Few chemicals are approved to control or eradicate nuisance fish populations in the United States. Carbon dioxide (CO2) is currently being developed and studied as a new piscicide option for nonselective population control. This study evaluated dry ice (solid state CO2) as a simple CO2 delivery method during winter piscicide applications. Nonnative Silver Carp Hypophthalmichthys molitrix, Bighead Carp H. nobilis, and native Fathead Minnow Pimephales promelas were overwintered together in ice‐covered ponds treated with 25 kg dry ice/100,000 L (low treatment) or 50 kg dry ice/100,000 L (high treatment). Overwinter fish survival was significantly reduced in ponds treated with dry ice relative to untreated control ponds. Fathead Minnows were less susceptible to CO2exposure than the carps, with 26–96% survival in low‐treatment ponds and 4–68% survival in high‐treatment ponds. Silver Carp and Bighead Carp were more sensitive to CO2 treatments and no individuals of either species survived in ponds with the high‐treatment level. Water samples were also collected in all ponds throughout this study, and we observed notably higher Silver Carp and Bighead Carp environmental DNA (eDNA) concentrations in dry‐ice‐treated ponds relative to untreated control ponds. Distinct changes in eDNA trends correlated with fish mortality, and results indicate that eDNA sampling could be a useful indicator of piscicide efficacy. This study demonstrates that CO2 administered as dry ice is an effective under‐ice piscicide method.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10227","usgsCitation":"Cupp, A.R., Smerud, J.R., Tix, J., Rivera, J., Kageyama, S.A., Merkes, C.M., Erickson, R.A., Amberg, J., and Gaikowski, M., 2018, Assessment of carbon dioxide piscicide treatments: North American Journal of Fisheries Management, v. 38, no. 6, p. 1241-1250, https://doi.org/10.1002/nafm.10227.","productDescription":"10 p.","startPage":"1241","endPage":"1250","ipdsId":"IP-096505","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437638,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VABB9H","text":"USGS data release","linkHelpText":"Assessment of carbon dioxide piscicide treatments: Data"},{"id":361257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"6","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smerud, Justin R. 0000-0003-4385-7437 jrsmerud@usgs.gov","orcid":"https://orcid.org/0000-0003-4385-7437","contributorId":5031,"corporation":false,"usgs":true,"family":"Smerud","given":"Justin","email":"jrsmerud@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tix, John 0000-0002-9531-5624 jtix@usgs.gov","orcid":"https://orcid.org/0000-0002-9531-5624","contributorId":197014,"corporation":false,"usgs":true,"family":"Tix","given":"John","email":"jtix@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rivera, Jose 0000-0003-3756-6860 jrivera@usgs.gov","orcid":"https://orcid.org/0000-0003-3756-6860","contributorId":201064,"corporation":false,"usgs":true,"family":"Rivera","given":"Jose","email":"jrivera@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kageyama, Stacie A. 0000-0003-4185-3627 skageyama@usgs.gov","orcid":"https://orcid.org/0000-0003-4185-3627","contributorId":195991,"corporation":false,"usgs":true,"family":"Kageyama","given":"Stacie","email":"skageyama@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757245,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757246,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gaikowski, Mark P. 0000-0002-6507-9341 mgaikowski@usgs.gov","orcid":"https://orcid.org/0000-0002-6507-9341","contributorId":149357,"corporation":false,"usgs":true,"family":"Gaikowski","given":"Mark P.","email":"mgaikowski@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":757247,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70201870,"text":"70201870 - 2018 - Alaska","interactions":[],"lastModifiedDate":"2019-02-01T12:06:17","indexId":"70201870","displayToPublicDate":"2019-01-01T12:06:08","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Alaska","docAbstract":"Alaska is the largest state in the Nation, almost one-fifth the size of the combined lower 48 United States, and is rich in natural capital resources. Alaska is often identified as being on the front lines of climate change since it is warming faster than any other state and faces a myriad of issues associated with a changing climate. The cost of infrastructure damage from a warming climate is projected to be very large, potentially ranging from $110 to $270 million per year, assuming timely repair and maintenance. Although climate change does and will continue to dramatically transform the climate and environment of the Arctic, proactive adaptation in Alaska has the potential to reduce costs associated with these impacts. This includes the dissemination of several tools, such as guidebooks to support adaptation planning, some of which focus on Indigenous communities. While many opportunities exist with a changing climate, economic prospects are not well captured in the literature at this time.
As the climate continues to warm, there is likely to be a nearly sea ice-free Arctic during the summer by mid-century. Ocean acidification is an emerging global problem that will intensify with continued carbon dioxide (CO2) emissions and negatively affects organisms. Climate change will likely affect management actions and economic drivers, including fisheries, in complex ways. The use of multiple alternative models to appropriately characterize uncertainty in future fisheries biomass trajectories and harvests could help manage these challenges. As temperature and precipitation increase across the Alaska landscape, physical and biological changes are also occurring throughout Alaska’s terrestrial ecosystems. Degradation of permafrost is expected to continue, with associated impacts to infrastructure, river and stream discharge, water quality, and fish and wildlife habitat.
Longer sea ice-free seasons, higher ground temperatures, and relative sea level rise are expected to exacerbate flooding and accelerate erosion in many regions, leading to the loss of terrestrial habitat in the future and in some cases requiring entire communities or portions of communities to relocate to safer terrain. The influence of climate change on human health in Alaska can be traced to three sources: direct exposures, indirect effects, and social or psychological disruption. Each of these will have different manifestations for Alaskans when compared to residents elsewhere in the United States. Climate change exerts indirect effects on human health in Alaska through changes to water, air, and soil and through ecosystem changes affecting disease ecology and food security, especially in rural communities.
Alaska’s rural communities are predominantly inhabited by Indigenous peoples who may be disproportionately vulnerable to socioeconomic and environmental change; however, they also have rich cultural traditions of resilience and adaptation. The impacts of climate change will likely affect all aspects of Alaska Native societies, from nutrition, infrastructure, economics, and health consequences to language, education, and the communities themselves.
The profound and diverse climate-driven changes in Alaska’s physical environment and ecosystems generate economic impacts through their effects on environmental services. These services include positive benefits directly from ecosystems (for example, food, water, and other resources), as well as services provided directly from the physical environment (for example, temperature moderation, stable ground for supporting infrastructure, and smooth surface for overland transportation). Some of these effects are relatively assured and in some cases are already occurring. Other impacts are highly uncertain, due to their dependence on the structure of global and regional economies and future human alterations to the environment decades into the future, but they could be large.
In Alaska, a range of adaptations to changing climate and related environmental conditions are underway and others have been proposed as potential actions, including measures to reduce vulnerability and risk, as well as more systemic institutional transformation.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH26","usgsCitation":"Markon, C., Gray, S., Berman, M., Eerkes-Medrano, L., Hennessy, T., Huntington, H.P., Littell, J., McCammon, M., Thoman, R., and Trainor, S., 2018, Alaska, 57 p., https://doi.org/10.7930/NCA4.2018.CH26.","productDescription":"57 p.","startPage":"1185","endPage":"1241","ipdsId":"IP-103840","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":360915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755845,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. W.","contributorId":212242,"corporation":false,"usgs":false,"family":"Avery","given":"C.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":755846,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Easterling, D. R.","contributorId":212243,"corporation":false,"usgs":false,"family":"Easterling","given":"D.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":755847,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Kunkel, K. E.","contributorId":83626,"corporation":false,"usgs":true,"family":"Kunkel","given":"K.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":755848,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Lewis, K. L. M.","contributorId":212244,"corporation":false,"usgs":false,"family":"Lewis","given":"K.","email":"","middleInitial":"L. M.","affiliations":[],"preferred":false,"id":755849,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"Maycock, T. K.","contributorId":212245,"corporation":false,"usgs":false,"family":"Maycock","given":"T.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":755850,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Stewart, B. C.","contributorId":212246,"corporation":false,"usgs":false,"family":"Stewart","given":"B.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":755851,"contributorType":{"id":2,"text":"Editors"},"rank":7}],"authors":[{"text":"Markon, Carl","contributorId":212151,"corporation":false,"usgs":false,"family":"Markon","given":"Carl","affiliations":[{"id":38437,"text":"Retired, U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":755635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Stephen T. 0000-0002-0959-3418 sgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-3418","contributorId":209851,"corporation":false,"usgs":true,"family":"Gray","given":"Stephen","email":"sgray@usgs.gov","middleInitial":"T.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":755636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berman, Matthew","contributorId":200375,"corporation":false,"usgs":false,"family":"Berman","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":755637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eerkes-Medrano, Laura 0000-0001-8413-9031","orcid":"https://orcid.org/0000-0001-8413-9031","contributorId":212152,"corporation":false,"usgs":false,"family":"Eerkes-Medrano","given":"Laura","email":"","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":755638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hennessy, Thomas","contributorId":212153,"corporation":false,"usgs":false,"family":"Hennessy","given":"Thomas","email":"","affiliations":[{"id":38438,"text":"U.S. Centers for Disease Control and Prevention","active":true,"usgs":false}],"preferred":false,"id":755639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huntington, Henry P. 0000-0003-2308-8677","orcid":"https://orcid.org/0000-0003-2308-8677","contributorId":212154,"corporation":false,"usgs":false,"family":"Huntington","given":"Henry","email":"","middleInitial":"P.","affiliations":[{"id":38439,"text":"Huntington Consulting","active":true,"usgs":false}],"preferred":false,"id":755640,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":755641,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCammon, Molly","contributorId":212155,"corporation":false,"usgs":false,"family":"McCammon","given":"Molly","email":"","affiliations":[{"id":38440,"text":"Alaska Ocean Observing System","active":true,"usgs":false}],"preferred":false,"id":755642,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thoman, Richard","contributorId":187613,"corporation":false,"usgs":false,"family":"Thoman","given":"Richard","affiliations":[],"preferred":false,"id":755643,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Trainor, Sarah 0000-0002-9911-9006","orcid":"https://orcid.org/0000-0002-9911-9006","contributorId":212156,"corporation":false,"usgs":false,"family":"Trainor","given":"Sarah","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":755644,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70201871,"text":"70201871 - 2018 - Land cover and land use change","interactions":[],"lastModifiedDate":"2019-02-01T12:02:23","indexId":"70201871","displayToPublicDate":"2019-01-01T12:02:18","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Land cover and land use change","docAbstract":"Climate can affect and be affected by changes in land cover (the physical features that cover the land such as trees or pavement) and land use (human management and activities on land, such as mining or recreation). A forest, for instance, would likely include tree cover but could also include areas of recent tree removals currently covered by open grass areas. Land cover and use are inherently coupled: changes in land-use practices can change land cover, and land cover enables specific land uses. Understanding how land cover, use, condition, and management vary in space and time is challenging.
