should be used when harvest is discrete, when the time and magnitude of harvest are known, and when F is greater than FMSY.
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This is especially true in karst terrains, where infiltrating surface water can rapidly affect groundwater quality. We analyzed the evolution of groundwater compositions (major ions and Sr isotopes) during the transition from extreme drought to wetconditions, and used inverse geochemical modeling (PHREEQC) to constrain controls on groundwater compositions during this evolution. Spring water and groundwater from two wells dominantly receiving diffuse and conduit flow (termed diffuse site and conduit site, respectively) in the Barton Springs segment of the Edwards aquifer (central Texas, USA) and surface water from losing streams that recharge the aquifer were sampled every 3–4 weeks during November 2008–March 2010. During this period, water compositions at the spring and conduit sites changed rapidly but there was no change at the diffuse site, illustrating the dual nature (i.e., diffuse vs. conduit) of flow in this karst system. Geochemical modeling demonstrated that, within a month of the onset of wetconditions, the majority of spring water and groundwater at the conduit site was composed of surface water, providing quantitative information on the timing and magnitude of the vulnerability of groundwater to potential surface-water contamination. The temporal pattern of increasing spring discharge and changing pattern of covariation between spring discharge and surface-water (steam) recharge indicates that that there were two modes of aquifer response—one with a small amount of storage and a second that accommodates more storage.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Hydrology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.jhydrol.2012.08.030","usgsCitation":"Wong, C., Mahler, B., Musgrove, M., and Banner, J., 2012, Changes in sources and storage in a karst aquifer during a transition from drought to wet conditions: Journal of Hydrology, v. 468-469, p. 159-172, https://doi.org/10.1016/j.jhydrol.2012.08.030.","productDescription":"14 p.","startPage":"159","endPage":"172","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":262168,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262165,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jhydrol.2012.08.030"}],"country":"United States","state":"Texas","volume":"468-469","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5066250ce4b053bff18e1be3","contributors":{"authors":[{"text":"Wong, C.I.","contributorId":98574,"corporation":false,"usgs":true,"family":"Wong","given":"C.I.","email":"","affiliations":[],"preferred":false,"id":467729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahler, B.J.","contributorId":36888,"corporation":false,"usgs":true,"family":"Mahler","given":"B.J.","email":"","affiliations":[],"preferred":false,"id":467726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musgrove, M.","contributorId":78933,"corporation":false,"usgs":true,"family":"Musgrove","given":"M.","email":"","affiliations":[],"preferred":false,"id":467727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banner, J.L.","contributorId":95683,"corporation":false,"usgs":true,"family":"Banner","given":"J.L.","email":"","affiliations":[],"preferred":false,"id":467728,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70040105,"text":"ofr20121213 - 2012 - Toxicity, sublethal effects, and potential modes of action of select fungicides on freshwater fish and invertebrates","interactions":[],"lastModifiedDate":"2024-03-04T18:34:47.127959","indexId":"ofr20121213","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","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":"2012-1213","title":"Toxicity, sublethal effects, and potential modes of action of select fungicides on freshwater fish and invertebrates","docAbstract":"Despite decades of agricultural and urban use of fungicides and widespread detection of these pesticides in surface waters, relatively few data are available on the effects of fungicides on fish and invertebrates in the aquatic environment. Nine fungicides are reviewed in this report: azoxystrobin, boscalid, chlorothalonil, fludioxonil, myclobutanil, fenarimol, pyraclostrobin, pyrimethanil, and zoxamide. These fungicides were identified as emerging chemicals of concern because of their high or increasing global use rates, detection frequency in surface waters, or likely persistence in the environment. A review of the literature revealed significant sublethal effects of fungicides on fish, aquatic invertebrates, and ecosystems, including zooplankton and fish reproduction, fish immune function, zooplankton community composition, metabolic enzymes, and ecosystem processes, such as leaf decomposition in streams, among other biological effects. Some of these effects can occur at fungicide concentrations well below single-species acute lethality values (48- or 96-hour concentration that effects a response in 50 percent of the organisms, that is, effective concentration killing 50 percent of the organisms in 48 or 96 hours) and chronic sublethal values (for example, 21-day no observed adverse effects concentration), indicating that single-species toxicity values may dramatically underestimate the toxic potency of some fungicides. Fungicide modes of toxic action in fungi can sometimes reflect the biochemical and (or) physiological effects of fungicides observed in vertebrates and invertebrates; however, far more studies are needed to explore the potential to predict effects in nontarget organisms based on specific fungicide modes of toxic action. Fungicides can also have additive and (or) synergistic effects when used with other fungicides and insecticides, highlighting the need to study pesticide mixtures that occur in surface waters. For fungicides that partition to organic matter in sediment and soils, it is particularly important to determine their effects on freshwater mussels and other freshwater benthic invertebrates in contact with sediments, as available toxicity studies with pelagic species, mainly Daphnia magna, may not be representative of these benthic organisms. Finally, there is a critical need for studies of the chronic effects of fungicides on reproduction, immunocompetence, and ecosystem function; sublethal endpoints with population and community-level relevance.
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However, the relationship between biodiversity and multifunctionality has never been assessed globally in natural ecosystems. We report here on a global empirical study relating plant species richness and abiotic factors to multifunctionality in drylands, which collectively cover 41% of Earth's land surface and support over 38% of the human population. Multifunctionality was positively and significantly related to species richness. The best-fitting models accounted for over 55% of the variation in multifunctionality and always included species richness as a predictor variable. Our results suggest that the preservation of plant biodiversity is crucial to buffer negative effects of climate change and desertification in drylands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Science","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AAAS","publisherLocation":"Washington, D.C.","doi":"10.1126/science.1215442","usgsCitation":"Maestre, F.T., Quero, J.L., Gotelli, N., Escudero, A., Ochoa, V., Delgado-Baquerizo, M., Garcia-Gomez, M., Bowker, M.A., Soliveres, S., Escolar, C., Garcia-Palacios, P., Berdugo, M., Valencia, E., Gozalo, B., Gallardo, A., Aguilera, L., Arredondo, T., Blones, J., Boeken, B., Bran, D., Conceicao, A.A., Cabrera, O., Chaieb, M., Derak, M., Eldridge, D., Espinosa, C.I., Florentino, A., Gaitan, J., Gatica, M.G., Ghiloufi, W., Gomez-Gonzalez, S., Gutie, J.R., Hernandez, R., Huang, X., Huber-Sannwald, E., Jankju, M., Miriti, M., Monerris, J., Mau, R.L., Morici, E., Naseri, K., Ospina, A., 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Cristian","contributorId":83786,"corporation":false,"usgs":true,"family":"Torres-Diaz","given":"Cristian","email":"","affiliations":[],"preferred":false,"id":467627,"contributorType":{"id":1,"text":"Authors"},"rank":49},{"text":"Val, James","contributorId":23007,"corporation":false,"usgs":true,"family":"Val","given":"James","email":"","affiliations":[],"preferred":false,"id":467599,"contributorType":{"id":1,"text":"Authors"},"rank":50},{"text":"Veiga, Jose P.","contributorId":86217,"corporation":false,"usgs":true,"family":"Veiga","given":"Jose","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":467629,"contributorType":{"id":1,"text":"Authors"},"rank":51},{"text":"Wang, Deli","contributorId":50774,"corporation":false,"usgs":true,"family":"Wang","given":"Deli","affiliations":[],"preferred":false,"id":467611,"contributorType":{"id":1,"text":"Authors"},"rank":52},{"text":"Zaady, Eli","contributorId":39638,"corporation":false,"usgs":true,"family":"Zaady","given":"Eli","email":"","affiliations":[],"preferred":false,"id":467606,"contributorType":{"id":1,"text":"Authors"},"rank":53}]}} ,{"id":70156717,"text":"70156717 - 2012 - Design and implementation of a structural health monitoring and alerting system for hospital buildings in the United States","interactions":[],"lastModifiedDate":"2021-10-22T14:10:12.140253","indexId":"70156717","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Design and implementation of a structural health monitoring and alerting system for hospital buildings in the United States","docAbstract":"This paper describes the current progress in the development of a structural health monitoring and alerting system to meet the needs of the U.S. Department of Veterans Affairs to monitor hospital buildings instrumented in high and very high seismic hazard regions in the U.S. The system, using the measured vibration data, is primarily designed for post-earthquake condition assessment of the buildings. It has two essential components – sensing and analysis. The sensing component includes all necessary firmware and sensors to measure the response of the building; while the analysis component consists of several data processing modules integrated into an open source software package which compresses a large amount of measured data into useful information to assess the building’s condition before and after an event. The information can be used for a rapid building safety assessment, and to support decisions for necessary repairs, replacements, and other maintenance and rehabilitation measures.