Climate change is thawing and potentially mobilizing vast quantities of organic carbon (OC) previously stored for millennia in permafrost soils of northern circumpolar landscapes. Climate-driven increases in fire and thermokarst may play a key role in OC mobilization by thawing permafrost and promoting transport of OC. Yet, the extent of OC mobilization and mechanisms controlling terrestrial-aquatic transfer are unclear. We demonstrate that hydrologic transport of soil dissolved OC (DOC) from the active layer and thawing permafrost to headwater streams is extremely heterogeneous and regulated by the interactions of soils, seasonal thaw, fire, and thermokarst. Repeated sampling of streams in eight headwater catchments of interior Alaska showed that the mean age of DOC for each stream ranges widely from modern to ∼2,000 years B.P. Together, an endmember mixing model and nonlinear, generalized additive models demonstrated that Δ14C-DOC signature (and mean age) increased from spring to fall, and was proportional to hydrologic contributions from a solute-rich water source, related to presumed deeper flow paths found predominantly in silty catchments. This relationship was correlated with and mediated by catchment properties. Mean DOC ages were older in catchments with >50% burned area, indicating that fire is also an important explanatory variable. These observations underscore the high heterogeneity in aged C export and difficulty of extrapolating estimates of permafrost-derived DOC export from watersheds to larger scales. Our results provide the foundation for developing a conceptual model of permafrost DOC export necessary for advancing understanding and prediction of land-water C exchange in changing boreal landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GB007242","usgsCitation":"Koch, J.C., Bogard, M., Butman, D., Finlay, K., Ebel, B., James, J., Johnston, S.E., Jorgenson, M., Pastick, N., Spencer, R., Striegl, R., Walvoord, M.A., and Wickland, K., 2022, Heterogeneous patterns of aged organic carbon export driven by hydrologic flow paths, soil texture, fire, and thaw in discontinuous permafrost headwaters: Global Biogeochemical Cycles, v. 36, no. 4, e2021GB007242, 16 p., https://doi.org/10.1029/2021GB007242.","productDescription":"e2021GB007242, 16 p.","ipdsId":"IP-134558","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448441,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gb007242","text":"Publisher Index Page"},{"id":398213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151,\n 65\n ],\n [\n -147,\n 65\n ],\n [\n -147,\n 67\n ],\n [\n -151,\n 67\n ],\n [\n -151,\n 65\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":839768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bogard, Matthew","contributorId":272635,"corporation":false,"usgs":false,"family":"Bogard","given":"Matthew","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":839769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butman, David","contributorId":224754,"corporation":false,"usgs":false,"family":"Butman","given":"David","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":839770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Finlay, Kerri","contributorId":289777,"corporation":false,"usgs":false,"family":"Finlay","given":"Kerri","email":"","affiliations":[{"id":27547,"text":"University of Regina","active":true,"usgs":false}],"preferred":false,"id":839771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":839772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"James, Jason","contributorId":289778,"corporation":false,"usgs":false,"family":"James","given":"Jason","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":839773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnston, Sarah Ellen","contributorId":213256,"corporation":false,"usgs":false,"family":"Johnston","given":"Sarah","email":"","middleInitial":"Ellen","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":839774,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jorgenson, M. Torre","contributorId":202940,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M. Torre","affiliations":[{"id":36554,"text":"Ecoscience","active":true,"usgs":false}],"preferred":false,"id":839775,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":839776,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Spencer, Rob 0000-0003-0860-4717","orcid":"https://orcid.org/0000-0003-0860-4717","contributorId":241050,"corporation":false,"usgs":false,"family":"Spencer","given":"Rob","email":"","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":839777,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Striegl, Rob 0000-0002-8251-4659","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":264605,"corporation":false,"usgs":false,"family":"Striegl","given":"Rob","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":839778,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":839779,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":206313,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":839780,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70239283,"text":"70239283 - 2022 - Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions","interactions":[],"lastModifiedDate":"2023-01-06T12:40:06.995804","indexId":"70239283","displayToPublicDate":"2022-03-17T06:34:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions","docAbstract":"Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high-frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady-state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub-daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation-based approach to estimate the sub-daily flux under highly dynamic flow conditions using multi-depth temperature observations at a 5-min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi-depth temperature data at 1- or 2-hr time intervals assuming one-dimensional flow and heat transport governing equation. By comparing estimated fluxes with model-generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation-based flux estimation method was able to capture the vertical sub-daily fluxes using multi-depth high-resolution temperature data alone, even in the presence of multi-dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1-D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.