Changes in land cover can occur in response to both human and climate drivers. For example, demand for new settlements often results in the permanent loss of natural and working lands, which can result in localized changes in weather patterns, temperature, and precipitation. Aggregated over large areas, these changes have the potential to influence Earth’s climate by altering regional and global circulation patterns, changing the albedo (reflectivity) of Earth’s surface, and changing the amount of carbon dioxide (CO2) in the atmosphere. Conversely, climate change can also influence land cover, resulting in a loss of forest cover from climate-related increases in disturbances, the expansion of woody vegetation into grasslands, and the loss of beaches due to coastal erosion amplified by rises in sea level.
Land use is also changed by both human and climate drivers. Land-use decisions are traditionally based on short-term economic factors. Land-use changes are increasingly being influenced by distant forces due to the globalization of many markets. Land use can also change due to local, state, and national policies, such as programs designed to remove cultivation from highly erodible land to mitigate degradation, legislation to address sea level rise in local comprehensive plans, or policies that reduce the rate of timber harvest on federal lands. Technological innovation has also influenced land-use change, with the expansion of cultivated lands from the development of irrigation technologies and, more recently, decreases in demand for agricultural land due to increases in crop productivity. The recent expansion of oil and gas extraction activities throughout large areas of the United States demonstrates how policy, economics, and technology can collectively influence and change land use and land cover.
Decisions about land use, cover, and management can help determine society’s ability to mitigate and adapt to climate change.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH5","usgsCitation":"Sleeter, B.M., Loveland, T., Domke, G., Herold, N., Wickham, J., and Wood, N.J., 2018, Land cover and land use change, 30 p., https://doi.org/10.7930/NCA4.2018.CH5.","productDescription":"30 p.","startPage":"202","endPage":"231","ipdsId":"IP-103826","costCenters":[{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"links":[{"id":360914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755838,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. W.","contributorId":212242,"corporation":false,"usgs":false,"family":"Avery","given":"C.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":755839,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Easterling, D. R.","contributorId":212243,"corporation":false,"usgs":false,"family":"Easterling","given":"D.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":755840,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Kunkel, K. E.","contributorId":83626,"corporation":false,"usgs":true,"family":"Kunkel","given":"K.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":755841,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Lewis, K. L. M.","contributorId":212244,"corporation":false,"usgs":false,"family":"Lewis","given":"K.","email":"","middleInitial":"L. M.","affiliations":[],"preferred":false,"id":755842,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"Maycock, T. K.","contributorId":212245,"corporation":false,"usgs":false,"family":"Maycock","given":"T.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":755843,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Stewart, B. C.","contributorId":212246,"corporation":false,"usgs":false,"family":"Stewart","given":"B.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":755844,"contributorType":{"id":2,"text":"Editors"},"rank":7}],"authors":[{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":755645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":202518,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":755646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Domke, Grant 0000-0003-0485-0355","orcid":"https://orcid.org/0000-0003-0485-0355","contributorId":212157,"corporation":false,"usgs":false,"family":"Domke","given":"Grant","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":755647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herold, Nate","contributorId":127749,"corporation":false,"usgs":false,"family":"Herold","given":"Nate","email":"","affiliations":[{"id":7054,"text":"NOAA/NMFS, Silver Spring, MD","active":true,"usgs":false}],"preferred":false,"id":755648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wickham, James","contributorId":140259,"corporation":false,"usgs":false,"family":"Wickham","given":"James","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":755649,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":755650,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201872,"text":"70201872 - 2018 - Northeast","interactions":[],"lastModifiedDate":"2019-02-01T11:58:14","indexId":"70201872","displayToPublicDate":"2019-01-01T11:58:09","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Northeast","docAbstract":"The distinct seasonality of the Northeast’s climate supports a diverse natural landscape adapted to the extremes of cold, snowy winters and warm to hot, humid summers. This natural landscape provides the economic and cultural foundation for many rural communities, which are largely supported by a diverse range of agricultural, tourism, and natural resource-dependent industries (see Ch. 10: Ag & Rural, Key Message 4). The recent dominant trend in precipitation throughout the Northeast has been towards increases in rainfall intensity, with increases in intensity exceeding those in other regions of the contiguous United States. Further increases in rainfall intensity are expected, with increases in total precipitation expected during the winter and spring but with little change in the summer. Monthly precipitation in the Northeast is projected to be about 1 inch greater for December through April by end of century (2070–2100) under the higher scenario (RCP8.5).
Ocean and coastal ecosystems are being affected by large changes in a variety of climate-related environmental conditions. These ecosystems support fishing and aquaculture, tourism and recreation, and coastal communities. Observed and projected increases in temperature, acidification, storm frequency and intensity, and sea levels are of particular concern for coastal and ocean ecosystems, as well as local communities and their interconnected social and economic systems. Increasing temperatures and changing seasonality on the Northeast Continental Shelf have affected marine organisms and the ecosystem in various ways. The warming trend experienced in the Northeast Continental Shelf has been associated with many fish and invertebrate species moving northward and to greater depths. Because of the diversity of the Northeast’s coastal landscape, the impacts from storms and sea level rise will vary at different locations along the coast.
Northeastern cities, with their abundance of concrete and asphalt and relative lack of vegetation, tend to have higher temperatures than surrounding regions due to the urban heat island effect. During extreme heat events, nighttime temperatures in the region’s big cities are generally several degrees higher than surrounding regions, leading to higher risk of heat-related death. Urban areas are at risk for large numbers of evacuated and displaced populations and damaged infrastructure due to both extreme precipitation events and recurrent flooding, potentially requiring significant emergency response efforts and consideration of a long-term commitment to rebuilding and adaptation, and/or support for relocation where needed. Much of the infrastructure in the Northeast, including drainage and sewer systems, flood and storm protection assets, transportation systems, and power supply, is nearing the end of its planned life expectancy. Climate-related disruptions will only exacerbate existing issues with aging infrastructure. Sea level rise has amplified storm impacts in the Northeast (Key Message 2), contributing to higher surges that extend farther inland, as demonstrated in New York City in the aftermath of Superstorm Sandy in 2012. Service and resource supply infrastructure in the Northeast is at increasing risk of disruption, resulting in lower quality of life, economic declines, and increased social inequality. Loss of public services affects the capacity of communities to function as administrative and economic centers and triggers disruptions of interconnected supply chains (Ch. 16: International, Key Message 1).
Increases in annual average temperatures across the Northeast range from less than 1°F (0.6°C) in West Virginia to about 3°F (1.7°C) or more in New England since 1901. Although the relative risk of death on very hot days is lower today than it was a few decades ago, heat-related illness and death remain significant public health problems in the Northeast. For example, a study in New York City estimated that in 2013 there were 133 excess deaths due to extreme heat. These projected increases in temperature are expected to lead to substantially more premature deaths, hospital admissions, and emergency department visits across the Northeast. For example, in the Northeast we can expect approximately 650 additional premature deaths per year from extreme heat by the year 2050 under either a lower (RCP4.5) or higher (RCP8.5) scenario and from 960 (under RCP4.5) to 2,300 (under RCP8.5) more premature deaths per year by 2090.
Communities, towns, cities, counties, states, and tribes across the Northeast are engaged in efforts to build resilience to environmental challenges and adapt to a changing climate. Developing and implementing climate adaptation strategies in daily practice often occur in collaboration with state and federal agencies (e.g., New Jersey Climate Adaptation Alliance 2017, New York Climate Clearinghouse 2017, Rhode Island STORMTOOLS 2017, EPA 2017, CDC 2015). Advances in rural towns, cities, and suburban areas include low-cost adjustments of existing building codes and standards. In coastal areas, partnerships among local communities and federal and state agencies leverage federal adaptation tools and decision support frameworks (for example, NOAA’s Digital Coast, USGS’s Coastal Change Hazards Portal, and New Jersey’s Getting to Resilience). Increasingly, cities and towns across the Northeast are developing or implementing plans for adaptation and resilience in the face of changing climate (e.g., EPA 2017). The approaches are designed to maintain and enhance the everyday lives of residents and promote economic development. In some cities, adaptation planning has been used to respond to present and future challenges in the built environment. Regional efforts have recommended changes in design standards when building, replacing, or retrofitting infrastructure to account for a changing climate.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH18","usgsCitation":"Dupigny-Giroux, L.L., Mecray, E.L., Lemcke-Stampone, M.D., Hodgkins, G.A., Lentz, E.E., Mills, K.E., Lane, E.D., Miller, R., Hollinger, D.Y., Solecki, W.D., Wellenius, G.A., Sheffield, P.E., McDonald, A.B., and Caldwell, C., 2018, Northeast, 74 p., https://doi.org/10.7930/NCA4.2018.CH18.","productDescription":"74 p.","startPage":"669","endPage":"742","ipdsId":"IP-103836","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":468162,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7930/nca4.2018.ch18","text":"Publisher Index Page"},{"id":360913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755831,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. 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0000-0001-9156-1193","orcid":"https://orcid.org/0000-0001-9156-1193","contributorId":212167,"corporation":false,"usgs":false,"family":"Sheffield","given":"Perry","email":"","middleInitial":"E.","affiliations":[{"id":38444,"text":"Icahn School of Medicine at Mount Sinai","active":true,"usgs":false}],"preferred":false,"id":755662,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McDonald, Anthony B.","contributorId":212168,"corporation":false,"usgs":false,"family":"McDonald","given":"Anthony","email":"","middleInitial":"B.","affiliations":[{"id":38445,"text":"Monmouth University","active":true,"usgs":false}],"preferred":false,"id":755663,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Caldwell, Christopher","contributorId":212169,"corporation":false,"usgs":false,"family":"Caldwell","given":"Christopher","email":"","affiliations":[{"id":38446,"text":"College of Menominee Nations","active":true,"usgs":false}],"preferred":false,"id":755664,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70201873,"text":"70201873 - 2018 - Hawai‘i and U.S.-Affiliated Pacific Islands","interactions":[],"lastModifiedDate":"2019-02-01T11:53:35","indexId":"70201873","displayToPublicDate":"2019-01-01T11:53:29","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Hawai‘i and U.S.-Affiliated Pacific Islands","docAbstract":"The U.S. Pacific Islands are culturally and environmentally diverse, treasured by the 1.9 million people who call them home. Pacific islands are particularly vulnerable to climate change impacts due to their exposure and isolation, small size, low elevation (in the case of atolls), and concentration of infrastructure and economy along the coasts.