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of the 15th World Conference on Earthquake Engineering, Portugal, 2012","conferenceTitle":"15th World Conference on Earthquake Engineering","conferenceDate":"September 24-28, 2012","conferenceLocation":"Lisbon, Portugal","language":"English","publisher":"International Association for Earthquake Engineering","usgsCitation":"Ulusoy, H.S., Kalkan, E., Fletcher, J.P., Friberg, P.A., Leith, W.K., and Banga, K., 2012, Design and implementation of a structural health monitoring and alerting system for hospital buildings in the United States, in Proceedings of the 15th World Conference on Earthquake Engineering, Portugal, 2012, Lisbon, Portugal, September 24-28, 2012, 10 p.","productDescription":"10 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037848","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":307587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":307586,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.iaee.or.jp/publications.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55dee32fe4b0518e354e0805","contributors":{"authors":[{"text":"Ulusoy, Hasan S. hulusoy@usgs.gov","contributorId":5360,"corporation":false,"usgs":true,"family":"Ulusoy","given":"Hasan","email":"hulusoy@usgs.gov","middleInitial":"S.","affiliations":[],"preferred":true,"id":570231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":570232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":570233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friberg, Paul A. 0000-0002-6914-3849","orcid":"https://orcid.org/0000-0002-6914-3849","contributorId":147087,"corporation":false,"usgs":false,"family":"Friberg","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":570234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leith, W. K.","contributorId":147088,"corporation":false,"usgs":false,"family":"Leith","given":"W.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":570235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banga, Krishna","contributorId":33152,"corporation":false,"usgs":true,"family":"Banga","given":"Krishna","email":"","affiliations":[],"preferred":false,"id":570236,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70156799,"text":"70156799 - 2012 - Directivity models produced for the Next Generation Attenuation West 2 (NGA-West 2) project","interactions":[],"lastModifiedDate":"2021-10-22T14:11:46.851003","indexId":"70156799","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Directivity models produced for the Next Generation Attenuation West 2 (NGA-West 2) project","docAbstract":"Five new directivity models are being developed for the NGA-West 2 project. All are based on the NGA-West 2 data base, which is considerably expanded from the original NGA-West data base, containing about 3,000 more records from earthquakes having finite-fault rupture models. All of the new directivity models have parameters based on fault dimension in km, not normalized fault dimension. This feature removes a peculiarity of previous models which made them inappropriate for modeling large magnitude events on long strike-slip faults. Two models are explicitly, and one is implicitly, 'narrowband' models, in which the effect of directivity does not monotonically increase with spectral period but instead peaks at a specific period that is a function of earthquake magnitude. These narrowband models' functional forms are capable of simulating directivity over a wider range of earthquake magnitude than previous models. The functional forms of the five models are presented.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 15th World Conference on Earthquake Engineering, Portugal, 2012","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"15th World Conference on Earthquake Engineering","conferenceDate":"September 24-28, 2012","conferenceLocation":"Lisbon, Portugal","language":"English","publisher":"International Association for Earthquake Engineering","usgsCitation":"Spudich, P.A., Watson-Lamprey, J., Somerville, P.G., Bayless, J., Shahi, S., Baker, J., Rowshandel, B., and Chiou, B., 2012, Directivity models produced for the Next Generation Attenuation West 2 (NGA-West 2) project, in Proceedings of the 15th World Conference on Earthquake Engineering, Portugal, 2012, Lisbon, Portugal, September 24-28, 2012, 9 p.","productDescription":"9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037616","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":307669,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":307668,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.iitk.ac.in/nicee/wcee/fifteenth_conf_purtgal/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55e1862fe4b05561fa206aa9","contributors":{"authors":[{"text":"Spudich, Paul A. 0000-0002-9484-4997 spudich@usgs.gov","orcid":"https://orcid.org/0000-0002-9484-4997","contributorId":2372,"corporation":false,"usgs":true,"family":"Spudich","given":"Paul","email":"spudich@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":570582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson-Lamprey, Jennie","contributorId":15000,"corporation":false,"usgs":false,"family":"Watson-Lamprey","given":"Jennie","email":"","affiliations":[],"preferred":false,"id":570583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Somerville, Paul G.","contributorId":47392,"corporation":false,"usgs":true,"family":"Somerville","given":"Paul","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":570584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bayless, Jeff","contributorId":147164,"corporation":false,"usgs":false,"family":"Bayless","given":"Jeff","email":"","affiliations":[],"preferred":false,"id":570585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shahi, Shrey","contributorId":140542,"corporation":false,"usgs":false,"family":"Shahi","given":"Shrey","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":570586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Jack W.","contributorId":62113,"corporation":false,"usgs":false,"family":"Baker","given":"Jack W.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":570587,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rowshandel, Badie","contributorId":140541,"corporation":false,"usgs":false,"family":"Rowshandel","given":"Badie","email":"","affiliations":[],"preferred":false,"id":570588,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chiou, Brian","contributorId":139219,"corporation":false,"usgs":false,"family":"Chiou","given":"Brian","affiliations":[],"preferred":false,"id":570589,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70040058,"text":"70040058 - 2012 - Toxicity of copper to early-life stage Kootenai River white sturgeon, Columbia River white sturgeon, and rainbow trout","interactions":[],"lastModifiedDate":"2016-12-31T12:24:42","indexId":"70040058","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Toxicity of copper to early-life stage Kootenai River white sturgeon, Columbia River white sturgeon, and rainbow trout","docAbstract":"White sturgeon (Acipenser transmontanus) populations throughout western North America are in decline, likely as a result of overharvest, operation of dams, and agricultural and mineral extraction activities in their watersheds. Recruitment failure may reflect the loss of early-life stage fish in spawning areas of the upper Columbia River, which are contaminated with metals from effluents associated with mineral-extraction activities. Early-life stage white sturgeon (A. transmontanus) from the Columbia River and Kootenai River populations were exposed to copper during 96-h flow-through toxicity tests to determine their sensitivity to the metal. Similar tests were conducted with rainbow trout (RBT [Oncorhynchus mykiss]) to assess the comparative sensitivity of this species as a surrogate for white sturgeon. Exposures were conducted with a water quality pH 8.1-8.3, hardness 81-119 mg/L as CaCO2, and dissolved organic carbon 0.2-0.4 mg/L. At approximately 30 days posthatch (dph), sturgeon were highly sensitive to copper with median lethal concentration (LC50) values ranging from 4.1 to 6.8 μg/L compared with 36.5 μg/L for 30 dph RBT. White sturgeon at 123-167 dph were less sensitive to copper with LC50 values ranging from 103.7 to 268.9 μg/L. RBT trout, however, remained more sensitive to copper at 160 dph with an LC50 value of 30.9 μg/L. The results indicate that high sensitivity to copper in early-life stage white sturgeon