The estuarine habitat mosaic supports the reproduction, growth, and survival of resident and migratory fish species by providing a diverse portfolio of unique habitats with varying physical and biological features. Global climate change is expected to result in increasing temperatures, rising sea levels, and changes in riverine hydrology, which will have profound effects on the extent and composition of the estuarine habitat mosaic and its associated nursery quality for juvenile fish. We used a spatially explicit bioenergetics model to assess how different climate change scenarios might affect juvenile salmon growth rate potential relative to present day conditions in the Nisqually River Delta, WA, USA. The model indicated that prey-rich habitats such as emergent salt marshes and eelgrass meadows were most likely to facilitate growth, and that reductions in their areal extent and accessibility could have severe consequences for salmon. For instance, unmitigated sea-level rise halved the predicted extent of low- and high-elevation emergent salt marsh, leading to a 30% reduction in end-of-season weights. Increasing water temperatures compounded these effects during the late spring and summer such that the average daily growth rate of an individual fish decreased by an additional 5–50% when compared to the effects of sea-level rise alone. Lethal temperatures (> 24 °C) were infrequently observed, but they were more likely to occur during summer low tides in the mudflat and eelgrass habitats when accessibility to prey-rich marsh was minimal, thereby limiting foraging capacity and the availability of thermal refugia. Our findings indicate that, barring the enactment of targeted management strategies, rising tidal levels and increasing ocean temperatures may reduce the quality of the estuarine habitat mosaic for out-migrating salmon and other sensitive fish species.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-021-01003-3","usgsCitation":"Davis, M.J., Woo, I., Ellings, C.S., Hodgson, S., Beauchamp, D., Nakai, G., and De La Cruz, S.E., 2022, A climate-mediated shift in the estuarine habitat mosaic limits prey availability and reduces nursery quality for juvenile salmon: Estuaries and Coasts, v. 45, p. 1445-1464, https://doi.org/10.1007/s12237-021-01003-3.","productDescription":"20 p.","startPage":"1445","endPage":"1464","ipdsId":"IP-129415","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":397156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta, Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.7385711669922,\n 47.06731569299121\n ],\n [\n -122.73273468017578,\n 47.067900315766245\n ],\n [\n -122.71350860595702,\n 47.06836800936954\n ],\n [\n -122.69496917724608,\n 47.07526601334617\n ],\n [\n -122.67488479614258,\n 47.08239690925263\n ],\n [\n -122.67454147338866,\n 47.11172875008271\n ],\n [\n -122.73822784423828,\n 47.11137826571562\n ],\n [\n -122.7385711669922,\n 47.06731569299121\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ellings, Christopher S.","contributorId":149343,"corporation":false,"usgs":false,"family":"Ellings","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":17711,"text":"Dep't Natural Resources, Nisqually Indian Tribe, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":838147,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hodgson, Sayre","contributorId":172121,"corporation":false,"usgs":false,"family":"Hodgson","given":"Sayre","email":"","affiliations":[{"id":26985,"text":"Nisqually Indian Tribe, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":838148,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":838149,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nakai, Glynnis","contributorId":172123,"corporation":false,"usgs":false,"family":"Nakai","given":"Glynnis","email":"","affiliations":[{"id":26986,"text":"US Fish and Wildlife Service, Nisqually Nat'l Wildlife Refuge, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":838150,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":838151,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231906,"text":"70231906 - 2022 - GW/SW-MST: A groundwater/surface-water method selection tool","interactions":[],"lastModifiedDate":"2022-11-16T16:51:50.333401","indexId":"70231906","displayToPublicDate":"2022-03-16T09:48:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"GW/SW-MST: A groundwater/surface-water method selection tool","docAbstract":"Groundwater/surface-water (GW/SW) exchange and hyporheic processes are topics receiving increasing attention from the hydrologic community. Hydraulic, chemical, temperature, geophysical, and remote sensing methods are used to achieve various goals (e.g., inference of GW/SW exchange, mapping of bed materials, etc.), but the application of these methods is constrained by site conditions such as water depth, specific conductance, bed material, and other factors. Researchers and environmental professionals working on GW/SW problems come from diverse fields and rarely have expertise in all available field methods; hence there is a need for guidance to design field campaigns and select methods that both contribute to study goals and are likely to work under site-specific conditions. Here, we present the spreadsheet-based GW/SW-Method Selection Tool (GW/SW-MST) to help practitioners identify methods for use in GW/SW and hyporheic studies. The GW/SW-MST is a Microsoft Excel-based decision support tool in which the user selects answers to questions about GW/SW-related study goals and site parameters and characteristics. Based on user input, the tool indicates which methods from a toolbox of 32 methods could potentially contribute to achieving the specified goals at the site described.