A prevalent cause of year-to-year changes in climate patterns around the globe and in the Pacific Islands region is the El Niño–Southern Oscillation (ENSO). The El Niño and La Niña phases of ENSO can dramatically affect precipitation, air and ocean temperature, sea surface height, storminess, wave size, and trade winds. It is unknown exactly how the timing and intensity of ENSO will continue to change in the coming decades, but recent climate model results suggest a doubling in frequency of both El Niño and La Niña extremes in this century as compared to the 20th century under scenarios with more warming, including the higher scenario (RCP8.5).
On islands, all natural sources of freshwater come from rainfall received within their limited land areas. Severe droughts are common, making water shortage one of the most important climate-related risks in the region. As temperature continues to rise and cloud cover decreases in some areas, evaporation is expected to increase, causing both reduced water supply and higher water demand. Streamflow in Hawai‘i has declined over approximately the past 100 years, consistent with observed decreases in rainfall.
The impacts of sea level rise in the Pacific include coastal erosion, episodic flooding, permanent inundation, heightened exposure to marine hazards, and saltwater intrusion to surface water and groundwater systems. Sea level rise will disproportionately affect the tropical Pacific and potentially exceed the global average.
Invasive species, landscape change, habitat alteration, and reduced resilience have resulted in extinctions and diminished ecosystem function. Inundation of atolls in the coming decades is projected to impact existing on-island ecosystems. Wildlife that relies on coastal habitats will likely also be severely impacted. In Hawaiʻi, coral reefs contribute an estimated $477 million to the local economy every year. Under projected warming of approximately 0.5°F per decade, all nearshore coral reefs in the Hawai‘i and Pacific Islands region will experience annual bleaching before 2050. An ecosystem-based approach to international management of open ocean fisheries in the Pacific that incorporates climate-informed catch limits is expected to produce more realistic future harvest levels and enhance ecosystem resilience.
Indigenous communities of the Pacific derive their sense of identity from the islands. Emerging issues for Indigenous communities of the Pacific include the resilience of marine-managed areas and climate-induced human migration from their traditional lands. The rich body of traditional knowledge is place-based and localized and is useful in adaptation planning because it builds on intergenerational sharing of observations. Documenting the kinds of governance structures or decision-making hierarchies created for management of these lands and waters is also important as a learning tool that can be shared with other island communities.
Across the region, groups are coming together to minimize damage and disruption from coastal flooding and inundation as well as other climate-related impacts. Social cohesion is already strong in many communities, making it possible to work together to take action. Early intervention can lower economic, environmental, social, and cultural costs and reduce or prevent conflict and displacement from ancestral land and resources.
Biodiversity—the variety of life on Earth—provides vital services that support and improve human health and well-being. Ecosystems, which are composed of living things that interact with the physical environment, provide numerous essential benefits to people. These benefits, termed ecosystem services, encompass four primary functions: provisioning materials, such as food and fiber; regulating critical parts of the environment, such as water quality and erosion control; providing cultural services, such as recreational opportunities and aesthetic value; and providing supporting services, such as nutrient cycling. Climate change poses many threats and potential disruptions to ecosystems and biodiversity, as well as to the ecosystem services on which people depend.
Building on the findings of the Third National Climate Assessment (NCA3), this chapter provides additional evidence that climate change is significantly impacting ecosystems and biodiversity in the United States. Mounting evidence also demonstrates that climate change is increasingly compromising the ecosystem services that sustain human communities, economies, and well-being. Both human and natural systems respond to change, but their ability to respond and thrive under new conditions is determined by their adaptive capacity, which may be inadequate to keep pace with rapid change. Our understanding of climate change impacts and the responses of biodiversity and ecosystems has improved since NCA3. The expected consequences of climate change will vary by region, species, and ecosystem type. Management responses are evolving as new tools and approaches are developed and implemented; however, they may not be able to overcome the negative impacts of climate change. Although efforts have been made since NCA3 to incorporate climate adaptation strategies into natural resource management, significant work remains to comprehensively implement climate-informed planning. This chapter presents additional evidence for climate change impacts to biodiversity, ecosystems, and ecosystem services, reflecting increased confidence in the findings reported in NCA3. The chapter also illustrates the complex and interrelated nature of climate change impacts to biodiversity, ecosystems, and the services they provide.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH7","usgsCitation":"Lipton, D., Rubenstein, M.A., Weiskopf, S.R., Carter, S.L., Peterson, J., Crozier, L., Fogarty, M., Gaichas, S., Hyde, K., Morelli, T.L., Morisette, J., Moustahfid, H., Munoz, R., Poudel, R., Staudinger, M., Stock, C., Thompson, L., Waples, R.S., and Weltzin, J., 2018, Ecosystems, Ecosystem Services, and Biodiversity, 54 p., https://doi.org/10.7930/NCA4.2018.CH7.","productDescription":"54 p.","startPage":"268","endPage":"321","ipdsId":"IP-103827","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":360911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755817,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. 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Because of this vast spatial extent and the strong role that land management plays in how agricultural ecosystems function, agricultural lands and activities represent a large portion of the North American carbon budget. Accordingly, improved quantification of the agricultural carbon cycle, new trends in agriculture, and added opportunities for emissions reductions provide a critical foundation for considering the relationships between agriculture and carbon cycling at local, regional, continental, and global scales. More than 145 countries have specifically included agriculture in their targets and actions for mitigating climate change (FAO 2016), and agriculture has featured particularly prominently in recent target and action commitments made by developing countries to reduce greenhouse gas (GHG) emissions (Richards et al., 2015).
Conversion of vast native forest and prairie to agriculture across North America between 1860 and 1960 resulted in carbon dioxide (CO2) fluxes to the atmosphere from biota and soils that exceeded those from fossil fuel emissions over the same period (Houghton et al., 1983). Correspondingly, soil organic carbon (SOC) declined in many soils during the 50 years following conversion from native ecosystems to production agriculture (Huggins et al., 1998; Janzen et al., 1998; Slobodian et al., 2002). Crop yields and corresponding above- and belowground biomass have steadily increased since the 1930s due to genetic and management innovations, which provide more organic input from which to build SOC ( Johnson et al., 2006; Hatfield and Walthall 2015). This, coupled with improved input-use efficiencies may reduce GHG-emissions per unit yield (GHG intensity), with additional improvements possible through management optimization (Grassini and Cassman 2012; Pittelkow et al., 2015). Options include reducing tillage, integrating perennials onto the landscape, reducing or eliminating bare-fallow land (i.e., land without living plants), adding cover crops, and enrolling lands in conservation easement programs. These options, originally proposed to control erosion, have potential co-benefits in terms of increased soil health, plant productivity, and soil carbon stabilization (Lehman et al., 2015). Conversely, returning lands previously enrolled in conservation easements (e.g., the Conservation Reserve Program [CRP] and other land set-aside efforts) to row-crop production, tillage, or aggressive harvesting of crop residues all risk degrading soil quality and exacerbating SOC loss. Of note is that the net results of land use and land management practices in an agricultural setting vary according to many factors, such as crop or production system type, soil type, climate, and the collection of practices at any given site. For example, many traditional practices followed by Indigenous people on tribal lands are based on an integrated approach to natural resource management and response to environmental change that may provide agricultural options uniquely suited to varied environmental settings (see Ch. 7: Tribal Lands, p. 303).
Agricultural land in the United States totaled 408.2 million hectares (ha) in 2014, of which 251 million ha were in permanent meadows and pastures, 152.2 million ha were in arable land, and 2.6 million ha were in permanent crops (FAOSTAT 2016). Compared with the distribution in 2007, these numbers reflect a 4.7 million ha decline in total agricultural lands, driven by declines in arable land and permanent crops but partially offset by a modest increase in permanent meadows and pastures. Although arable lands have been declining, the combined acreage of the four major crops (corn, wheat, soybeans, and cotton) has risen slightly, with increases in land planted in corn and soybeans and decreases in cotton and wheat (see Figure 5.1, p. 232). Despite the overall slight decline in agricultural land area, the value of U.S. agricultural production rose over the past decade as a result of increased production efficiency and higher prices (USDA 2017a; see also www.ers.usda.gov). Canada has about 65 million ha of agricultural land, of which about 46 million ha are arable, accounting for only about 7% of the country’s total land area (FAOSTAT 2017). Prominent crops on Canada’s arable lands include cereals (e.g., wheat, barley, and maize), oilseeds (e.g., canola and soybeans), and pulses (e.g., peas and lentils). Natural and seeded pastures available for grazing in Canada make up about 20 million ha (Legesse et al., 2016). Agricultural land in Mexico makes up 107 million ha, of which 23 million ha are arable land, 2.7 million ha are permanent crops, and 81 million ha are permanent meadows and pastures (FAOSTAT 2017). Mexico’s major crops are fruits, corn, grains, vegetables, and sugarcane.