may be a factor in recruitment failure occurring in the upper Columbia and Kootenai rivers. When site-specific water-quality criteria were estimated using the biotic ligand model (BLM), derived values were not protective of early-life stage fish, nor were estimates derived by water-hardness adjustment.","language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/s00244-012-9782-3","usgsCitation":"Little, E.E., Calfee, R., and Linder, G., 2012, Toxicity of copper to early-life stage Kootenai River white sturgeon, Columbia River white sturgeon, and rainbow trout: Archives of Environmental Contamination and Toxicology, v. 63, no. 3, p. 400-408, https://doi.org/10.1007/s00244-012-9782-3.","productDescription":"9 p.","startPage":"400","endPage":"408","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":262156,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-08-14","publicationStatus":"PW","scienceBaseUri":"50662515e4b053bff18e1c13","contributors":{"authors":[{"text":"Little, E. E.","contributorId":13187,"corporation":false,"usgs":true,"family":"Little","given":"E.","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":467582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calfee, R.D.","contributorId":85130,"corporation":false,"usgs":true,"family":"Calfee","given":"R.D.","affiliations":[],"preferred":false,"id":467584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Linder, G.","contributorId":43070,"corporation":false,"usgs":true,"family":"Linder","given":"G.","email":"","affiliations":[],"preferred":false,"id":467583,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70040079,"text":"70040079 - 2012 - Electrical anisotropy of gas hydrate-bearing sand reservoirs in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2012-10-03T17:16:15","indexId":"70040079","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Electrical anisotropy of gas hydrate-bearing sand reservoirs in the Gulf of Mexico","docAbstract":"We present new results and interpretations of the electricalanisotropy and reservoir architecture in gashydrate-bearingsands using logging data collected during the Gulf of MexicoGasHydrate Joint Industry Project Leg II. We focus specifically on sandreservoirs in Hole Alaminos Canyon 21 A (AC21-A), Hole Green Canyon 955 H (GC955-H) and Hole Walker Ridge 313 H (WR313-H). Using a new logging-while-drilling directional resistivity tool and a one-dimensional inversion developed by Schlumberger, we resolve the resistivity of the current flowing parallel to the bedding, R| and the resistivity of the current flowing perpendicular to the bedding, R|. We find the sandreservoir in Hole AC21-A to be relatively isotropic, with R| and R| values close to 2 Ω m. In contrast, the gashydrate-bearingsandreservoirs in Holes GC955-H and WR313-H are highly anisotropic. In these reservoirs, R| is between 2 and 30 Ω m, and R| is generally an order of magnitude higher. Using Schlumberger's WebMI models, we were able to replicate multiple resistivity measurements and determine the formation resistivity the gashydrate-bearingsandreservoir in Hole WR313-H. The results showed that gashydrate saturations within a single reservoir unit are highly variable. For example, the sand units in Hole WR313-H contain thin layers (on the order of 10-100 cm) with varying gashydrate saturations between 15 and 95%. Our combined modeling results clearly indicate that the gashydrate-bearingsandreservoirs in Holes GC955-H and WR313-H are highly anisotropic due to varying saturations of gashydrate forming in thin layers within larger sand units.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine and Petroleum Geology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.marpetgeo.2011.09.003","usgsCitation":"Cook, A.E., Anderson, B.I., Rasmus, J., Sun, K., Li, Q., Collett, T.S., and Goldberg, D.S., 2012, Electrical anisotropy of gas hydrate-bearing sand reservoirs in the Gulf of Mexico: Marine and Petroleum Geology, v. 34, no. 1, p. 72-84, https://doi.org/10.1016/j.marpetgeo.2011.09.003.","productDescription":"13 p.","startPage":"72","endPage":"84","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":474342,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7916/d8rf64h0","text":"External Repository"},{"id":262157,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262145,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.marpetgeo.2011.09.003"}],"country":"United States","otherGeospatial":"Gulf Of Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.85,18.166666666666668 ], [ -97.85,30.383333333333333 ], [ -81.03333333333333,30.383333333333333 ], [ -81.03333333333333,18.166666666666668 ], [ -97.85,18.166666666666668 ] ] ] } } ] }","volume":"34","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50662510e4b053bff18e1bf2","contributors":{"authors":[{"text":"Cook, Anne E.","contributorId":84612,"corporation":false,"usgs":true,"family":"Cook","given":"Anne","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":467683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Barbara I.","contributorId":69832,"corporation":false,"usgs":true,"family":"Anderson","given":"Barbara","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":467682,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rasmus, John","contributorId":15451,"corporation":false,"usgs":true,"family":"Rasmus","given":"John","email":"","affiliations":[],"preferred":false,"id":467680,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sun, Keli","contributorId":88993,"corporation":false,"usgs":true,"family":"Sun","given":"Keli","email":"","affiliations":[],"preferred":false,"id":467684,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Qiming","contributorId":36792,"corporation":false,"usgs":true,"family":"Li","given":"Qiming","email":"","affiliations":[],"preferred":false,"id":467681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":467679,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goldberg, David S.","contributorId":96133,"corporation":false,"usgs":true,"family":"Goldberg","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":467685,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70040093,"text":"sir20125202 - 2012 - Estimation of evaporation from open water - A review of selected studies, summary of U.S. Army Corps of Engineers data collection and methods, and evaluation of two methods for estimation of evaporation from five reservoirs in Texas","interactions":[],"lastModifiedDate":"2016-08-08T08:24:42","indexId":"sir20125202","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5202","title":"Estimation of evaporation from open water - A review of selected studies, summary of U.S. Army Corps of Engineers data collection and methods, and evaluation of two methods for estimation of evaporation from five reservoirs in Texas","docAbstract":"Organizations responsible for the management of water resources, such as the U.S. Army Corps of Engineers (USACE), are tasked with estimation of evaporation for water-budgeting and planning purposes. The USACE has historically used Class A pan evaporation data (pan data) to estimate evaporation from reservoirs but many USACE Districts have been experimenting with other techniques for an alternative to collecting pan data. The energy-budget method generally is considered the preferred method for accurate estimation of open-water evaporation from lakes and reservoirs. Complex equations to estimate evaporation, such as the Penman, DeBruin-Keijman, and Priestley-Taylor, perform well when compared with energy-budget method estimates when all of the important energy terms are included in the equations and ideal data are collected. However, sometimes nonideal data are collected and energy terms, such as the change in the amount of stored energy and advected energy, are not included in the equations. When this is done, the corresponding errors in evaporation estimates are not quantifiable. Much simpler methods, such as the Hamon method and a method developed by the U.S. Weather Bureau (USWB) (renamed the National Weather Service in 1970), have been shown to provide reasonable estimates of evaporation when compared to energy-budget method estimates. Data requirements for the Hamon and USWB methods are minimal and sometimes perform well with remotely collected data. The Hamon method requires average daily air temperature, and the USWB method requires daily averages of air temperature, relative humidity, wind speed, and solar radiation. Estimates of annual lake evaporation from pan data are frequently within 20 percent of energy-budget method estimates. Results of evaporation estimates from the Hamon method and the USWB method were compared against historical pan data at five selected reservoirs in Texas (Benbrook Lake, Canyon Lake, Granger Lake, Hords Creek Lake, and Sam Rayburn Lake) to evaluate their performance and to develop coefficients to minimize bias for the purpose of estimating reservoir evaporation with accuracies similar to estimates of evaporation obtained from pan data. The modified Hamon method estimates of reservoir evaporation were similar to estimates of reservoir evaporation from pan data for daily, monthly, and annual time periods. The modified Hamon method estimates of annual reservoir evaporation were always within 20 percent of annual reservoir evaporation from pan data. Unmodified and modified USWB method estimates of annual reservoir evaporation were within 20 percent of annual reservoir evaporation from pan data for about 91 percent of the years compared. Average