","language":"English","publisher":"National Groundwater Association (NGWA)","doi":"10.1111/gwat.13194","usgsCitation":"Hammett, S., Day-Lewis, F., Trottier, B.R., Barlow, P.M., Briggs, M., Delin, G.N., Harvey, J., Johnson, C., Lane, J., Rosenberry, D., and Werkema, D.D., 2022, GW/SW-MST: A groundwater/surface-water method selection tool: Groundwater, v. 60, no. 6, p. 784-791, https://doi.org/10.1111/gwat.13194.","productDescription":"8 p.","startPage":"784","endPage":"791","ipdsId":"IP-128682","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448467,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9477975","text":"Publisher Index Page"},{"id":435922,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YFJALF","text":"USGS data release","linkHelpText":"GW/SW-MST: A Groundwater/Surface-Water Method Selection Tool"},{"id":401640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hammett, Steven 0000-0002-6051-966X","orcid":"https://orcid.org/0000-0002-6051-966X","contributorId":292207,"corporation":false,"usgs":false,"family":"Hammett","given":"Steven","email":"","affiliations":[{"id":38050,"text":"Contractor","active":true,"usgs":false}],"preferred":false,"id":844072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844073,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trottier, Brett Russell 0000-0002-6148-0875","orcid":"https://orcid.org/0000-0002-6148-0875","contributorId":291383,"corporation":false,"usgs":true,"family":"Trottier","given":"Brett","email":"","middleInitial":"Russell","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":844080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barlow, Paul M. 0000-0003-4247-6456 pbarlow@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6456","contributorId":1200,"corporation":false,"usgs":true,"family":"Barlow","given":"Paul","email":"pbarlow@usgs.gov","middleInitial":"M.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":844074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":844075,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Delin, Geoffrey N. 0000-0001-7991-6158","orcid":"https://orcid.org/0000-0001-7991-6158","contributorId":224981,"corporation":false,"usgs":true,"family":"Delin","given":"Geoffrey","email":"","middleInitial":"N.","affiliations":[{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":844076,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844077,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Carole D. 0000-0001-6941-1578","orcid":"https://orcid.org/0000-0001-6941-1578","contributorId":245365,"corporation":false,"usgs":true,"family":"Johnson","given":"Carole D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844082,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":844078,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rosenberry, D.O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":38500,"corporation":false,"usgs":true,"family":"Rosenberry","given":"D.O.","affiliations":[],"preferred":true,"id":844079,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Werkema, Dale D.","contributorId":40488,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":844081,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70230900,"text":"70230900 - 2022 - Forest cover lessens the impact of drought on streamflow in Puerto Rico","interactions":[],"lastModifiedDate":"2022-05-13T15:20:23.133171","indexId":"70230900","displayToPublicDate":"2022-03-15T08:56:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Forest cover lessens the impact of drought on streamflow in Puerto Rico","docAbstract":"Tropical regions are experiencing high rates of forest cover loss coupled with changes in the volume and timing of rainfall. These shifts can compromise streamflow and water provision, highlighting the need to identify how forest cover influences streamflow generation under variable rainfall conditions. Although rainfall is the key driver of streamflow regimes, the role of forests is less clear, particularly in tropical regions where forest loss is an ongoing risk. Forest cover loss alters evapotranspiration, rainfall infiltration and storage, and may increase stream ecosystem vulnerability to rainfall extremes. Puerto Rico, an island with spatially heterogenous forest cover and a marked geographic rainfall gradient, is projected to experience more frequent droughts and flash flooding. Using 15-minute streamflow data collected between 2005 and 2016 from 20 USGS stream gages and 3-hourly Multi-Source Weighted-Ensemble Precipitation rainfall estimates, we utilized flow-duration curves and linear mixed regression models to examine the role of forest cover in regulating the timing and volume of streamflow. The mixed model approach helps to account for differences in watershed characteristics. We determined the effects of rainfall and forest cover on low and peak flows in Puerto Rican streams, then evaluated changes in these relationships under dry and wet antecedent rainfall conditions. Watersheds with high forest cover had consistently greater low and peak streamflow than deforested ones under all rainfall conditions, although the effect was more marked during wet antecedent conditions, suggesting that peak flow is largely the result of saturation excess overland flow. During dry antecedent rainfall conditions, highly forested watersheds had higher streamflow than deforested ones, suggesting greater hillslope storage and release may also be at play. Our results demonstrate that forest cover generated a net increase in hillslope infiltration and storage and may lessen drought impacts on streamflow in Puerto Rico. Resilience to prolonged drought may be limited by finite water storage potential in this steep, mountainous setting, highlighting maintenance of forest cover as an important water management strategy to increase infiltration.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14551","usgsCitation":"Hall, J.S., Scholl, M.A., Gorokhovich, Y., and Uriarte, M., 2022, Forest cover lessens the impact of drought on streamflow in Puerto Rico: Hydrological Processes, v. 36, no. 5, e14551, 16 p., https://doi.org/10.1002/hyp.14551.","productDescription":"e14551, 16 