","largerWorkTitle":"Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report","language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/SOCCR2.2018.Ch5","usgsCitation":"Hristov, A.N., Johnson, J.M., Rice, C.W., Brown, M.E., Conant, R.T., Del Grosso, S.J., Gurwick, N.P., Rotz, C., Sainju, U.M., Skinner, R.H., West, T.O., Runkle, B.R., Janzen, H., Reed, S.C., Cavallaro, N., and Shrestha, G., 2018, Agriculture, 35 p., https://doi.org/10.7930/SOCCR2.2018.Ch5.","productDescription":"35 p.","startPage":"229","endPage":"263","ipdsId":"IP-088978","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":361017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Cavallaro, Nancy","contributorId":212784,"corporation":false,"usgs":false,"family":"Cavallaro","given":"Nancy","email":"","affiliations":[{"id":38681,"text":"USDA National Institute of Food and Agriculture","active":true,"usgs":false}],"preferred":false,"id":756665,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Shrestha, Gyami","contributorId":145521,"corporation":false,"usgs":false,"family":"Shrestha","given":"Gyami","email":"","affiliations":[],"preferred":false,"id":756666,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Birdsey, Richard","contributorId":210640,"corporation":false,"usgs":false,"family":"Birdsey","given":"Richard","affiliations":[{"id":25456,"text":"Woods Hole Research Center, Falmouth, MA, United States","active":true,"usgs":false}],"preferred":false,"id":756667,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Mayes, Melanie A.","contributorId":212782,"corporation":false,"usgs":false,"family":"Mayes","given":"Melanie","email":"","middleInitial":"A.","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":756668,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Najjar, Raymond G.","contributorId":168568,"corporation":false,"usgs":false,"family":"Najjar","given":"Raymond G.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":756669,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":756670,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Romero-Lankao, Patricia","contributorId":212783,"corporation":false,"usgs":false,"family":"Romero-Lankao","given":"Patricia","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":756671,"contributorType":{"id":2,"text":"Editors"},"rank":7},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":756672,"contributorType":{"id":2,"text":"Editors"},"rank":8}],"authors":[{"text":"Hristov, Alexander N.","contributorId":81334,"corporation":false,"usgs":false,"family":"Hristov","given":"Alexander","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":756545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Jane M. F.","contributorId":212804,"corporation":false,"usgs":false,"family":"Johnson","given":"Jane","email":"","middleInitial":"M. F.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":756650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rice, Charles W.","contributorId":212805,"corporation":false,"usgs":false,"family":"Rice","given":"Charles","email":"","middleInitial":"W.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":756651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Molly E.","contributorId":62490,"corporation":false,"usgs":true,"family":"Brown","given":"Molly","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":756652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conant, Richard T.","contributorId":207107,"corporation":false,"usgs":false,"family":"Conant","given":"Richard","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":756653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Del Grosso, Stephen J.","contributorId":145477,"corporation":false,"usgs":false,"family":"Del Grosso","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":16129,"text":"Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA","active":true,"usgs":false}],"preferred":false,"id":756654,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gurwick, Noel P.","contributorId":212818,"corporation":false,"usgs":false,"family":"Gurwick","given":"Noel","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":756655,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rotz, C. Alan","contributorId":212819,"corporation":false,"usgs":false,"family":"Rotz","given":"C. Alan","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":756656,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sainju, Upendra M.","contributorId":212820,"corporation":false,"usgs":false,"family":"Sainju","given":"Upendra","email":"","middleInitial":"M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":756657,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Skinner, R. Howard","contributorId":146142,"corporation":false,"usgs":false,"family":"Skinner","given":"R.","email":"","middleInitial":"Howard","affiliations":[{"id":16601,"text":"USDA-ARS, Pasture Systems and Watershed Management Unit","active":true,"usgs":false}],"preferred":false,"id":756658,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"West, Tristram O.","contributorId":39230,"corporation":false,"usgs":true,"family":"West","given":"Tristram","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":756659,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Runkle, Benjamin R. K.","contributorId":196373,"corporation":false,"usgs":false,"family":"Runkle","given":"Benjamin","email":"","middleInitial":"R. K.","affiliations":[],"preferred":false,"id":756660,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Janzen, Henry","contributorId":212821,"corporation":false,"usgs":false,"family":"Janzen","given":"Henry","email":"","affiliations":[{"id":24491,"text":"Agriculture and Agri-Food Canada","active":true,"usgs":false}],"preferred":false,"id":756661,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":756662,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Cavallaro, Nancy","contributorId":212784,"corporation":false,"usgs":false,"family":"Cavallaro","given":"Nancy","email":"","affiliations":[{"id":38681,"text":"USDA National Institute of Food and Agriculture","active":true,"usgs":false}],"preferred":false,"id":756663,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shrestha, Gyami","contributorId":145521,"corporation":false,"usgs":false,"family":"Shrestha","given":"Gyami","email":"","affiliations":[],"preferred":false,"id":756664,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70202028,"text":"70202028 - 2018 - Groundwater modeling","interactions":[],"lastModifiedDate":"2019-02-07T10:45:21","indexId":"70202028","displayToPublicDate":"2019-01-01T10:45:06","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Groundwater modeling","docAbstract":"The state of the science and practice in groundwater modeling brings to mind highly sophisticated computer models that are running in parallel on many multi-processor machines. These models are expected to incorporate many different processes of both saturated and unsaturated groundwater flow and transport and possibly the media to which it connects, like surface waters and the atmosphere. We are increasingly aware we cannot study groundwater flow in isolation if we are to make useful predictions of, for instance, the impacts of climate change on the groundwater regime. We have come a long way.
Today we are no longer limited to equations for flow toward a well, perhaps near an infinitely long straight canal (method of images), to sandbox models in the laboratory, or to simple steady state models of flow in a single aquifer. We now have computer models that solve groundwater flow and transport in multi-aquifer settings under transient conditions and with a user-friendly graphical user interface that allows widespread use. Additionally, multi-media models are now leaving the research environment and becoming available to mainstream consultants. So in that sense the science of groundwater modeling has matured.
The practice of groundwater modeling, however, has also matured. We have come to realize that model output, being a necessary simplification of an unknowably complex natural world, has inherent limitations. That is, a model of reality is not reality itself. There is uncertainty associated with all facets of our model—parameterization, aquifer geometry and discretization, boundary conditions, and future hydrologic drivers such as future pumping regimes and climates. Today a model is now more appropriately seen as a tool that provides a quantitative framework to make supportable forecasts rather than an oracle that gives us all the answers.
In this chapter we set out to briefly review the state of the science and practice in modeling. In doing so, we augment existing assessments from the journal Groundwater (e.g., Hunt and Zheng 2012; Langevin and Panday 2012; Molz 2017a,b; White 2017), specifically in terms of modeling approach. An effective modeling approach is critical. If a modeler does not decompose the societal problem correctly, the model will not be fit-for-purpose, no matter how sophisticated the code’s capabilities. Moreover, capabilities of codes will be ever improving; good modeling practices have a timelessness that is more robust.
How best to decompose the problem and provide models that are accepted? We lay out here some approaches for today’s applied groundwater modeling. Specifically, we suggest: (1) a step-wise modeling process; (2) including a two-dimensional areal model within this process; (3) keeping abreast of industry standards; and (4) ways to increase acceptance of the models we produce.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Groundwater: State of the science and practice","language":"English","publisher":"National Groundwater Association","isbn":"1-56034-047-9","usgsCitation":"Haitjema, H.M., and Hunt, R., 2018, Groundwater modeling, chap. of Groundwater: State of the science and practice, p. 41-46.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-101055","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361067,"type":{"id":15,"text":"Index Page"},"url":"https://groundwatersolutionsgroup.com/wp-content/uploads/2018/12/Science-and-Practice_10.17_FINAL.pdf#page=45"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haitjema, Henk M.","contributorId":74678,"corporation":false,"usgs":true,"family":"Haitjema","given":"Henk","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":756765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":208800,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[],"preferred":true,"id":756764,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201875,"text":"70201875 - 2018 - Southeast","interactions":[],"lastModifiedDate":"2019-02-01T10:40:51","indexId":"70201875","displayToPublicDate":"2019-01-01T10:40:46","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Southeast","docAbstract":"The Southeast includes vast expanses of coastal and inland low-lying areas, the southern portion of the Appalachian Mountains, numerous high-growth metropolitan areas, and large rural expanses. These beaches and bayous, fields and forests, and cities and small towns are all at risk from a changing climate. While some climate change impacts, such as sea level rise and extreme downpours, are being acutely felt now, others, like increasing exposure to dangerous high temperatures, humidity, and new local diseases, are expected to become more significant in the coming decades. While all regional residents and communities are potentially at risk for some impacts, some communities or populations are at greater risk due to their locations, services available to them, and economic situations.
Observed warming since the mid-20th century has been uneven in the Southeast region, with average daily minimum temperatures increasing three times faster than average daily maximum temperatures. The number of extreme rainfall events is increasing. Climate model simulations of future conditions project increases in both temperature and extreme precipitation.
Trends towards a more urbanized and denser Southeast are expected to continue, creating new climate vulnerabilities. Cities across the Southeast are experiencing more and longer summer heat waves. Vector-borne diseases pose a greater risk in cities than in rural areas because of higher population densities and other human factors, and the major urban centers in the Southeast are already impacted by poor air quality during warmer months. Increasing precipitation and extreme weather events will likely impact roads, freight rail, and passenger rail, which will likely have cascading effects across the region. Infrastructure related to drinking water and wastewater treatment also has the potential to be compromised by climate-related events. Increases in extreme rainfall events and high tide coastal floods due to future climate change will impact the quality of life of permanent residents as well as tourists visiting the low-lying and coastal regions of the Southeast. Sea level rise is contributing to increased coastal flooding in the Southeast, and high tide flooding already poses daily risks to businesses, neighborhoods, infrastructure, transportation, and ecosystems in the region. There have been numerous instances of intense rainfall events that have had devastating impacts on inland communities in recent years.