daily differences between modified USWB method estimates and estimates from pan data as a percentage of the average amount of daily evaporation from pan data were within 20 percent for 98 percent of the months. Without any modification to the USWB method, average daily differences as a percentage of the average amount of daily evaporation from pan data were within 20 percent for 73 percent of the months. Use of the unmodified USWB method is appealing because it means estimates of average daily reservoir evaporation can be made from air temperature, relative humidity, wind speed, and solar radiation data collected from remote weather stations without the need to develop site-specific coefficients from historical pan data. Site-specific coefficients would need to be developed for the modified version of the Hamon method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125202","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Fort Worth District","usgsCitation":"Harwell, G.R., 2012, Estimation of evaporation from open water - A review of selected studies, summary of U.S. Army Corps of Engineers data collection and methods, and evaluation of two methods for estimation of evaporation from five reservoirs in Texas: U.S. Geological Survey Scientific Investigations Report 2012-5202, vii, 96 p., https://doi.org/10.3133/sir20125202.","productDescription":"vii, 96 p.","numberOfPages":"107","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":262142,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5202.gif"},{"id":262140,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5202/pdf/sir2012-5202.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":262139,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5202/","linkFileType":{"id":5,"text":"html"}}],"projection":"Albers Equal Area","datum":"North American Datum of 1983","country":"United States","state":"Texas","city":"Austin, Dallas, Fort Worth, Houston, San Angelo, San Antonio, Waco","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -100.83333333333333,29 ], [ -100.83333333333333,34.333333333333336 ], [ -93.33333333333333,34.333333333333336 ], [ -93.33333333333333,29 ], [ -100.83333333333333,29 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50662510e4b053bff18e1bf5","contributors":{"authors":[{"text":"Harwell, Glenn R. gharwell@usgs.gov","contributorId":3789,"corporation":false,"usgs":true,"family":"Harwell","given":"Glenn","email":"gharwell@usgs.gov","middleInitial":"R.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":467703,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70040086,"text":"ofr20121049 - 2012 - Test drilling and data collection in the Calaveras County portion of the Eastern San Joaquin Groundwater Subbasin, California, December 2009-June 2011","interactions":[],"lastModifiedDate":"2012-09-27T17:16:16","indexId":"ofr20121049","displayToPublicDate":"2012-09-27T00:00:00","publicationYear":"2012","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":"2012-1049","title":"Test drilling and data collection in the Calaveras County portion of the Eastern San Joaquin Groundwater Subbasin, California, December 2009-June 2011","docAbstract":"Two multiple-well monitoring sites were drilled in the Calaveras County portion of the Eastern San Joaquin Groundwater Subbasin, about 100 miles east of San Francisco, California, during December 2009 and January 2010. Site 3N/9E-12G1-4 was drilled to a depth of 503 feet below land surface (bls), and four wells were installed. Site 4N/9E-36A1-3 was drilled to a depth of 400 feet bls, and three wells were installed. Lithologic and geophysical data collected during test drilling indicated the presence of volcanic sands interspersed with lahar deposits that are characteristic of the Mehrten Formation to about 420 feet bls at site 12G1-4, and the presence of volcanic sands interspersed with clay that are characteristic of the Valley Springs Formation at site 36A1-3. In January 2010, water levels at site 12G1-4 ranged from 120 to 127 feet bls (the shallowest well at the site, 12G4, screened from 90 to 110 feet bls, was dry). Between May and November 2010, water levels declined as much as 22 feet in wells 12G1 and 12G2, the deepest wells at this site, and declined about 6 feet in shallower well 12G3. During this same period, water-levels declined less than 8 feet in the three wells at site 36A1-3. Water levels in all monitoring wells recovered to near-May-2010 levels by mid-spring 2011. Dissolved solids in the six sampled monitoring wells (residue on evaporation) ranged from 154 to 239 milligrams per liter (mg/L); arsenic concentrations ranged from 1.8 to 13 micrograms per liter (μg/L), and were greater than the U.S. Environmental Protection Agency Maximum Contaminant Level (MCL) for arsenic of 10 μg/L in well 36A2. The oxygen-18 (δ18O) and deuterium (δD) stable-isotopic composition of water from the six monitoring wells and from nine domestic and public-supply wells sampled as part of this study ranged from -6.7 to -8.2 per mil (δ18O), and -50 to -60 per mil (δD), and was consistent with values expected for water recharged in the lower altitudes of the Sierra Nevada. Well 36A3, the shallowest well at site 36A1-3, was the only well that contained measurable tritium - indicative of water recharged after 1952. Carbon-14 activities from the six monitoring wells ranged from 76.0 to 18.9 percent modern carbon, and groundwater ages (time since recharge), not corrected for chemical reactions, ranged from 2,200 to 13,400 years before present.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121049","collaboration":"Prepared in cooperation with the Calaveras County Water District and the California Department of Water Resources","usgsCitation":"Metzger, L.F., Izbicki, J., and Nawikas, J., 2012, Test drilling and data collection in the Calaveras County portion of the Eastern San Joaquin Groundwater Subbasin, California, December 2009-June 2011: U.S. Geological Survey Open-File Report 2012-1049, iv, 26 p., https://doi.org/10.3133/ofr20121049.","productDescription":"iv, 26 p.","numberOfPages":"30","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":262138,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1049.jpg"},{"id":262132,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1049/","linkFileType":{"id":5,"text":"html"}},{"id":262133,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1049/pdf/ofr20121049.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Calaveras","otherGeospatial":"Eastern San Joaquin Groundwater Subbasin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.08333333333333,38 ], [ -121.08333333333333,38.25 ], [ -120.8,38.25 ], [ -120.8,38 ], [ -121.08333333333333,38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50662515e4b053bff18e1c10","contributors":{"authors":[{"text":"Metzger, Loren F. 0000-0003-2454-2966 lmetzger@usgs.gov","orcid":"https://orcid.org/0000-0003-2454-2966","contributorId":1378,"corporation":false,"usgs":true,"family":"Metzger","given":"Loren","email":"lmetzger@usgs.gov","middleInitial":"F.","affiliations":[],"preferred":true,"id":467699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izbicki, John A. 0000-0003-0816-4408 jaizbick@usgs.gov","orcid":"https://orcid.org/0000-0003-0816-4408","contributorId":1375,"corporation":false,"usgs":true,"family":"Izbicki","given":"John A.","email":"jaizbick@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":467698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nawikas, Joseph M. 0000-0001-9061-6674","orcid":"https://orcid.org/0000-0001-9061-6674","contributorId":96528,"corporation":false,"usgs":true,"family":"Nawikas","given":"Joseph M.","affiliations":[],"preferred":false,"id":467700,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70040042,"text":"70040042 - 2012 - Modeling habitat dynamics accounting for possible misclassification","interactions":[],"lastModifiedDate":"2012-09-26T17:16:49","indexId":"70040042","displayToPublicDate":"2012-09-26T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling habitat dynamics accounting for possible misclassification","docAbstract":"Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Landscape Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/s10980-012-9746-z","usgsCitation":"Veran, S., Kleiner, K., Choquet, R., Collazo, J., and Nichols, J., 2012, Modeling habitat dynamics accounting for possible misclassification: Landscape Ecology, v. 27, no. 7, p. 943-956, https://doi.org/10.1007/s10980-012-9746-z.","productDescription":"14 p.","startPage":"943","endPage":"956","numberOfPages":"14","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":262069,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262066,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10980-012-9746-z","linkFileType":{"id":5,"text":"html"}}],"volume":"27","issue":"7","noUsgsAuthors":false,"publicationDate":"2012-05-24","publicationStatus":"PW","scienceBaseUri":"50e06805e4b0fec3206ecd6f","contributors":{"authors":[{"text":"Veran, Sophie","contributorId":76983,"corporation":false,"usgs":true,"family":"Veran","given":"Sophie","email":"","affiliations":[],"preferred":false,"id":467541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleiner, Kevin J.","contributorId":7955,"corporation":false,"usgs":true,"family":"Kleiner","given":"Kevin J.","affiliations":[],"preferred":false,"id":467540,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choquet, Remi","contributorId":88994,"corporation":false,"usgs":true,"family":"Choquet","given":"Remi","email":"","affiliations":[],"preferred":false,"id":467542,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime jaime_collazo@usgs.gov","contributorId":2613,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"jaime_collazo@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":467539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":467538,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70040046,"text":"sir20125142 - 2012 - Estimation of baseline daily mean streamflows for ungaged locations on Pennsylvania streams, water years 1960-2008","interactions":[],"lastModifiedDate":"2016-08-10T21:28:25","indexId":"sir20125142","displayToPublicDate":"2012-09-26T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5142","title":"Estimation of baseline daily mean streamflows for ungaged locations on Pennsylvania streams, water years 1960-2008","docAbstract":"Water-resource managers use daily mean streamflows to generate streamflow statistics and analyze streamflow conditions. An in-depth evaluation of flow regimes to promote instream ecological health often requires streamflow information obtainable only from a time series hydrograph. Historically, it has been difficult to estimate daily mean streamflow for an ungaged location. The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection, Susquehanna River Basin Commission, and The Nature Conservancy, has developed the Baseline Streamflow Estimator (BaSE) to estimate baseline streamflow at a daily time scale for ungaged streams in Pennsylvania using data collected during water years 1960–2008. Baseline streamflow is minimally altered by regulation, diversion, or mining, and other anthropogenic activities. Daily mean streamflow is estimated in BaSE using a methodology that equates streamflow as a percentile from a flow duration curve for a particular day at an ungaged location with streamflow as a percentile from the flow duration curve for the same day at a reference streamgage that is considered to be hydrologically similar to the ungaged location. An appropriate reference streamgage is selected using map correlation, in which variogram models are developed that correlate streamflow at one streamgage with streamflows at all other streamgages. The percentiles from a flow duration curve for the ungaged location are converted to streamflow through the use of regression equations. Regression equations used to predict 17 flow-duration exceedance probabilities were developed for Pennsylvania using geographic information system-derived basin characteristics. The standard error of prediction for the regression equations ranged from 11 percent to 92 percent with the mean of 31 percent.
\nThe map correlation method for estimating streamflow was tested at locations within two pilot basins, the Upper Delaware River Basin and the Lower Susquehanna River Basin, before being applied statewide. Reference streamgages within the pilot basins were used as ungaged locations for analyzing the map correlation method. Correlation using Spearman’s rho and centroid distance performed as well as, or better than, the method using the closest streamgage as a reference streamgage. Map correlation using the correlation metrics identified in the pilot basins was applied to 156 streamgages in and near Pennsylvania.
\nBaSE uses the map correlation method and flow-duration exceedance probability regression equations to estimate baseline daily mean streamflow for an ungaged location. The output from BaSE is a Microsoft Excel® report file that summarizes the reference streamgage and ungaged location information, including basin characteristics, percent difference in basin characteristics between the two locations, any warning associated with the basin characteristics, mean and median streamflow for the ungaged location, and a daily hydrograph of streamflow for water years 1960–2008 for the ungaged location. The daily mean streamflow for the ungaged location can be exported as a text file to be used as input into other statistical software packages. BaSE estimates daily mean streamflow for baseline conditions only, and any alterations to streamflow from regulation, large water use, or substantial mining are not reflected in the estimated streamflow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125142","collaboration":"Prepared in cooperation with the Pennsylvania Department of Environmental Protection, the Susquehanna River Basin Commission, and The Nature Conservancy","usgsCitation":"Stuckey, M.H., Koerkle, E.H., and Ulrich, J.E., 2012, Estimation of baseline daily mean streamflows for ungaged locations on Pennsylvania streams, water years 1960-2008 (First posted September 26, 2012; Revised and reposted August 11, 2014, version 1.1): U.S. Geological Survey Scientific Investigations Report 2012-5142, Report: viii, 56 p.; Appendix 5; Baseline Streamflow Estimator (v1.1), https://doi.org/10.3133/sir20125142.","productDescription":"Report: viii, 56 p.; Appendix 5; Baseline Streamflow Estimator (v1.1)","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1959-10-01","temporalEnd":"2008-09-30","costCenters":[{"id":532,"text":"Pennsylvania 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Center","active":true,"usgs":true}],"preferred":true,"id":467550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ulrich, James E. julrich@usgs.gov","contributorId":47228,"corporation":false,"usgs":true,"family":"Ulrich","given":"James","email":"julrich@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":false,"id":467551,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70040034,"text":"70040034 - 2012 - Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.","interactions":[],"lastModifiedDate":"2012-09-26T17:16:49","indexId":"70040034","displayToPublicDate":"2012-09-26T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.","docAbstract":"Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69–95%, 51–87%, and 86–100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Conservation Biology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Society for Conservation Biology","publisherLocation":"Washington, D.C.","doi":"10.1111/j.1523-1739.2012.01897.x","usgsCitation":"Ruesch, A.S., Torgersen, C., Lawler, J.J., Olden, J., Peterson, E.E., Volk, C.J., and Lawrence, D.J., 2012, Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.: Conservation Biology, v. 26, no. 5, p. 873-882, https://doi.org/10.1111/j.1523-1739.2012.01897.x.","productDescription":"10 p.","startPage":"873","endPage":"882","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":262073,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262064,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1523-1739.2012.01897.x","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","otherGeospatial":"John Day River","volume":"26","issue":"5","noUsgsAuthors":false,"publicationDate":"2012-07-24","publicationStatus":"PW","scienceBaseUri":"50e492d1e4b0e8fec6cd8b4e","contributors":{"authors":[{"text":"Ruesch, Aaron S.","contributorId":26559,"corporation":false,"usgs":true,"family":"Ruesch","given":"Aaron","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":467516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":48143,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian E.","affiliations":[],"preferred":false,"id":467518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":467520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olden, Julian D.","contributorId":66951,"corporation":false,"usgs":true,"family":"Olden","given":"Julian D.","affiliations":[],"preferred":false,"id":467519,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peterson, Erin E.","contributorId":16264,"corporation":false,"usgs":true,"family":"Peterson","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":467514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Volk, Carol J.","contributorId":25809,"corporation":false,"usgs":true,"family":"Volk","given":"Carol","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":467515,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lawrence, David J.","contributorId":34374,"corporation":false,"usgs":true,"family":"Lawrence","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":467517,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70040043,"text":"70040043 - 2012 - Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon Coast Ranges","interactions":[],"lastModifiedDate":"2012-09-26T17:16:49","indexId":"70040043","displayToPublicDate":"2012-09-26T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon Coast Ranges","docAbstract":"In this paper, we modify dynamic occupancy models developed for detection-nondetection data to allow for the dependence of local vital rates on neighborhood occupancy, where neighborhood is defined very flexibly. Such dependence of occupancy dynamics on the status of a relevant neighborhood is pervasive, yet frequently ignored. Our framework permits joint inference about the importance of neighborhood effects and habitat covariates in determining colonization and extinction rates. Our specific motivation is the recent expansion of the Barred Owl (Strix varia) in western Oregon, USA, over the period 1990-2010. Because the focal period was one of dramatic range expansion and local population increase, the use of models that incorporate regional occupancy (sources of colonists) as determinants of dynamic rate parameters is especially appropriate. We began our analysis of 21 years of Barred Owl presence/nondetection data in the Tyee Density Study Area (TDSA) by testing a suite of six models that varied only in the covariates included in the modeling of detection probability. We then tested whether models that used regional occupancy as a covariate for colonization and extinction outperformed models with constant or year-specific colonization or extinction rates. Finally we tested whether habitat covariates improved the AIC of our models, focusing on which habitat covariates performed best, and whether the signs of habitat effects are consistent with a priori hypotheses. We conclude that all covariates used to model detection probability lead to improved AIC, that regional occupancy influences colonization and extinction rates, and that habitat plays an important role in determining extinction and colonization rates. As occupancy increases from low levels toward equilibrium, colonization increases and extinction decreases, presumably because there are more and more dispersing juveniles. While both rates are affected, colonization increases more than extinction decreases. Colonization is higher and extinction is lower in survey polygons with more riparian forest. The effects of riparian forest on extinction rates are greater than on colonization rates. Model results have implications for management of the invading Barred Owl, both through habitat alteration and removal.