p.","ipdsId":"IP-122081","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":399811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-65.3277,18.295843],[-65.337451,18.308308],[-65.327318,18.323666],[-65.342068,18.34529],[-65.335701,18.349535],[-65.329334,18.341955],[-65.321754,18.338316],[-65.309833,18.337973],[-65.304409,18.332054],[-65.298328,18.330529],[-65.255933,18.342117],[-65.221568,18.320959],[-65.222853,18.310464],[-65.249857,18.296691],[-65.260282,18.290823],[-65.283269,18.280214],[-65.3277,18.295843]]],[[[-67.89174,18.11397],[-67.887099,18.112574],[-67.87643,18.114157],[-67.869804,18.118851],[-67.861548,18.122144],[-67.848245,18.10832],[-67.843202,18.094858],[-67.843615,18.085099],[-67.845293,18.081938],[-67.853098,18.078195],[-67.865598,18.06544],[-67.871462,18.0578],[-67.895921,18.052342],[-67.904431,18.05913],[-67.918778,18.063116],[-67.927841,18.068572],[-67.940799,18.079716],[-67.934479,18.111306],[-67.932185,18.113221],[-67.91088,18.119668],[-67.89174,18.11397]]],[[[-65.308717,18.145172],[-65.302295,18.141089],[-65.294896,18.14283],[-65.287962,18.148097],[-65.275165,18.13443],[-65.276214,18.131936],[-65.283248,18.132999],[-65.296036,18.12799],[-65.322794,18.126589],[-65.327184,18.124106],[-65.338506,18.112439],[-65.342037,18.11138],[-65.350493,18.111914],[-65.364733,18.120377],[-65.397837,18.110873],[-65.399791,18.108832],[-65.411767,18.106211],[-65.423765,18.097764],[-65.426311,18.093749],[-65.45138,18.086096],[-65.45681,18.087778],[-65.465849,18.087715],[-65.468768,18.092643],[-65.47979,18.096352],[-65.507265,18.091646],[-65.524209,18.081977],[-65.542087,18.081177],[-65.558646,18.08566],[-65.569305,18.091616],[-65.570628,18.097325],[-65.57686,18.103224],[-65.575579,18.115669],[-65.546199,18.119329],[-65.511712,18.13284],[-65.489829,18.135912],[-65.46791,18.143767],[-65.437058,18.15766],[-65.399517,18.161935],[-65.371373,18.157517],[-65.334289,18.147761],[-65.313476,18.144296],[-65.308717,18.145172]]],[[[-66.438813,18.485713],[-66.420921,18.488639],[-66.410344,18.489886],[-66.394287,18.489748],[-66.377286,18.488044],[-66.37282,18.487726],[-66.349647,18.486335],[-66.337728,18.48562],[-66.315477,18.474724],[-66.31503,18.47468],[-66.291225,18.472347],[-66.283675,18.472203],[-66.276599,18.478129],[-66.269799,18.480281],[-66.258015,18.476906],[-66.251547,18.472464],[-66.241797,18.46874],[-66.220148,18.466],[-66.199032,18.466163],[-66.192664,18.466212],[-66.183886,18.460506],[-66.179218,18.455305],[-66.172315,18.451462],[-66.159796,18.451706],[-66.153037,18.454457],[-66.14395,18.459761],[-66.139572,18.462317],[-66.139451,18.462387],[-66.139443,18.462315],[-66.138532,18.453305],[-66.133085,18.445881],[-66.127938,18.444632],[-66.125198,18.451209],[-66.124284,18.456324],[-66.123188,18.45943],[-66.123343,18.460363],[-66.125015,18.470435],[-66.118338,18.469581],[-66.092098,18.466535],[-66.083254,18.462022],[-66.073987,18.4581],[-66.043272,18.453655],[-66.03944,18.454441],[-66.036559,18.450216],[-66.036491,18.450117],[-66.023221,18.443875],[-66.006523,18.444347],[-65.99718,18.449895],[-65.992935,18.457489],[-65.992793,18.458102],[-65.992349,18.460024],[-65.99079,18.460419],[-65.958492,18.451354],[-65.92567,18.444881],[-65.916843,18.444619],[-65.907756,18.446893],[-65.904988,18.450926],[-65.878683,18.438322],[-65.838825,18.431865],[-65.831476,18.426849],[-65.828457,18.423543],[-65.816691,18.410663],[-65.794556,18.402845],[-65.787666,18.402544],[-65.774937,18.413951],[-65.77053,18.41294],[-65.769749,18.409473],[-65.771695,18.406277],[-65.750455,18.385208],[-65.750179,18.38505],[-65.742154,18.380459],[-65.733567,18.382211],[-65.699069,18.368156],[-65.669636,18.362102],[-65.668845,18.361939],[-65.634431,18.369835],[-65.627246,18.376436],[-65.626527,18.381728],[-65.624975,18.386553],[-65.622761,18.387771],[-65.618229,18.386496],[-65.614891,18.382473],[-65.619068,18.367755],[-65.628198,18.353711],[-65.63419,18.338965],[-65.628047,18.328252],[-65.626456,18.298982],[-65.634389,18.292349],[-65.635826,18.288271],[-65.634893,18.283923],[-65.630833,18.264989],[-65.623111,18.248012],[-65.597618,18.234289],[-65.589947,18.228225],[-65.593795,18.224059],[-65.615981,18.227389],[-65.626731,18.235484],[-65.638181,18.229121],[-65.637565,18.224444],[-65.628414,18.205149],[-65.635281,18.199975],[-65.639688,18.205656],[-65.662185,18.207018],[-65.664127,18.207136],[-65.690749,18.19499],[-65.694515,18.187011],[-65.691021,18.178998],[-65.695856,18.179324],[-65.710895,18.186963],[-65.712533,18.189146],[-65.717999,18.190176],[-65.728471,18.185588],[-65.734664,18.180368],[-65.738834,18.174066],[-65.739125,18.173453],[-65.743632,18.163957],[-65.758728,18.156601],[-65.766919,18.148424],[-65.777584,18.129239],[-65.796711,18.083746],[-65.796289,18.079835],[-65.794686,18.078607],[-65.795028,18.073561],[-65.796711,18.069842],[-65.801831,18.058527],[-65.809174,18.056818],[-65.817107,18.063378],[-65.825848,18.057482],[-65.83109,18.050664],[-65.834274,18.038988],[-65.832429,18.014916],[-65.839591,18.015077],[-65.850913,18.011954],[-65.870335,18.006597],[-65.875122,18.002826],[-65.884937,17.988521],[-65.896102,17.99026],[-65.905319,17.983974],[-65.910537,17.981855],[-65.924738,17.976087],[-65.976611,17.967669],[-65.98455,17.969411],[-65.985358,17.971854],[-65.995185,17.978989],[-66.007731,17.980541],[-66.017308,17.979583],[-66.019539,17.978354],[-66.024,17.975896],[-66.046585,17.954853],[-66.049033,17.954561],[-66.058217,17.959238],[-66.068678,17.966335],[-66.069979,17.966357],[-66.08141,17.966552],[-66.116194,17.949141],[-66.127009,17.946953],[-66.140661,17.94102],[-66.147912,17.933963],[-66.155387,17.929406],[-66.159742,17.928613],[-66.161232,17.931747],[-66.175626,17.933565],[-66.186914,17.935363],[-66.189726,17.933936],[-66.200174,17.929515],[-66.206961,17.932268],[-66.213374,17.944614],[-66.202655,17.944753],[-66.185554,17.940997],[-66.179548,17.943727],[-66.174839,17.948214],[-66.176814,17.950438],[-66.206207,17.96305],[-66.206807,17.963307],[-66.215355,17.959376],[-66.218081,17.95729],[-66.231519,17.943912],[-66.229181,17.934651],[-66.232013,17.931154],[-66.252737,17.934574],[-66.260684,17.936083],[-66.270905,17.947098],[-66.275651,17.94826],[-66.290782,17.946491],[-66.297679,17.959148],[-66.31695,17.976683],[-66.323659,17.978536],[-66.338152,17.976492],[-66.33839,17.976458],[-66.362511,17.968231],[-66.365098,17.964832],[-66.368777,17.957717],[-66.371591,17.951469],[-66.385059,17.939004],[-66.391227,17.945819],[-66.398945,17.950925],[-66.412131,17.957286],[-66.445481,17.979379],[-66.450368,17.983226],[-66.454888,17.986784],[-66.461342,17.990273],[-66.491396,17.990262],[-66.510143,17.985618],[-66.540537,17.975476],[-66.583233,17.961229],[-66.589658,17.969386],[-66.594392,17.970682],[-66.605035,17.969015],[-66.623788,17.98105],[-66.631944,17.982746],[-66.645651,17.98026],[-66.657797,17.974605],[-66.664391,17.968259],[-66.672819,17.966451],[-66.699115,17.977568],[-66.709856,17.982109],[-66.713394,17.987763],[-66.716957,17.990344],[-66.731118,17.991658],[-66.746248,17.990349],[-66.750427,17.995443],[-66.753964,17.99959],[-66.755341,18.001203],[-66.764491,18.006317],[-66.770307,18.005955],[-66.799656,17.99245],[-66.806866,17.983786],[-