The ecological resources that people depend on for livelihoods, protection, and well-being are increasingly at risk from the impacts of climate change. Sea level rise will result in the rapid conversion of coastal, terrestrial, and freshwater ecosystems to tidal saline habitats. Reductions in the frequency and intensity of cold winter temperature extremes are already allowing tropical and subtropical species to move northward and replace more temperate species. Warmer winter temperatures are also expected to facilitate the northward movement of problematic invasive species, which could transform natural systems north of their current distribution. In the future, rising temperatures and increases in the duration and intensity of drought are expected to increase wildfire occurrence and also reduce the effectiveness of prescribed fire practices.
Many in rural communities are maintaining connections to traditional livelihoods and relying on natural resources that are inherently vulnerable to climate changes. Climate trends and possible climate futures show patterns that are already impacting—and are projected to further impact—rural sectors, from agriculture and forestry to human health and labor productivity. Future temperature increases are projected to pose challenges to human health. Increases in temperatures, water stress, freeze-free days, drought, and wildfire risks, together with changing conditions for invasive species and the movement of diseases, create a number of potential risks for existing agricultural systems. Rural communities tend to be more vulnerable to these changes due to factors such as demography, occupations, earnings, literacy, and poverty incidence. In fact, a recent economic study using a higher scenario (RCP8.5) suggests that the southern and midwestern populations are likely to suffer the largest losses from future climate changes in the United States. Climate change tends to compound existing vulnerabilities and exacerbate existing inequities. Already poor regions, including those found in the Southeast, are expected to continue incurring greater losses than elsewhere in the United States.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH19","usgsCitation":"Carter, L., Terando, A.J., Dow, K., Hiers, K., Kunkel, K.E., Lascurain, A.R., Marcy, D., Osland, M.J., and Schramm, P., 2018, Southeast, 66 p., https://doi.org/10.7930/NCA4.2018.CH19.","productDescription":"66 p.","startPage":"743","endPage":"808","ipdsId":"IP-103837","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":468164,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7930/nca4.2018.ch19","text":"Publisher Index Page"},{"id":360910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755810,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. 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C.","contributorId":212246,"corporation":false,"usgs":false,"family":"Stewart","given":"B.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":755816,"contributorType":{"id":2,"text":"Editors"},"rank":7}],"authors":[{"text":"Carter, Lynne","contributorId":212191,"corporation":false,"usgs":false,"family":"Carter","given":"Lynne","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":755695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":755696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dow, Kirstin 0000-0002-4547-5566","orcid":"https://orcid.org/0000-0002-4547-5566","contributorId":212192,"corporation":false,"usgs":false,"family":"Dow","given":"Kirstin","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":755697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hiers, Kevin","contributorId":212193,"corporation":false,"usgs":false,"family":"Hiers","given":"Kevin","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":755698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kunkel, Kenneth E.","contributorId":147887,"corporation":false,"usgs":false,"family":"Kunkel","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":755699,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lascurain, Aranzazu R.","contributorId":173919,"corporation":false,"usgs":false,"family":"Lascurain","given":"Aranzazu","email":"","middleInitial":"R.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":755700,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marcy, Doug","contributorId":212194,"corporation":false,"usgs":false,"family":"Marcy","given":"Doug","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":755701,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Osland, Michael J. 0000-0001-9902-8692 mosland@usgs.gov","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":3080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","email":"mosland@usgs.gov","middleInitial":"J.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":755702,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schramm, Paul","contributorId":212195,"corporation":false,"usgs":false,"family":"Schramm","given":"Paul","email":"","affiliations":[{"id":27265,"text":"Centers for Disease Control and Prevention","active":true,"usgs":false}],"preferred":false,"id":755703,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70201876,"text":"70201876 - 2018 - U.S. Caribbean","interactions":[],"lastModifiedDate":"2019-02-01T10:37:14","indexId":"70201876","displayToPublicDate":"2019-01-01T10:37:01","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"U.S. Caribbean","docAbstract":"Historically, the U.S. Caribbean region has experienced relatively stable seasonal rainfall patterns, moderate annual temperature fluctuations, and a variety of extreme weather events, such as tropical storms, hurricanes, and drought. However, the Caribbean climate is changing and is projected to be increasingly variable as levels of greenhouse gases in the atmosphere increase.
The high percentage of coastal area relative to the total island land area in the U.S. Caribbean means that a large proportion of the region’s people, infrastructure, and economic activity are vulnerable to sea level rise, more frequent intense rainfall events and associated coastal flooding, and saltwater intrusion. High levels of exposure and sensitivity to risk in the U.S. Caribbean region are compounded by a low level of adaptive capacity, due in part to the high costs of mitigation and adaptation measures relative to the region’s gross domestic product, particularly when compared to continental U.S. coastal areas. The limited geographic and economic scale of Caribbean islands means that disruptions from extreme climate-related events, such as droughts and hurricanes, can devastate large portions of local economies and cause widespread damage to crops, water supplies, infrastructure, and other critical resources and services.
The U.S. Caribbean territories of Puerto Rico and the U.S. Virgin Islands (USVI) have distinct differences in topography, language, population size, governance, natural and human resources, and economic capacity. However, both are highly dependent on natural and built coastal assets; service-related industries account for more than 60% of the USVI economy. Beaches, affected by sea level rise and erosion, are among the main tourist attractions. In Puerto Rico, critical infrastructure (for example, drinking water pipelines and pump stations, sanitary pipelines and pump stations, wastewater treatment plants, and power plants) is vulnerable to the effects of sea level rise, storm surge, and flooding. In the USVI, infrastructure and historical buildings in the inundation zone for sea level rise include the power plants on both St. Thomas and St. Croix; schools; housing communities; the towns of Charlotte Amalie, Christiansted, and Frederiksted; and pipelines for water and sewage.
Climate change will likely result in water shortages due to an overall decrease in annual rainfall, a reduction in ecosystem services, and increased risks for agriculture, human health, wildlife, and socioeconomic development in the U.S. Caribbean. These shortages would result from some locations within the Caribbean experiencing longer dry seasons and shorter, but wetter, wet seasons in the future. Extended dry seasons are projected to increase fire likelihood. Excessive rainfall, coupled with poor construction practices, unpaved roads, and steep slopes, can exacerbate erosion rates and have adverse effects on reservoir capacity, water quality, and nearshore marine habitats.
Ocean warming poses a significant threat to the survival of corals and will likely also cause shifts in associated habitats that compose the coral reef ecosystem. Severe, repeated, or prolonged periods of high temperatures leading to extended coral bleaching can result in colony death. Ocean acidification also is likely to diminish the structural integrity of coral habitats. Studies show that major shifts in fisheries distribution and changes to the structure and composition of marine habitats adversely affect food security, shoreline protection, and economies throughout the Caribbean.
In Puerto Rico, the annual number of days with temperatures above 90°F has increased over the last four and a half decades. During that period, stroke and cardiovascular disease, which are influenced by such elevated temperatures, became the primary causes of death. Increases in average temperature and in extreme heat events will likely have detrimental effects on agricultural operations throughout the U.S. Caribbean region. Many farmers in the tropics, including the U.S. Caribbean, are considered small-holding, limited resource farmers and often lack the resources and/or capital to adapt to changing conditions.
Most Caribbean countries and territories share the need to assess risks, enable actions across scales, and assess changes in ecosystemsto inform decision-making on habitat protection under a changing climate. U.S. Caribbean islands have the potential to improve adaptation and mitigation actions by fostering stronger collaborations with Caribbean initiatives on climate change and disaster risk reduction.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH20","usgsCitation":"Gould, W.A., Diaz, E.L., Alvarez-Berrios, N.L., Aponte-Gonzalez, F., Archibald, W., Bowden, J.H., Carrubba, L., Crespo, W., Fain, S.J., Gonzalez, G., Goulbourne, A., Harmsen, E., Holupchinski, E., Khalyani, A.H., Kossin, J.P., Leinberger, A.J., Marrero-Santiago, V.I., Martinez-Sanchez, O., McGinley, K., Mendez-Lazaro, P., Morrell, J., Melendez Oyola, M., Pares-Ramos, I.K., Pulwarty, R., Sweet, W.V., Terando, A.J., and Torres-González, S., 2018, U.S. Caribbean, 63 p., https://doi.org/10.7930/NCA4.2018.CH20.","productDescription":"63 p.","startPage":"809","endPage":"871","ipdsId":"IP-103838","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":468165,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7930/nca4.2018.ch20","text":"Publisher Index Page"},{"id":360909,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755803,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. 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Overall, climate projections suggest that the number of heavy precipitation events (events with greater than 1 inch per day of rainfall) is projected to increase. Moving forward, the magnitude of year-to-year variability overshadows the small projected average decrease in streamflow. Changes in extreme events are likely to overwhelm average changes in both the eastern and western regions of the Northern Great Plains. Major flooding across the basin in 2011 was followed by severe drought in 2012, representing new and unprecedented variability that is likely to become more common in a warmer world.
The Northern Great Plains region plays a critical role in national food security. Among other anticipated changes, projected warmer and generally wetter conditions with elevated atmospheric carbon dioxide concentrations are expected to increase the abundance and competitive ability of weeds and invasive species, increase livestock production and efficiency of production, and result in longer growing seasons at mid- and high latitudes. Net primary productivity, including crop yields and forage production, is also likely to increase, although an increasing number of extreme temperature events during critical pollination and grain fill periods is likely to reduce crop yields.