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ESA","publisherLocation":"Ithaca, NY","doi":"10.1890/11-1709.1","usgsCitation":"Yackulic, C.B., Reid, J., Davis, R., Hines, J., Nichols, J., and Forsman, E., 2012, Neighborhood and habitat effects on vital rates: expansion of the Barred Owl in the Oregon Coast Ranges: Ecology, v. 93, no. 8, p. 1953-1966, https://doi.org/10.1890/11-1709.1.","productDescription":"14 p.","startPage":"1953","endPage":"1966","numberOfPages":"14","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":262070,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262067,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/11-1709.1","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","volume":"93","issue":"8","noUsgsAuthors":false,"publicationDate":"2012-08-03","publicationStatus":"PW","scienceBaseUri":"50e0970fe4b0fec3206ee88b","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":467545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, Janice","contributorId":89391,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":467547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Raymond","contributorId":91349,"corporation":false,"usgs":true,"family":"Davis","given":"Raymond","affiliations":[],"preferred":false,"id":467548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hines, James E. jhines@usgs.gov","contributorId":3506,"corporation":false,"usgs":true,"family":"Hines","given":"James E.","email":"jhines@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":467544,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":405,"corporation":false,"usgs":true,"family":"Nichols","given":"James D.","email":"jnichols@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":467543,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Forsman, Eric","contributorId":28470,"corporation":false,"usgs":true,"family":"Forsman","given":"Eric","affiliations":[],"preferred":false,"id":467546,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70040027,"text":"70040027 - 2012 - Review of flow rate estimates of the Deepwater Horizon oil spill","interactions":[],"lastModifiedDate":"2012-12-18T15:28:50","indexId":"70040027","displayToPublicDate":"2012-09-26T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Review of flow rate estimates of the Deepwater Horizon oil spill","docAbstract":"The unprecedented nature of the Deepwater Horizon oil spill required the application of research methods to estimate the rate at which oil was escaping from the well in the deep sea, its disposition after it entered the ocean, and total reservoir depletion. Here, we review what advances were made in scientific understanding of quantification of flow rates during deep sea oil well blowouts. We assess the degree to which a consensus was reached on the flow rate of the well by comparing in situ observations of the leaking well with a time-dependent flow rate model derived from pressure readings taken after the Macondo well was shut in for the well integrity test. Model simulations also proved valuable for predicting the effect of partial deployment of the blowout preventer rams on flow rate. Taken together, the scientific analyses support flow rates in the range of ~50,000–70,000 barrels/d, perhaps modestly decreasing over the duration of the oil spill, for a total release of ~5.0 million barrels of oil, not accounting for BP's collection effort. By quantifying the amount of oil at different locations (wellhead, ocean surface, and atmosphere), we conclude that just over 2 million barrels of oil (after accounting for containment) and all of the released methane remained in the deep sea. By better understanding the fate of the hydrocarbons, the total discharge can be partitioned into separate components that pose threats to deep sea vs. coastal ecosystems, allowing responders in future events to scale their actions accordingly.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Proceedings of the National Academy of Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"National Academy of Sciences of the United States of America","publisherLocation":"Washington, D.C.","doi":"10.1073/pnas.1112139108","usgsCitation":"McNutt, M.K., Camilli, R., Crone, T.J., Guthrie, G.D., Hsieh, P.A., Ryerson, T.B., Savas, O., and Shaffer, F., 2012, Review of flow rate estimates of the Deepwater Horizon oil spill: Proceedings of the National Academy of Sciences, v. 109, no. 50, p. 20260-20267, https://doi.org/10.1073/pnas.1112139108.","productDescription":"8 p.","startPage":"20260","endPage":"20267","additionalOnlineFiles":"Y","costCenters":[{"id":511,"text":"Office of the Director","active":false,"usgs":true}],"links":[{"id":474345,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1112139108","text":"Publisher Index Page"},{"id":262071,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262063,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1073/pnas.1112139108","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alabama;Florida;Louisiana;Mississippi;Texas","volume":"109","issue":"50","noUsgsAuthors":false,"publicationDate":"2011-12-20","publicationStatus":"PW","scienceBaseUri":"50d20ca9e4b08b071e771bc6","contributors":{"authors":[{"text":"McNutt, Marcia K. 0000-0003-0117-7716 mcnutt@usgs.gov","orcid":"https://orcid.org/0000-0003-0117-7716","contributorId":327,"corporation":false,"usgs":true,"family":"McNutt","given":"Marcia","email":"mcnutt@usgs.gov","middleInitial":"K.","affiliations":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"preferred":false,"id":467501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Camilli, Rich","contributorId":12727,"corporation":false,"usgs":true,"family":"Camilli","given":"Rich","affiliations":[],"preferred":false,"id":467505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crone, Timothy J.","contributorId":31612,"corporation":false,"usgs":true,"family":"Crone","given":"Timothy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":467507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guthrie, George D.","contributorId":24615,"corporation":false,"usgs":true,"family":"Guthrie","given":"George","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":467506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hsieh, Paul A. 0000-0003-4873-4874 pahsieh@usgs.gov","orcid":"https://orcid.org/0000-0003-4873-4874","contributorId":1634,"corporation":false,"usgs":true,"family":"Hsieh","given":"Paul","email":"pahsieh@usgs.gov","middleInitial":"A.","affiliations":[{"id":39113,"text":"WMA - Office of Quality Assurance","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":467502,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ryerson, Thomas B.","contributorId":7123,"corporation":false,"usgs":true,"family":"Ryerson","given":"Thomas","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":467503,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Savas, Omer","contributorId":104756,"corporation":false,"usgs":true,"family":"Savas","given":"Omer","email":"","affiliations":[],"preferred":false,"id":467508,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaffer, Frank","contributorId":12308,"corporation":false,"usgs":true,"family":"Shaffer","given":"Frank","email":"","affiliations":[],"preferred":false,"id":467504,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70040050,"text":"ofr20111209 - 2012 - Data visualization, time-series analysis, and mass-balance modeling of hydrologic and water-quality data for the McTier Creek watershed, South Carolina, 2007-2009","interactions":[],"lastModifiedDate":"2016-12-08T14:49:32","indexId":"ofr20111209","displayToPublicDate":"2012-09-26T00:00:00","publicationYear":"2012","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":"2011-1209","title":"Data visualization, time-series analysis, and mass-balance modeling of hydrologic and water-quality data for the McTier Creek watershed, South Carolina, 2007-2009","docAbstract":"The McTier Creek watershed is located in the headwaters of the Edisto River Basin, which is in the Coastal Plain region of South Carolina. The Edisto ecosystem has some of the highest recorded fish-tissue mercury concentrations in the United States. In an effort to advance the understanding of the fate and transport of mercury in stream ecosystems, the U.S. Geological Survey, as part of its National Water-Quality Assessment Program, initiated a field investigation of mercury in the McTier Creek watershed in 2006. The initial efforts of the investigation included the collection of extensive hydrologic and water-quality field data, along with the development of several hydrologic and water-quality models. This series of measured and modeled data forms the primary source of information for this investigation to assess the fate and transport of mercury within the McTier Creek watershed.