66.807924,17.979606],[-66.806903,17.976046],[-66.805683,17.975052],[-66.795106,17.977438],[-66.789302,17.980793],[-66.784953,17.978326],[-66.787245,17.972914],[-66.80827,17.965635],[-66.8224,17.954499],[-66.838584,17.949931],[-66.852288,17.955004],[-66.856474,17.956553],[-66.859471,17.954316],[-66.862545,17.952022],[-66.871697,17.952707],[-66.88344,17.952526],[-66.899639,17.948298],[-66.904585,17.950527],[-66.906532,17.955356],[-66.906276,17.963368],[-66.924529,17.972808],[-66.928651,17.970204],[-66.930414,17.963127],[-66.916127,17.959102],[-66.909483,17.952559],[-66.909359,17.94988],[-66.912522,17.947446],[-66.930313,17.943389],[-66.932636,17.939998],[-66.931581,17.9369],[-66.919298,17.932062],[-66.923826,17.926923],[-66.927261,17.926875],[-66.959998,17.940216],[-66.980516,17.951648],[-66.98105,17.952505],[-66.982669,17.9551],[-66.982206,17.961192],[-66.987287,17.970663],[-66.996738,17.972899],[-67.003972,17.970799],[-67.014744,17.968468],[-67.024522,17.970722],[-67.062478,17.973819],[-67.076534,17.967759],[-67.089827,17.951418],[-67.101468,17.946621],[-67.109985,17.945806],[-67.109986,17.945806],[-67.128251,17.948153],[-67.133733,17.951919],[-67.167031,17.963073],[-67.178566,17.964792],[-67.183508,17.962706],[-67.188717,17.950989],[-67.187474,17.946252],[-67.183694,17.937982],[-67.183457,17.931135],[-67.194785,17.932826],[-67.196924,17.935651],[-67.197273,17.937461],[-67.197517,17.941514],[-67.197668,17.943549],[-67.198988,17.94782],[-67.200973,17.949896],[-67.210034,17.953595],[-67.212101,17.956027],[-67.21433,17.962436],[-67.215271,17.983464],[-67.211973,17.992993],[-67.207694,17.998019],[-67.177893,18.008882],[-67.174299,18.011149],[-67.172397,18.014906],[-67.172138,18.021422],[-67.173761,18.024548],[-67.193269,18.03185],[-67.209887,18.035439],[-67.196694,18.066491],[-67.190656,18.064269],[-67.184589,18.06775],[-67.183938,18.069914],[-67.186465,18.074195],[-67.192999,18.076877],[-67.198212,18.076828],[-67.199314,18.091135],[-67.19529,18.096149],[-67.183921,18.103683],[-67.182182,18.108507],[-67.176554,18.151046],[-67.178618,18.159318],[-67.180822,18.168055],[-67.180701,18.168182],[-67.155185,18.195001],[-67.152665,18.203493],[-67.158001,18.216719],[-67.173,18.230666],[-67.175429,18.248008],[-67.187843,18.266671],[-67.187873,18.266874],[-67.189971,18.281015],[-67.196056,18.290443],[-67.209963,18.294974],[-67.225403,18.296648],[-67.226081,18.296722],[-67.235137,18.299935],[-67.267484,18.353149],[-67.27135,18.362329],[-67.268259,18.366989],[-67.260671,18.370197],[-67.23909,18.375318],[-67.226744,18.378247],[-67.216998,18.382078],[-67.202167,18.389908],[-67.160144,18.415587],[-67.159608,18.415915],[-67.156599,18.418983],[-67.155245,18.424401],[-67.156619,18.439562],[-67.161746,18.453462],[-67.169011,18.466352],[-67.169016,18.478488],[-67.164144,18.487396],[-67.14283,18.505485],[-67.138249,18.507776],[-67.125655,18.511706],[-67.103468,18.514523],[-67.093752,18.515757],[-67.07929,18.513256],[-67.020276,18.510603],[-66.988958,18.497724],[-66.95954,18.489878],[-66.957733,18.489129],[-66.957517,18.489171],[-66.944636,18.491693],[-66.906872,18.483556],[-66.90143,18.484552],[-66.867386,18.490785],[-66.849673,18.490745],[-66.83694,18.487659],[-66.836635,18.487701],[-66.79932,18.492775],[-66.780311,18.491411],[-66.764893,18.484097],[-66.749301,18.476701],[-66.742067,18.474681],[-66.733986,18.473457],[-66.710743,18.472611],[-66.683719,18.481367],[-66.679876,18.484944],[-66.664364,18.487809],[-66.645839,18.488777],[-66.624618,18.494199],[-66.586778,18.484948],[-66.584074,18.484287],[-66.565241,18.485523],[-66.562916,18.48845],[-66.563485,18.490512],[-66.558503,18.489987],[-66.53484,18.481253],[-66.533487,18.481663],[-66.529476,18.482877],[-66.511609,18.476848],[-66.470292,18.46907],[-66.456486,18.46892],[-66.449184,18.470991],[-66.441852,18.479751],[-66.439961,18.485525],[-66.438813,18.485713]]]]},\"properties\":{\"name\":\"Puerto Rico\",\"nation\":\"USA \"}}]}","volume":"36","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hall, Jazlynn S. 0000-0002-8782-0065","orcid":"https://orcid.org/0000-0002-8782-0065","contributorId":290688,"corporation":false,"usgs":false,"family":"Hall","given":"Jazlynn","email":"","middleInitial":"S.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":841585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scholl, Martha A. 0000-0001-6994-4614 mascholl@usgs.gov","orcid":"https://orcid.org/0000-0001-6994-4614","contributorId":1920,"corporation":false,"usgs":true,"family":"Scholl","given":"Martha","email":"mascholl@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":841586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorokhovich, Yuri","contributorId":290689,"corporation":false,"usgs":false,"family":"Gorokhovich","given":"Yuri","email":"","affiliations":[{"id":39562,"text":"City University of New York","active":true,"usgs":false}],"preferred":false,"id":841587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uriarte, Maria","contributorId":287019,"corporation":false,"usgs":false,"family":"Uriarte","given":"Maria","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":841588,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230138,"text":"70230138 - 2022 - Functional wetland loss drives emerging risks to waterbird migration networks","interactions":[],"lastModifiedDate":"2022-03-30T15:51:12.581428","indexId":"70230138","displayToPublicDate":"2022-03-10T10:45:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Functional wetland loss drives emerging risks to waterbird migration networks","docAbstract":"Migratory waterbirds (i.e., shorebirds, wading birds, and waterfowl) rely on a diffuse continental network of wetland habitats to support annual life cycle needs. Emerging threats of climate and land-use change raise new concerns over the sustainability of these habitat networks as water scarcity triggers cascading ecological effects impacting wetland habitat availability. Here we use important waterbird regions in Oregon and California, United States, as a model system to examine patterns of landscape change impacting wetland habitat networks in western North America. Wetland hydrology and flooded agricultural habitats were monitored monthly from 1988 to 2020 using satellite imagery to quantify the timing and duration of inundation—a key delimiter of habitat niche values associated with waterbird