Ecosystems across the Northern Great Plains provide recreational opportunities and other valuable goods and services that are ingrained in the region’s cultures. Higher temperatures, reduced snow cover, and more variable precipitation will make it increasingly challenging to manage the region’s valuable wetlands, rivers, and snow-dependent ecosystems. In the mountains of western Wyoming and western Montana, the fraction of total water in precipitation that falls as snow is expected to decline by 25% to 40% by 2100 under a higher scenario (RCP8.5), which would negatively affect the region’s winter recreation industry. At lower-elevation areas of the Northern Great Plains, climate-induced land-use changes in agriculture can have cascading effects on closely entwined natural ecosystems, such as wetlands, and the diverse species and recreational opportunities they support.
Energy resources in the Northern Great Plains include abundant crude oil, natural gas, coal, wind, and stored water, and to a lesser extent, corn-based ethanol, solar energy, and uranium. The infrastructure associated with the extraction, distribution, and energy produced from these resources is vulnerable to the impacts of climate change. Railroads and pipelines are vulnerable to damage or disruption from increasing heavy precipitation events and associated flooding and erosion. Declining water availability in the summer would likely increase costs for oil production operations, which require freshwater resources. These cost increases will either lead to lower production or be passed on to consumers. Finally, higher maximum temperatures, longer and more severe heat waves, and higher overnight lows are expected to increase electricity demand for cooling in the summer, further stressing the power grid.
Indigenous peoples in the region are observing changes to climate, many of which are impacting livelihoods as well as traditional subsistence and wild foods, wildlife, plants and water for ceremonies, medicines, and health and well-being. Because some tribes and Indigenous peoples are among those in the region with the highest rates of poverty and unemployment, and because many are still directly reliant on natural resources, they are among the most at risk to climate change (e.g., Gamble et al. 2016, Cozzetto et al. 2013, Espey et al. 2014, Wong et al. 2014, Kornfeld 2016, Paul and Caplins 2016, Maynard 2014, USGCRP 2017)
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/NCA4.2018.CH22","usgsCitation":"Conant, R.T., Kluck, D., Anderson, M.T., Badger, A., Boustead, B.M., Derner, J.D., Farris, L., Hayes, M., Livneh, B., McNeeley, S., Peck, D., Shulski, M., and Small, V., 2018, Northern Great Plains, 26 p., https://doi.org/10.7930/NCA4.2018.CH22.","productDescription":"26 p.","startPage":"941","endPage":"986","ipdsId":"IP-103839","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"links":[{"id":468166,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7930/nca4.2018.ch22","text":"Publisher Index Page"},{"id":360908,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Reidmiller, David 0000-0001-9321-7548","orcid":"https://orcid.org/0000-0001-9321-7548","contributorId":212241,"corporation":false,"usgs":true,"family":"Reidmiller","given":"David","email":"","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":755796,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Avery, C. W.","contributorId":212242,"corporation":false,"usgs":false,"family":"Avery","given":"C.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":755797,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Easterling, D. R.","contributorId":212243,"corporation":false,"usgs":false,"family":"Easterling","given":"D.","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":755798,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Kunkel, K. E.","contributorId":83626,"corporation":false,"usgs":true,"family":"Kunkel","given":"K.","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":755799,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Lewis, K. L. M.","contributorId":212244,"corporation":false,"usgs":false,"family":"Lewis","given":"K.","email":"","middleInitial":"L. M.","affiliations":[],"preferred":false,"id":755800,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"Maycock, T. K.","contributorId":212245,"corporation":false,"usgs":false,"family":"Maycock","given":"T.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":755801,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Stewart, B. C.","contributorId":212246,"corporation":false,"usgs":false,"family":"Stewart","given":"B.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":755802,"contributorType":{"id":2,"text":"Editors"},"rank":7}],"authors":[{"text":"Conant, Richard T.","contributorId":207107,"corporation":false,"usgs":false,"family":"Conant","given":"Richard","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":755731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kluck, Doug 0000-0002-9698-7991","orcid":"https://orcid.org/0000-0002-9698-7991","contributorId":212219,"corporation":false,"usgs":false,"family":"Kluck","given":"Doug","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":755732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Mark T. 0000-0002-1477-6788 manders@usgs.gov","orcid":"https://orcid.org/0000-0002-1477-6788","contributorId":1764,"corporation":false,"usgs":true,"family":"Anderson","given":"Mark","email":"manders@usgs.gov","middleInitial":"T.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":755733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Badger, Andrew 0000-0003-4537-9993","orcid":"https://orcid.org/0000-0003-4537-9993","contributorId":212220,"corporation":false,"usgs":false,"family":"Badger","given":"Andrew","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":755734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boustead, Barbara M. 0000-0002-0230-7001","orcid":"https://orcid.org/0000-0002-0230-7001","contributorId":212221,"corporation":false,"usgs":false,"family":"Boustead","given":"Barbara","email":"","middleInitial":"M.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":755735,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Derner, Justin D.","contributorId":195928,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":755736,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Farris, Laura","contributorId":212222,"corporation":false,"usgs":false,"family":"Farris","given":"Laura","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":755737,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hayes, Michael","contributorId":192358,"corporation":false,"usgs":false,"family":"Hayes","given":"Michael","affiliations":[],"preferred":false,"id":755738,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Livneh, Ben","contributorId":145804,"corporation":false,"usgs":false,"family":"Livneh","given":"Ben","email":"","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":755739,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McNeeley, Shannon","contributorId":202840,"corporation":false,"usgs":false,"family":"McNeeley","given":"Shannon","affiliations":[],"preferred":false,"id":755740,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Peck, Dannele","contributorId":202842,"corporation":false,"usgs":false,"family":"Peck","given":"Dannele","affiliations":[],"preferred":false,"id":755741,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shulski, Martha","contributorId":212223,"corporation":false,"usgs":false,"family":"Shulski","given":"Martha","email":"","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":755742,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Small, Valerie","contributorId":212224,"corporation":false,"usgs":false,"family":"Small","given":"Valerie","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":755743,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70202197,"text":"70202197 - 2018 - The tectonically controlled San Gabriel Channel–Lobe Transition Zone, Catalina Basin, Southern California Borderland","interactions":[],"lastModifiedDate":"2019-02-14T10:24:22","indexId":"70202197","displayToPublicDate":"2019-01-01T10:24:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"The tectonically controlled San Gabriel Channel–Lobe Transition Zone, Catalina Basin, Southern California Borderland","docAbstract":"High-resolution geophysical data across the Catalina Basin, offshore southern California, USA, reveal a complex channel–lobe transition zone (CLTZ) and provide an opportunity to characterize an entire seafloor CLTZ in a tectonically active and confined-basin setting. The seafloor morphology, distribution of depositional and erosional features, and location of depocenters in the CLTZ are controlled by shifting confinement and seafloor gradient related to inherited basement structures, active faults, and basin margins. Below a Holocene hemipelagic drape, the Catalina Basin is dominated by CLTZ and lobe sedimentation from the San Gabriel Channel, with lesser accumulations from local sediment sources limited to basin margins. The San Gabriel Channel is structurally confined as it enters the Catalina Basin and appears unable to avulse; it continues into the basin as a channel that rapidly widens, decreases in relief, and becomes scoured at its margins. A CLTZ is imaged between the confined San Gabriel channel and its terminal lobes deposited > 50 km into the basin. Narrow, apparently disconnected channels with knickpoints occur throughout the proximal and mid-CLTZ and are concentrated near basement highs and basin-bounding Quaternary-active dextral strike-slip faults. A field of small-scale erosional crescent-shaped scours (∼ 100 m length, ∼ 200 m width, up to ∼ 10 m relief across ∼ 30 km2region) occurs above a partially buried basement high that creates perturbations in seafloor gradient. Likewise, above a buried basement structure that locally increases seafloor gradient (up to 0.4°), the distal CLTZ may contain sediment waves (∼ 2–4 m wave height and ∼ 200–300 m wavelength) that are smaller than many other CLTZ examples. This study of the San Gabriel CLTZ in Catalina Basin provides high-resolution geophysical data coverage of a complete CLTZ and illustrates a tectonically controlled end-member CLTZ from the modern seafloor.