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20111209","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency National Exposure Research Laboratory","usgsCitation":"Benedict, S., Conrads, P., Feaster, T., Journey, C.A., Golden, H., Knightes, C.D., Davis, G.M., and Bradley, P.M., 2012, Data visualization, time-series analysis, and mass-balance modeling of hydrologic and water-quality data for the McTier Creek watershed, South Carolina, 2007-2009: U.S. Geological Survey Open-File Report 2011-1209, vi, 21 p.; col. ill.; maps (col.), https://doi.org/10.3133/ofr20111209.","productDescription":"vi, 21 p.; col. ill.; maps 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Stephen T. benedict@usgs.gov","contributorId":3198,"corporation":false,"usgs":true,"family":"Benedict","given":"Stephen T.","email":"benedict@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":467566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":467563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feaster, Toby D. 0000-0002-5626-5011 tfeaster@usgs.gov","orcid":"https://orcid.org/0000-0002-5626-5011","contributorId":1109,"corporation":false,"usgs":true,"family":"Feaster","given":"Toby D.","email":"tfeaster@usgs.gov","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":467564,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Journey, Celeste A. 0000-0002-2284-5851 cjourney@usgs.gov","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":2617,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste","email":"cjourney@usgs.gov","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":467565,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Golden, Heather E.","contributorId":94914,"corporation":false,"usgs":true,"family":"Golden","given":"Heather E.","affiliations":[],"preferred":false,"id":467569,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knightes, Christopher D.","contributorId":32666,"corporation":false,"usgs":true,"family":"Knightes","given":"Christopher","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":467568,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davis, Gary M.","contributorId":12741,"corporation":false,"usgs":true,"family":"Davis","given":"Gary","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":467567,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":467562,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70255881,"text":"70255881 - 2012 - An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data","interactions":[],"lastModifiedDate":"2024-07-09T13:32:55.362554","indexId":"70255881","displayToPublicDate":"2012-09-25T08:28:03","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data","docAbstract":"The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (∼80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (khat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (khat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (∼90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ∼30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms. The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.
","language":"English","publisher":"MDPI","doi":"10.3390/rs4102890","usgsCitation":"Thenkabail, P.S., and Wu, Z., 2012, An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data: Remote Sensing, v. 4, no. 10, p. 2890-2918, https://doi.org/10.3390/rs4102890.","productDescription":"29 p.","startPage":"2890","endPage":"2918","ipdsId":"IP-035313","costCenters":[{"id":273,"text":"Flagstaff Science Center","active":false,"usgs":true}],"links":[{"id":474346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs4102890","text":"Publisher Index Page"},{"id":430841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tajikistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[71.0142,40.24437],[70.64802,39.93575],[69.55961,40.10321],[69.46489,39.52668],[70.54916,39.6042],[71.78469,39.27946],[73.67538,39.43124],[73.92885,38.50582],[74.25751,38.60651],[74.86482,38.37885],[74.82999,37.99001],[74.98,37.41999],[73.9487,37.42157],[73.26006,37.49526],[72.63689,37.04756],[72.19304,36.94829],[71.84464,36.73817],[71.44869,37.06564],[71.54192,37.90577],[71.2394,37.95327],[71.34813,38.25891],[70.80682,38.48628],[70.3763,38.1384],[70.27057,37.73516],[70.11658,37.58822],[69.51879,37.609],[69.19627,37.15114],[68.85945,37.34434],[68.13556,37.02312],[67.83,37.14499],[68.39203,38.15703],[68.17603,38.90155],[67.44222,39.14014],[67.70143,39.58048],[68.53642,39.53345],[69.01163,40.08616],[69.32949,40.72782],[70.66662,40.96021],[70.45816,40.49649],[70.60141,40.21853],[71.0142,40.24437]]]},\"properties\":{\"name\":\"Tajikistan\"}}]}","volume":"4","issue":"10","noUsgsAuthors":false,"publicationDate":"2012-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":905870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":905871,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70040007,"text":"70040007 - 2012 - Subsurface gas hydrates in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2012-09-25T17:16:32","indexId":"70040007","displayToPublicDate":"2012-09-25T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Subsurface gas hydrates in the northern Gulf of Mexico","docAbstract":"The northernGulf of Mexico (GoM) has long been a focus area for the study of gashydrates. Throughout the 1980s and 1990s, work focused on massive gashydrates deposits that were found to form at and near the seafloor in association with hydrocarbon seeps. However, as global scientific and industrial interest in assessment of the drilling hazards and resource implications of gashydrate accelerated, focus shifted to understanding the nature and abundance of \"buried\" gashydrates. Through 2005, despite the drilling of more than 1200 oil and gas industry wells through the gashydrate stability zone, published evidence of significant sub-seafloor gashydrate in the GoM was lacking. A 2005 drilling program by the GoM GasHydrate Joint Industry Project (the JIP) provided an initial confirmation of the occurrence of gashydrates below the GoM seafloor. In 2006, release of data from a 2003 industry well in Alaminos Canyon 818 provided initial documentation of gashydrate occurrence at high concentrations in sand reservoirs in the GoM. From 2006 to 2008, the JIP facilitated the integration of geophysical and geological data to identify sites prospective for gashydrate-bearing sands, culminating in the recommendation of numerous drilling targets within four sites spanning a range of typical deepwater settings. Concurrent with, but independent of, the JIP prospecting effort, the Bureau of Ocean Energy Management (BOEM) conducted a preliminary assessment of the GoM gashydratepetroleum system, resulting in an estimate of 607 trillion cubic meters (21,444 trillion cubic feet) gas-in-place of which roughly one-third occurs at expected high concentrations in sand reservoirs. In 2009, the JIP drilled seven wells at three sites, discovering gashydrate at high saturation in sand reservoirs in four wells and suspected gashydrate at low to moderate saturations in two other wells. These results provide an initial confirmation of the complex nature and occurrence of gashydrate-bearing sands in the GoM, the efficacy of the integrated geological/geophysical prospecting approach used to identify the JIP drilling sites, and the relevance of the 2008 BOEM assessment.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine and Petroleum Geology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.marpetgeo.2011.10.003","usgsCitation":"Boswell, R., Collett, T.S., Frye, M., Shedd, W., McConnell, D.R., and Shelander, D., 2012, Subsurface gas hydrates in the northern Gulf of Mexico: Marine and Petroleum Geology, v. 34, no. 1, p. 4-30, https://doi.org/10.1016/j.marpetgeo.2011.10.003.","productDescription":"27 p.","startPage":"4","endPage":"30","numberOfPages":"26","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":262046,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262043,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.marpetgeo.2011.10.003","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"Gulf Of Mexico","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.85,18.166666666666668 ], [ -97.85,30.383333333333333 ], [ -81.03333333333333,30.383333333333333 ], [ -81.03333333333333,18.166666666666668 ], [ -97.85,18.166666666666668 ] ] ] } } ] }","volume":"34","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50e4dfa4e4b0e8fec6ce49b7","contributors":{"authors":[{"text":"Boswell, Ray","contributorId":12307,"corporation":false,"usgs":true,"family":"Boswell","given":"Ray","affiliations":[],"preferred":false,"id":467435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collett, Timothy S. 0000-0002-7598-4708 