use. Trends were binned by management practice and wetland hydroperiods (semi-permanent, seasonal, and temporary) to identify differences in their climate and land-use change sensitivity. Wetland results were assessed using 33 waterbird species to detect non-linear effects of network change across a diversity of life cycle and habitat needs. Pervasive loss of semi-permanent wetlands was an indicator of systemic functional decline. Shortened hydroperiods caused by excessive drying transitioned semi-permanent wetlands to seasonal and temporary hydrologies—a process that in part counterbalanced concurrent seasonal and temporary wetland losses. Expansion of seasonal and temporary wetlands associated with closed-basin lakes offset wetland declines on other public and private lands, including wildlife refuges. Diving ducks, black terns, and grebes exhibited the most significant risk of habitat decline due to semi-permanent wetland loss that overlapped important migration, breeding, molting, and wintering periods. Shorebirds and dabbling ducks were beneficiaries of stable agricultural practices and top-down processes of functional wetland declines that operated collectively to maintain habitat needs. Outcomes from this work provide a novel perspective of wetland ecosystem change affecting waterbirds and their migration networks. Understanding the complexity of these relationships will become increasingly important as water scarcity continues to restructure the timing and availability of wetland resources.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.844278","usgsCitation":"Donnelly, J., Moore, J.N., Casazza, M.L., and Coons, S.P., 2022, Functional wetland loss drives emerging risks to waterbird migration networks: Frontiers in Ecology and Evolution, v. 10, 844278, 18 p., https://doi.org/10.3389/fevo.2022.844278.","productDescription":"844278, 18 p.","ipdsId":"IP-137357","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448530,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.844278","text":"Publisher Index Page"},{"id":397863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.71826171875,\n 35.35321610123823\n ],\n [\n -118.71826171875,\n 36.06686213257888\n ],\n [\n -119.35546875000001,\n 36.87962060502676\n ],\n [\n -120.62988281249999,\n 38.151837403006766\n ],\n [\n -121.31103515625,\n 39.36827914916014\n ],\n [\n -121.86035156249999,\n 40.59727063442024\n ],\n [\n -122.36572265625,\n 40.730608477796636\n ],\n [\n -122.87109375,\n 40.38002840251183\n ],\n [\n -122.56347656249999,\n 39.33429742980725\n ],\n [\n -121.9921875,\n 38.54816542304656\n ],\n [\n -121.06933593749999,\n 37.579412513438385\n ],\n [\n -120.498046875,\n 36.61552763134925\n ],\n [\n -119.2236328125,\n 35.28150065789119\n ],\n [\n -118.71826171875,\n 35.35321610123823\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.47607421874999,\n 40.730608477796636\n ],\n [\n -119.46533203125,\n 40.697299008636755\n ],\n [\n -119.46533203125,\n 41.64007838467894\n ],\n [\n -118.740234375,\n 42.32606244456202\n ],\n [\n -117.99316406249999,\n 43.004647127794435\n ],\n [\n -118.89404296875,\n 44.15068115978094\n ],\n [\n -119.90478515625,\n 44.465151013519616\n ],\n [\n -121.1572265625,\n 44.071800467511565\n ],\n [\n -121.66259765625001,\n 43.27720532212024\n ],\n [\n -121.13525390625,\n 42.24478535602799\n ],\n [\n -121.2451171875,\n 41.672911819602085\n ],\n [\n -121.06933593749999,\n 41.244772343082076\n ],\n [\n -120.56396484375,\n 41.16211393939692\n ],\n [\n -120.47607421874999,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Donnelly, J Patrick","contributorId":289526,"corporation":false,"usgs":false,"family":"Donnelly","given":"J Patrick","affiliations":[{"id":62169,"text":"Intermountain West Joint Venture - U.S. Fish and Wildlife Service, Migratory Bird Program, Missoula, MT, United States","active":true,"usgs":false}],"preferred":false,"id":839227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Johnnie N","contributorId":289527,"corporation":false,"usgs":false,"family":"Moore","given":"Johnnie","email":"","middleInitial":"N","affiliations":[{"id":62170,"text":"Group for Quantitative Study of Snow and Ice, Department of Geosciences, University of Montana, Missoula, MT, United States","active":true,"usgs":false}],"preferred":false,"id":839228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coons, Shea P","contributorId":289528,"corporation":false,"usgs":false,"family":"Coons","given":"Shea","email":"","middleInitial":"P","affiliations":[{"id":62172,"text":"Avian Science Center - University of Montana, Missoula, MT, United States","active":true,"usgs":false}],"preferred":false,"id":839230,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229510,"text":"sir20225003 - 2022 - Response of Green Lake, Wisconsin, to changes in phosphorus loading, with special emphasis on near-surface total phosphorus concentrations and metalimnetic dissolved oxygen minima","interactions":[],"lastModifiedDate":"2026-04-08T17:07:36.608501","indexId":"sir20225003","displayToPublicDate":"2022-03-09T13:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5003","displayTitle":"Response of Green Lake, Wisconsin, to Changes in Phosphorus Loading, With Special Emphasis on Near-Surface Total Phosphorus Concentrations and Metalimnetic Dissolved Oxygen Minima","title":"Response of Green Lake, Wisconsin, to changes in phosphorus loading, with special emphasis on near-surface total phosphorus concentrations and metalimnetic dissolved oxygen minima","docAbstract":"Green Lake is the deepest natural inland lake in Wisconsin, with a maximum depth of about 72 meters. In the early 1900s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity) with low dissolved oxygen (DO) concentrations occurring in only the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake have increased; these changes have led to increased algal production and low DO concentrations not only in the deepest areas but also in the middle of the water column (metalimnion). The U.S. Geological Survey has routinely monitored the lake since 2004 and its tributaries since 1988. Results from this monitoring led the Wisconsin Department of Natural Resources (WDNR) to list the lake as impaired because of low DO concentrations in the metalimnion, and they identified elevated TP concentrations as the cause of impairment.