","language":"English","publisher":"SEPM","doi":"10.2110/jsr.2018.50","usgsCitation":"Maier, K.L., Roland, E., Walton, M.A., Conrad, J.E., Brothers, D., Dartnell, P., and Kluesner, J., 2018, The tectonically controlled San Gabriel Channel–Lobe Transition Zone, Catalina Basin, Southern California Borderland: Journal of Sedimentary Research, v. 88, no. 8, p. 942-959, https://doi.org/10.2110/jsr.2018.50.","productDescription":"18 p.","startPage":"942","endPage":"959","ipdsId":"IP-094240","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":361244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Catalina Basin, Southern California Borderland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.8333,\n 33\n ],\n [\n -118,\n 33\n ],\n [\n -118,\n 33.5\n ],\n [\n -118.8333,\n 33.5\n ],\n [\n -118.8333,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Maier, Katherine L. 0000-0003-2908-3340 kcoble@usgs.gov","orcid":"https://orcid.org/0000-0003-2908-3340","contributorId":4926,"corporation":false,"usgs":true,"family":"Maier","given":"Katherine","email":"kcoble@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roland, Emily C.","contributorId":147830,"corporation":false,"usgs":false,"family":"Roland","given":"Emily C.","affiliations":[{"id":13254,"text":"University of Washington, School of Oceanography","active":true,"usgs":false}],"preferred":false,"id":757196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton, Maureen A. 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The Spor Mountain Formation (SMF) hosts the deposit, which is the largest known volcanic rock-related Be deposit in the world. Discovery of the Be deposit at Spor Mountain in the 1960s displaced beryl as the main commercial source of beryllium in the global supply chain. Technological advances in mineral processing enabled bertrandite (Be4Si2O7(OH)2) ore of variable grade and composition from Spor Mountain to compete with beryl ore derived from pegmatite. The deposit currently accounts for approximately 85% of the global beryllium mine production.\nThe Be deposit is in the Basin and Range province of North America, which is characterized by Oligocene and Eocene calderas, extensive alkalic rhyolitic lava and ash flow tuffs, widespread uranium and fluorite occurrences, and Precambrian to Paleozoic sedimentary rocks. The SMF consists of a hydrothermally-altered, fluorite-bearing, lithic-rich (clasts of carbonate, quartzite, and older volcanic rocks) pyroclastic tuff (informal name: Be tuff member) that is overlain by altered, porphyritic, and topaz-rich rhyolite (alkali rhyolite member). The tuff encloses elongate mineralized layers containing numerous nodules that consist of calcite, chalcedony, opal, fluorite, and bertrandite (Be4Si2O7(OH)2, the main ore mineral.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Magmatism of the Earth and related Strategic Metal Deposits","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Conference on Magmatism of the Earth and Related Strategic Metal Deposits","conferenceDate":"September 3-7, 2018","conferenceLocation":"Moscow, Russia","language":"English","usgsCitation":"Foley, N.K., and Ayuso, R.A., 2018, Shrimp U-Pb zircon and opal geochronology, isotope geochemistry, and genesis of the super large Be deposit at Spor Mountain, Utah, USA, in Magmatism of the Earth and related Strategic Metal Deposits, Moscow, Russia, September 3-7, 2018, p. 90-94.","productDescription":"5 p.","startPage":"90","endPage":"94","ipdsId":"IP-096788","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":369941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":369940,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://magmas-and-metals.ru"}],"country":"United States","state":"Utah","otherGeospatial":"Spor Mountain Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.22200775146483,\n 39.69463958513244\n ],\n [\n -113.14922332763672,\n 39.69463958513244\n ],\n [\n -113.14922332763672,\n 39.76738084178371\n ],\n [\n -113.22200775146483,\n 39.76738084178371\n ],\n [\n -113.22200775146483,\n 39.69463958513244\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Foley, Nora K. 0000-0003-0124-3509 nfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-0124-3509","contributorId":4010,"corporation":false,"usgs":true,"family":"Foley","given":"Nora","email":"nfoley@usgs.gov","middleInitial":"K.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":760095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":760096,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197878,"text":"70197878 - 2018 - Fault displacement hazard for strike-slip faults","interactions":[],"lastModifiedDate":"2019-06-27T16:25:46","indexId":"70197878","displayToPublicDate":"2018-12-31T16:21:11","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Fault displacement hazard for strike-slip faults","docAbstract":"In this paper we summarize data, methods, and models developed for a probabilistic assessment of fault displacement hazards across the U.S. We compare earthquake displacement data and empirical fault displacement models that have been developed for normal faults, strike-slip faults, and reverse faults. In general, the data and models are similar near the center of the fault for the three faulting types, but differ near the ends with the strike-slip data being lower than the reverse and normal faulting data. We also compare these U.S. models with data and equations developed using Japanese fault displacement data. The Japan model is also similar to the U.S. models near the center of the fault but decays less rapidly near the ends of the fault. In addition, we discuss impacts of models developed to analyze off-fault strain on secondary faults, multi-strand displacement hazard, and various mapping quality factors. For our study, we show example fault displacements for a M 7 fault with recurrence of 800 and 1600 years. We conclude that a deterministic assessment of fault displacements is often higher than the probabilistic displacements for less active faults with earthquake rupture recurrence that is longer than the hazard return period of interest. Fault displacement hazard is applied in engineering applications for buildings, bridges, pipelines, and nuclear facilities. We present three applications for fault displacement hazard at nuclear facilities and important structures.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Earthquake Engineering. National Conference. 11TH 2018. (11NCEE) (12 Vols) Integrating Science, Engineering, and Policy","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh U.S. National Conference on Earthquake Engineering","conferenceDate":"June 25-29, 2018","conferenceLocation":"Los Angeles, CA","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Petersen, M.D., and Chen, R., 2018, Fault displacement hazard for strike-slip faults, in Earthquake Engineering. National Conference. 11TH 2018. (11NCEE) (12 Vols) Integrating Science, Engineering, and Policy, v. 3, Los Angeles, CA, June 25-29, 2018, p. 1794-1805.","productDescription":"12 p.","startPage":"1794","endPage":"1805","ipdsId":"IP-096800","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":365131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":738897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Rui","contributorId":187504,"corporation":false,"usgs":false,"family":"Chen","given":"Rui","email":"","affiliations":[],"preferred":false,"id":738898,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196245,"text":"70196245 - 2018 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2017","interactions":[],"lastModifiedDate":"2019-12-30T09:24:17","indexId":"70196245","displayToPublicDate":"2018-12-31T16:20:33","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2017","docAbstract":"This report presents biomass-based summaries of fish communities in western Lake Erie derived from USGS bottom trawl surveys from 2013 to 2017 during June and September. The survey design provided temporal and spatial coverage that does not exist in the interagency trawl database, and thus complemented the August Ohio-Ontario effort to reinforce stock assessments with more robust data. Analyses herein evaluated trends in: total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity and community structure. Data from this effort can be explored interactively online (https://lebs.shinyapps.io/western-basin/), and future analyses will be supported by public data and metadata records available on ScienceBase (https://doi.org/10.5066/F7KK9B1R).","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Compiled reports to the Great Lakes Fishery Commission of the annual bottom trawl and acoustics surveys, 2017","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Keretz, K.R., Kocovsky, P., Kraus, R.T., and Vandergoot, C., 2018, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2017, 11 p.","productDescription":"11 p.","startPage":"106","endPage":"116","ipdsId":"IP-095150","costCenters":[{"id":324,"text":"Great Lakes Science 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The 2017 survey was conducted during September and included transects in Lake Huron’s main basin, Georgian Bay, and North Channel. Mean lake-wide pelagic fish density was 1582 fish/ha and mean pelagic fish biomass was 10.5 kg/ha in 2017, which represents 96% and 93% of the long-term mean respectively. Mean lake-wide biomass was 23% higher in 2017 as compared to 2016. The total estimated lake-wide standing stock biomass of pelagic fish species, excluding cisco, was ~49 kt (± 10.4 kt), consisting almost entirely of bloater (26.8 kt; 55%) and rainbow smelt (22 kt; 45%), with small contributions from sticklebacks (0.13 kt; 0.26 %), emerald shiner (0.09 kt; 0.18%), and alewife (0.004kt; <0.005%). Age-0 rainbow smelt abundance increased from 155 fish/ha in 2016 to 598 fish/ha in 2017. Biomass of age-1+ rainbow smelt increased from 2.5 kg/ha in 2016 to 4.1 kg/ha in 2017. Age-0 bloater abundance increased from 94 fish/ha in 2016 to 342 fish/ha in 2017. Biomass of age-1+ bloater in 2017 (5.0 kg/ha) remained at levels similar to 2016 (5.2 kg/ha). Emerald shiner density decreased from 38.6 fish/ha in 2016 to 19.5 fish/ha in 2017. Emerald shiner biomass remained at 0.02 kg/ha between 2016-2017 which represented 19% of the long-term mean. Cisco lake-wide mean biomass was estimated at 2.2 kg/ha and mean density was estimated at 5.1 fish/ha in 2017. 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California","interactions":[],"lastModifiedDate":"2019-02-20T16:03:11","indexId":"70202297","displayToPublicDate":"2018-12-31T16:03:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3746,"text":"Western North American Naturalist","onlineIssn":"1944-8341","printIssn":"1527-0904","active":true,"publicationSubtype":{"id":10}},"title":"Little islands recording global events: Late Quaternary sea level history and paleozoogeography of Santa Barbara and Anacapa Islands, Channel Islands National Park, California","docAbstract":"Marine terraces are common on the Pacific Coast of North America and record interglacial high-sea stands superimposed on either stable or tectonically rising crustal blocks. Despite many years of study of these landforms in southern California, little work on terraces has been conducted on the two smallest of the California Channel Islands, Santa Barbara Island (SBI) and Anacapa Island (ANA). Presented here are new field and laboratory data on the ages, paleontology, and sea level history of marine terraces of these two islands. On both islands, the lowest marine terraces have shoreline angle elevations of ∼11 m above sea level. Amino acid geochronology shows that terrace deposits on both islands host fossils of two ages, one group dating to the ∼120-ka high-sea stand and the other group likely dating to the ∼100-ka high-sea stand. A mix of fossil ages is consistent with the paleontology as well, with SBI in particular showing a faunal assemblage that includes both extralimital southern and southward-ranging species (inferred to be from the ∼120-ka high-sea stand) and extralimital northern and northward-ranging species (inferred to be from the ∼100-ka high-sea stand). Fossil mixing from these two high-sea stands supports the hypothesis that glacial isostatic adjustment (GIA) processes have left a strong imprint on the geologic record of sea level history in southern California. Nevertheless, the elevations of these terraces and that of a low terrace on Santa Cruz Island indicate that modeled GIA estimates of paleo-sea level for the peak of the last interglacial period at ∼120 ka could be too high. Future development of models of GIA effects on the Pacific Coast of North America will need to consider geologic records, such as those from SBI and ANA, in refining reconstructions of sea level history.