tcollett@usgs.gov","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":1698,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","email":"tcollett@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":467434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frye, Matthew","contributorId":48428,"corporation":false,"usgs":true,"family":"Frye","given":"Matthew","affiliations":[],"preferred":false,"id":467439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shedd, William","contributorId":13851,"corporation":false,"usgs":true,"family":"Shedd","given":"William","affiliations":[],"preferred":false,"id":467436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McConnell, Daniel R.","contributorId":47628,"corporation":false,"usgs":true,"family":"McConnell","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":467438,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelander, Dianna","contributorId":40463,"corporation":false,"usgs":true,"family":"Shelander","given":"Dianna","email":"","affiliations":[],"preferred":false,"id":467437,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70040014,"text":"70040014 - 2012 - Population-level impact of white-nose syndrome on the endangered Indiana bat","interactions":[],"lastModifiedDate":"2012-09-25T17:16:32","indexId":"70040014","displayToPublicDate":"2012-09-25T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Population-level impact of white-nose syndrome on the endangered Indiana bat","docAbstract":"Establishing status and trend for an endangered species is critical to recovery, especially when it is faced with a nascent extinction agent. We calculated, with hierarchical log-linear change-point models, hibernaculum-level population trends between 1983 and 2009 for the endangered Indiana bat (Myotis sodalis) now subjected to the fast-spreading fungal disease white-nose syndrome. We combined trends from 222 wintering populations before and after onset of the disease to determine trend for clusters of interacting wintering populations, recovery units, and the species. Before onset of the disease, a west-to-east gradient in trends existed, with westernmost populations declining and easternmost populations increasing in abundance. The species as a whole, however, was stationary between 1983 and 2005 (-0.5% mean annual change; 95% confidence interval [CI] = -2.8, +1.8%). Estimated mean population size in 2009 was 377,124 bats (195,398-957,348), with large variance apparently caused by white-nose syndrome. With the onset of white-nose syndrome (2006-2009), the species exhibited a 10.3% annual decline (95% CI = -21.1, +2.0%). White-nose syndrome is having an appreciable influence on the status and trends of Indiana bat populations, stalling and in some cases reversing population gains made in recent years.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Mammalogy","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Society of Mammalogists","publisherLocation":"http://www.mammalsociety.org/","doi":"10.1644/11-MAMM-A-355.1","usgsCitation":"Thogmartin, W.E., King, R.A., McKann, P., Szymanski, J.A., and Pruitt, L., 2012, Population-level impact of white-nose syndrome on the endangered Indiana bat: Journal of Mammalogy, v. 93, no. 4, p. 1086-1098, https://doi.org/10.1644/11-MAMM-A-355.1.","productDescription":"13 p.","startPage":"1086","endPage":"1098","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":474349,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1644/11-mamm-a-355.1","text":"External Repository"},{"id":262048,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262042,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1644/11-MAMM-A-355.1","linkFileType":{"id":5,"text":"html"}}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -93.28333333333333,32.666666666666664 ], [ -93.28333333333333,45.85 ], [ -68.95,45.85 ], [ -68.95,32.666666666666664 ], [ -93.28333333333333,32.666666666666664 ] ] ] } } ] }","volume":"93","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-09-14","publicationStatus":"PW","scienceBaseUri":"50e164b4e4b0ff1e7c577741","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":467455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, R. Andrew","contributorId":40839,"corporation":false,"usgs":true,"family":"King","given":"R.","email":"","middleInitial":"Andrew","affiliations":[],"preferred":false,"id":467458,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKann, Patrick C.","contributorId":14940,"corporation":false,"usgs":true,"family":"McKann","given":"Patrick C.","affiliations":[],"preferred":false,"id":467456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Szymanski, Jennifer A.","contributorId":51593,"corporation":false,"usgs":true,"family":"Szymanski","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":467459,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pruitt, Lori","contributorId":17468,"corporation":false,"usgs":true,"family":"Pruitt","given":"Lori","email":"","affiliations":[],"preferred":false,"id":467457,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70040019,"text":"tm7C8 - 2012 - Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks","interactions":[],"lastModifiedDate":"2012-10-03T17:16:16","indexId":"tm7C8","displayToPublicDate":"2012-09-25T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C8","title":"Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks","docAbstract":"Models of environmental systems have become increasingly complex, incorporating increasingly large numbers of parameters in an effort to represent physical processes on a scale approaching that at which they occur in nature. Consequently, the inverse problem of parameter estimation (specifically, model calibration) and subsequent uncertainty analysis have become increasingly computation-intensive endeavors. Fortunately, advances in computing have made computational power equivalent to that of dozens to hundreds of desktop computers accessible through a variety of alternate means: modelers have various possibilities, ranging from traditional Local Area Networks (LANs) to cloud computing. Commonly used parameter estimation software is well suited to take advantage of the availability of such increased computing power. Unfortunately, logistical issues become increasingly important as an increasing number and variety of computers are brought to bear on the inverse problem. To facilitate efficient access to disparate computer resources, the PESTCommander program documented herein has been developed to provide a Graphical User Interface (GUI) that facilitates the management of model files (\"file management\") and remote launching and termination of \"slave\" computers across a distributed network of computers (\"run management\"). In version 1.0 described here, PESTCommander can access and ascertain resources across traditional Windows LANs: however, the architecture of PESTCommander has been developed with the intent that future releases will be able to access computing resources (1) via trusted domains established in Wide Area Networks (WANs) in multiple remote locations and (2) via heterogeneous networks of Windows- and Unix-based operating systems. The design of PESTCommander also makes it suitable for extension to other computational resources, such as those that are available via cloud computing. Version 1.0 of PESTCommander was developed primarily to work with the parameter estimation software PEST; the discussion presented in this report focuses on the use of the PESTCommander together with Parallel PEST. However, PESTCommander can be used with a wide variety of programs and models that require management, distribution, and cleanup of files before or after model execution. In addition to its use with the Parallel PEST program suite, discussion is also included in this report regarding the use of PESTCommander with the Global Run Manager GENIE, which was developed simultaneously with PESTCommander.","largerWorkTitle":"Automated Data Processing and Computations (Book 7)","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C8","collaboration":"Great Lakes Restoration Initiative. This report is Chapter 8 of Section C in Book 7, Automated Data Processing and Computations.","usgsCitation":"Karanovic, M., Muffels, C.T., Tonkin, M.J., and Hunt, R.J., 2012, Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks: U.S. Geological Survey Techniques and Methods 7-C8, iii; 9 p.; PESTCommander Software, https://doi.org/10.3133/tm7C8.","productDescription":"iii; 9 p.; PESTCommander Software","numberOfPages":"18","onlineOnly":"Y","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":262049,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm_7_c8.gif"},{"id":262044,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/tm7c8/","linkFileType":{"id":5,"text":"html"}},{"id":262045,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/tm7c8/pdf/TMBook7_ChapC8.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50788b9ae4b0cfc2d59f59d5","contributors":{"authors":[{"text":"Karanovic, Marinko","contributorId":54831,"corporation":false,"usgs":true,"family":"Karanovic","given":"Marinko","email":"","affiliations":[],"preferred":false,"id":467490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muffels, Christopher T.","contributorId":105949,"corporation":false,"usgs":true,"family":"Muffels","given":"Christopher","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":467491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tonkin, Matthew J.","contributorId":26376,"corporation":false,"usgs":true,"family":"Tonkin","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":467489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":467488,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}