As part of this study by the U.S. Geological Survey, in cooperation with the Green Lake Sanitary District, the lake and its tributaries were comprehensively sampled in 2017–18 to augment ongoing monitoring that would further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water-quality models were then used to determine the causes of the low DO concentrations and the magnitudes of P-load reductions needed to improve the water quality of the lake enough to meet multiple water-quality goals, including the WDNR’s criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900s but, since the late 1970s, have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–18 (the baseline period for this study), the near-surface geometric mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were above the 0.015-mg/L WDNR criterion for the lake), and the metalimnetic DO minimum concentrations (MOMs) measured in August ranged from 1.0 to 4.7 mg/L. The degradation in water quality was assumed to have been caused by excessive P inputs to the lake; therefore, the TP inputs to the lake were estimated. The mean annual external P load during 2014–18 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. During fall turnover, internal sediment recycling contributed an additional 7,040 kilograms that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there was a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, quantified by Secchi disk depths, had a greater response to decreases in P loading than to increases in P loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration in the east side of the lake, with higher TP concentrations than in the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr is equivalent to a 46-percent reduction in the potentially controllable external P sources (all external sources except for precipitation, atmospheric deposition, and waterfowl) from those measured during water years 2014–18. The total external P loading would need to decrease to 7,680 kg/yr (a 17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to decrease to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June–September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM–AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that MOMs are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM–AED results indicated that settling of organic material during summer might be slowed by the colder, denser, and more viscous water in the metalimnion and thus increase DO consumption. Based on empirical evidence from a comparison of MOMs with various meteorological, hydrologic, water quality, and in-lake physical factors, MOMs were lower during summers, when metalimnetic water temperatures were warmer, near-surface Chl-a and TP concentrations were higher, and Secchi depths were lower. GLM–AED results indicated that the external P load would need to be reduced to about 4,060 kg/yr, a 57-percent reduction from that measured in 2014–18, to eliminate the occurrence of MOMs less than 5 mg/L during more than 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake; however, it may take several years for the full effects of the external-load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225003","collaboration":"Prepared in cooperation with the Green Lake Sanitary District","usgsCitation":"Robertson, D.M., Siebers, B.J., Ladwig, R., Hamilton, D.P., Reneau, P.C., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2022, Response of Green Lake, Wisconsin, to changes in phosphorus loading, with special emphasis on near-surface total phosphorus concentrations and metalimnetic dissolved oxygen minima: U.S. Geological Survey Scientific Investigations Report 2022–5003, 77 p., https://doi.org/10.3133/sir20225003.","productDescription":"Report: xi, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123380","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502291,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112545.htm","linkFileType":{"id":5,"text":"html"}},{"id":396912,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5003/images/"},{"id":396910,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H85BK0","text":"USGS data release","linkHelpText":"Eutrophication models to simulate changes in the water quality of Green Lake, Wisconsin in response to changes in phosphorus loading, with supporting water-quality data for the lake, its tributaries, and atmospheric deposition"},{"id":396911,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5003/sir20225003.XML"},{"id":396909,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5003/sir20225003.pdf","text":"Report","size":"8.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5003"},{"id":396908,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5003/coverthb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.09225463867188,\n 43.756712928570245\n ],\n [\n -88.86428833007814,\n 43.756712928570245\n ],\n [\n -88.86428833007814,\n 43.85384062624276\n ],\n [\n -89.09225463867188,\n 43.85384062624276\n ],\n [\n -89.09225463867188,\n 43.756712928570245\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
We created a 270-m coupled model of land-use and groundwater conditions, LUCAS-W[ater], for California’s Central Coast. This groundwater-dependent region is undergoing a dramatic reorganization of groundwater management under California’s 2014 Sustainable Groundwater Management Act (SGMA).
Understanding land-use and land-cover change supports long-term sustainable water management. Anthropogenic water demand has depleted groundwater aquifers worldwide, while future water shortages will likely affect land-use change, creating system feedbacks. Our novel participatory approach fused changes in land-use and associated water use from county-scale data to local water agencies’ estimates of total sustainable supply, scaling up local hydro-geologic knowledge from heterogeneous aquifers and diverse management approaches to a regional level. We assessed five stakeholder-driven scenarios with the same historic rates of urban and agricultural land-use change, but different water and land-use management, analyzing how management strategies altered both the spatial pattern of development and subsequent water sustainability from 2001 to 2061.
Transformative strategies using demand-side interventions that coupled water availability to land-use more effectively achieved long-term sustainability than adaptive strategies using supply-side interventions to increase water supplies. Limiting water withdrawals within SGMA regulated basins resulted in leakage of development into unregulated basins, increasing groundwater pumping there. Protecting ecosystems, farmlands, and recharge areas from development reduced leakage into undeveloped basins without negatively affecting water sustainability.
Releases of water from Flaming Gorge Dam together with climate-related variations in runoff determine the streamflow regime of the Green River, which affects the physical characteristics of the channel and riparian ecosystem of the Green River corridor in Canyonlands National Park. The dam has decreased peak streamflows and raised base streamflows, resulting in vegetation encroachment and channel narrowing and simplification, which could be detrimental to endangered fish habitats over time. Operations of Flaming Gorge Dam are in part determined by flow recommendations provided by the Upper Colorado River Basin Endangered Fish Recovery Program that are designed to benefit native fish and disadvantage nonnative fish. These recommendations alone may not be sufficient to prevent channel narrowing and simplification. Increases in base flows may contribute to channel narrowing and simplification by increasing the water available to riparian vegetation and reducing the water volume available for increasing peak-flow magnitude or duration This report describes how proposed revisions to these flow recommendations would affect the physical characteristics of the Green River corridor in Canyonlands National Park, with a focus on riparian vegetation and channel width.