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,{"id":70197879,"text":"70197879 - 2018 - Preliminary 2018 national seismic hazard model for the conterminous United States","interactions":[],"lastModifiedDate":"2019-06-27T16:19:28","indexId":"70197879","displayToPublicDate":"2018-12-31T15:51:06","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary 2018 national seismic hazard model for the conterminous United States","docAbstract":"The 2014 U.S. Geological Survey national seismic hazard model for the conterminous U.S. will be updated in 2018 and 2020 to coincide with the Building Seismic Safety Council’s Project 17 timeline for development of new building code design criteria. The two closely timed updates are planned to allow more time for the Provisions Update Committee to analyze the consequences of the hazard model changes in the design criteria. To prepare the 2018 update we held a workshop (March 7-8, 2018) with scientists and engineers to solicit feedback on the model. The 2018 model will be available for public comment during the summer of 2018. The purpose of this paper is to solicit feedback on the modeling choices and results. The 2018 NSHM considers an updated seismicity catalog and certain key changes in the way ground motions are calculated. First, we implemented new Next Generation Attenuation Relationships for the Central and Eastern North America Region and other published models that allow for the calculation of ground motions at additional periods and site classes in the central and eastern U.S. (CEUS). Second, basin depth terms were implemented in the ground motion models in select regions of the western U.S. (WUS) to account for enhanced long-period ground motions at softer soil sites overlying sedimentary basins. Preliminary results indicate higher ground motions for all periods in parts of the CEUS and for long-periods and soft soils in urban areas overlying sedimentary basins in the WUS.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Earthquake Engineering. National Conference. 11TH 2018. (11NCEE) (12 Vols) Integrating Science, Engineering, and Policy","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Eleventh U.S. National Conference on Earthquake Engineering","conferenceDate":"June 25-29, 2018","conferenceLocation":"Los Angeles, CA","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Petersen, M.D., Shumway, A., Powers, P.M., Mueller, C., Rezaeian, S., Moschetti, M.P., McNamara, D.E., Thompson, E.M., Boyd, O.S., Luco, N., Hoover, S.M., and Rukstales, K.S., 2018, Preliminary 2018 national seismic hazard model for the conterminous United States, in Earthquake Engineering. National Conference. 11TH 2018. (11NCEE) (12 Vols) Integrating Science, Engineering, and Policy, v. 10, Los Angeles, CA, June 25-29, 2018, p. 6005-6015.","productDescription":"11 p.","startPage":"6005","endPage":"6015","ipdsId":"IP-096803","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":365130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science 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rukstales@usgs.gov","orcid":"https://orcid.org/0000-0003-2818-078X","contributorId":775,"corporation":false,"usgs":true,"family":"Rukstales","given":"Kenneth","email":"rukstales@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738910,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70204521,"text":"70204521 - 2018 - Efficacy of injectable tulathromycin for reduction of vertical transmission of Renibacterium salmoninarum in Spring Chinook Salmon Oncorhynchus tshawytscha","interactions":[],"lastModifiedDate":"2019-10-31T15:52:21","indexId":"70204521","displayToPublicDate":"2018-12-31T15:50:53","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"Efficacy of injectable tulathromycin for reduction of vertical transmission of Renibacterium salmoninarum in Spring Chinook Salmon Oncorhynchus tshawytscha","title":"Efficacy of injectable tulathromycin for reduction of vertical transmission of Renibacterium salmoninarum in Spring Chinook Salmon Oncorhynchus tshawytscha","docAbstract":"Bacterial kidney disease (BKD) caused by Renibacterium salmoninarum (Rs) occurs nearly worldwide where wild or cultured salmonid fishes are present. Control of BKD is confounded by its two modes of transmission, horizontal (fish-to-fish) and vertical (from female parent to progeny via the eggs). A highly successful BKD control strategy employed in Pacific Northwest hatcheries culturing spring Chinook salmon (Oncorhynchus tshawytscha) includes: (1) injecting pre-spawning adults with a macrolide antibiotic to improve survival and reduce Rs infection levels, (2) broodstock culling of highly infected females and (3) improved fish husbandry. However, the future availability of the injectable macrolide antibiotic (erythromycin) used for adults is uncertain. This drug shortage has resulted in an urgent need to identify a replacement injectable antibiotic to ensure continued successful control of BKD. The research conducted was intended to provide information for addressing this need via preliminary tests of the safety and efficacy of a new macrolide antibiotic, injectable tulathromycin, which is sold under the trade name DRAXXIN® (Zoetis Animal Health). A long-term goal is to reduce or eliminate the use of antibiotic treatment in spring Chinook salmon hatchery culture. Non-treated females were included in the study to provide empirical data in support of this goal. A subset of pre-spawning spring Chinook salmon at Leavenworth NFH was injected on July 10, 2014 with DRAXXIN at 5 mg per kg body weight (31 fish, left pelvic fin clip). Another subset of females (30 fish, right pelvic fin clip) was left uninjected. The surviving fish (31 DRAXXIN-injected fish and 28 uninjected fish) were spawned between August 18 and September 2, 2014. Although there were apparent trends toward higher pre-spawn survival and lower Rs prevalence and levels for the DRAXXIN-injected females in comparison to the uninjected females, the differences were not statistically significant for any of the Rs assays used (P > 0.05). Based on USFWS enzyme-linked immnosorbent assay (ELISA) test results of kidney tissue samples from the spawning females, egg lots from DRAXXIN-injected and uninjected females were assigned to Rs vertical transmission risk groups (low, medium or high). A subset of 220 eyed eggs from each female was transferred to the Western Fisheries Research Center (USGS) on October 8, 2014, hatched and reared until the study was terminated on September 22, 2015. The study results provided no evidence that DRAXXIN injection of adult female Chinook salmon affected their fecundity, egg eye-up, or survival and growth of progeny fry. There was little evidence of Rs infection in progeny of either DRAXXIN-injected or uninjected females, so the effect of DRAXXIN injection on vertical transmission of Rs could not be assessed. To adequately evaluate the efficacy of DRAXXIN injection for reducing Rs vertical transmission to progeny, additional studies should be conducted with larger numbers of DRAXXIN-injected and uninjected Chinook salmon females with a greater range of Rs levels.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Elliott, D., 2018, Efficacy of injectable tulathromycin for reduction of vertical transmission of Renibacterium salmoninarum in Spring Chinook Salmon Oncorhynchus tshawytscha, 28 p.","productDescription":"28 p.","ipdsId":"IP-100904","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":368850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366054,"type":{"id":11,"text":"Document"},"url":"https://ecos.fws.gov/ServCat/DownloadFile/163944"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott, Diane","contributorId":217727,"corporation":false,"usgs":true,"family":"Elliott","given":"Diane","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":767384,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70197891,"text":"70197891 - 2018 - Developing a global earthquake risk model","interactions":[],"lastModifiedDate":"2019-06-27T15:42:28","indexId":"70197891","displayToPublicDate":"2018-12-31T15:42:15","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Developing a global earthquake risk model","docAbstract":"The understanding of earthquake risk is the first step towards the development and implementation of disaster risk reduction measures. However, in many countries, especially the countries of the developing world, earthquake risk models either do not exist or are publicly inaccessible. The Global Earthquake Model (GEM) Foundation and its partners have been supporting regional programmes and bilateral collaborations to develop a global earthquake risk model, due by the end of 2018. This paper describes how the main components (seismic hazard, exposure models and vulnerability functions) of this global effort are being collected, developed or improved. The calculations are being performed using the OpenQuake-engine, the open-source software for seismic hazard and risk calculations supported by GEM. This model will be able to provide estimates of critical risk metrics such as annualized average economic and human losses or aggregated losses for particular return periods, which are fundamental to the development of efficient and effective mitigation planning.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of 16th European Conference on Earthquake Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"16th European Conference on Earthquake Engineering","conferenceDate":"18-21 June, 2018","conferenceLocation":"Thessaloniki, Greece","language":"English","publisher":"European Association for Earthquake Engineering","usgsCitation":"Silva, V., Crowley, H., Jaiswal, K.S., Acevedo, A.B., Pittore, M., and Journey, M., 2018, Developing a global earthquake risk model, in Proceedings of 16th European Conference on Earthquake Engineering, Thessaloniki, Greece, 18-21 June, 2018, 11834; 11 p.","productDescription":"11834; 11 p.","ipdsId":"IP-094620","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":365128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, Vitor","contributorId":152129,"corporation":false,"usgs":false,"family":"Silva","given":"Vitor","email":"","affiliations":[{"id":18873,"text":"University of Aveiro","active":true,"usgs":false}],"preferred":false,"id":738955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crowley, Helen","contributorId":152131,"corporation":false,"usgs":false,"family":"Crowley","given":"Helen","email":"","affiliations":[{"id":18874,"text":"EUCENTRE","active":true,"usgs":false}],"preferred":false,"id":738956,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":738957,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Acevedo, Ana Beatriz","contributorId":205958,"corporation":false,"usgs":false,"family":"Acevedo","given":"Ana","email":"","middleInitial":"Beatriz","affiliations":[{"id":37198,"text":"Universidad EAFIT","active":true,"usgs":false}],"preferred":false,"id":738958,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pittore, Massimiliano","contributorId":205959,"corporation":false,"usgs":false,"family":"Pittore","given":"Massimiliano","email":"","affiliations":[{"id":27333,"text":"GFZ","active":true,"usgs":false}],"preferred":false,"id":738959,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Journey, Murray","contributorId":205960,"corporation":false,"usgs":false,"family":"Journey","given":"Murray","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":738960,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202971,"text":"70202971 - 2018 - Basin-scale model for predicting marsh edge erosion","interactions":[],"lastModifiedDate":"2019-09-13T11:05:58","indexId":"70202971","displayToPublicDate":"2018-12-31T15:26:53","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Basin-scale model for predicting marsh edge erosion","docAbstract":"Recent attempts to relate marsh edge retreat rate to wave power have met varying levels of success. Schwimmer (2001) correlated wave power to marsh boundary retreat rates over a five-year period along sites within Rehoboth Bay, Delaware, USA. Marani et al. (2011) derived a linear relationship between volumetric retreat rate and mean wave power density using Buckingham’s theorem of dimensional analysis. Leonardi and Fagherazzi (2015) added an exponential function to the Schwimmer (2001) equation to account for variability in soil resistance and mean wave height. These equations factor in soil type, water elevation, vegetation, and macrofauna through field-calibrated empirical constants, i.e., they are not explicitly considered. Consequently, the existing capability of predicting marsh edge erosion rate as a function of wave power and soil and vegetation properties is rather limited for engineering applications. For instance, Allison et al. (2017) show that without taking the marsh platform, soil, and vegetation into account, the relationships between marsh edge erosion rates and wave power on a basin or coastal-wide scale are not strong enough statistically to serve as a useful predictive model. 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