Hydrologic conditions for the Green River downstream from Flaming Gorge Dam are classified by the U.S. Department of the Interior Bureau of Reclamation as dry, moderately dry, average, moderately wet, or wet. The flow recommendations for peak-flow magnitude and duration in wet years are consistent with geomorphic objectives and historical post-dam flows. In moderately wet years, although the recommended peaks may be sufficient to prevent narrowing over the short term, these peaks are lower than historical post-dam peak flows for moderately wet years and could therefore allow reduction in the occasional large peaks necessary to maintain sediment mobility and channel complexity. For average and drier years, the recommendations allow, but do not require, peak-flow magnitude and durations that are likely to achieve geomorphic objectives.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221019","collaboration":"Prepared in cooperation with Canyonlands National Park","usgsCitation":"Grams, P.E., Friedman, J.M., Dean, D.J., and Topping, D.J., 2022, The effects of requested flows for native fish on sediment dynamics, geomorphology, and riparian vegetation for the Green River in Canyonlands National Park, Utah: U.S. Geological Survey Open-File Report 2022–1019, 20 p., https://doi.org/10.3133/ofr20221019.","productDescription":"vi, 20 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-126163","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501764,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112531.htm","linkFileType":{"id":5,"text":"html"}},{"id":396864,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1019/ofr20221019.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":396863,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1019/covrthb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Green River, Canyonlands National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.4730224609375,\n 38.19502155795575\n ],\n [\n -109.64630126953125,\n 38.19502155795575\n ],\n [\n -109.64630126953125,\n 39.1833042481843\n ],\n [\n -110.4730224609375,\n 39.1833042481843\n ],\n [\n -110.4730224609375,\n 38.19502155795575\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"The U.S. Geological Survey (USGS) created a virtual training series for the Afghanistan Ministry of Energy and Water (MEW), now known as the National Water Affairs Regulation Authority (NWARA), to provide critical hydrological training as an alternative to an in-person training. The USGS was scheduled to provide in-person surface-water training for NWARA during 2020; however, travel was halted because of the Coronavirus disease 2019 (COVID–19) pandemic. The virtual training consisted of prerecorded and live presentations that were scheduled during 4 weeks in August 2021. However, the training was halted after the second week due to the collapse of the Afghan Government. Fortunately, the prerecorded presentations and training materials were delivered before the trainings were halted, so they can be viewed or shared by the participants in the future. A benefit to having produced prerecorded trainings is that USGS can leverage or adapt the trainings for nongovernmental organizations (NGOs) involved in humanitarian water relief efforts in Afghanistan or can be used for other international training efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223014","collaboration":"Prepared in cooperation with U.S. Agency for International Development","usgsCitation":"Groten, J.T., Valder, J.F., Densmore, B.K., Neal, L.W., Krahulik, J., and Mack, T.J., 2022, Virtual training prepared for the former Afghanistan Ministry of Energy and Water—Streamgaging, fluvial sediment sampling, bathymetry, and streamflow and sediment modeling: U.S. Geological Survey Fact Sheet 2022–3014, 2 p., https://doi.org/10.3133/fs20223014.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-137256","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396849,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3014/coverthb.jpg"},{"id":396850,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3014/fs20223014.pdf","text":"Report","size":"545 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3014"},{"id":396851,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3014/fs20223014.XML"},{"id":396854,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3014/images"}],"contact":"Director, Office of International Programs
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Deep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi-task DL. A multi-task scaling factor controlled the relative contribution of the auxiliary variable's error to the overall loss during training. Our experiments examined the improvement in prediction accuracy of the multi-task approach using paired streamflow and water temperature data from sites across the conterminous United States. Our results showed that for 56 out of 101 sites, the best performing multi-task models performed better overall than the single-task models in terms of Nash-Sutcliffe efficiency for predicting streamflow with single-site models. For 43 sites, the best multi-task, single-site models made no significant difference in predicting streamflow. The multi-task approach had a smaller effect when applied to a model trained with data from 101 sites together, significantly improving performance for only 17 sites. The multi-task scaling factor was consequential in determining to what extent the multi-task approach was beneficial. A naïve selection of this factor led to significantly worse-performing models for 3 of 101 sites when predicting streamflow as the primary variable, and 47 of 53 sites when predicting stream temperature as the primary variable. We conclude that a multi-task approach can make more accurate predictions by leveraging information from interdependent hydrologic variables, but only for some sites, variables, and model configurations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR030138","usgsCitation":"Sadler, J.M., Appling, A.P., Read, J., Oliver, S.K., Jia, X., Zwart, J.A., and Kumar, V., 2022, Multi-task deep learning of daily streamflow and water temperature: Water Resources Research, v. 58, no. 4, e2021WR030138, 18 p., https://doi.org/10.1029/2021WR030138.","productDescription":"e2021WR030138, 18 p.","ipdsId":"IP-129032","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr030138","text":"Publisher Index Page"},{"id":386338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":817231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":817232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":817233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":817235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":817236,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":817237,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228689,"text":"fs20223003 - 2022 - Water priorities for the Nation—The USGS National Water Dashboard","interactions":[],"lastModifiedDate":"2026-03-24T21:06:50.95795","indexId":"fs20223003","displayToPublicDate":"2022-03-02T11:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3003","displayTitle":"Water Priorities for the Nation—The USGS National Water Dashboard","title":"Water priorities for the Nation—The USGS National Water Dashboard","docAbstract":"The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database (https://doi.org/10.5066/F7P55KJN) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223003","usgsCitation":"Miller, M.P., Burley, T.E., and McCallum, B.E., 2022, Water priorities for the Nation—The USGS National Water Dashboard: U.S. Geological Survey Fact Sheet 2022–3003, 2 p., https://doi.org/10.3133/fs20223003.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-127299","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":396067,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3003/coverthb2.jpg"},{"id":501482,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112529.htm","linkFileType":{"id":5,"text":"html"}},{"id":402116,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3003/fs20223003_Spanish.pdf","text":"Report (Spanish version)","size":"991 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3003_Spanish"},{"id":396068,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3003/fs20223003.pdf","text":"Report","size":"972 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3003"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.56640625,\n 18.771115062337024\n ],\n [\n -154.68749999999997,\n 19.642587534013032\n ],\n [\n -156.9287109375,\n 21.453068633086783\n ],\n [\n -159.521484375,\n 22.43134015636061\n ],\n [\n -160.5322265625,\n 21.983801417384697\n ],\n [\n -159.9609375,\n 21.207458730482642\n ],\n [\n -158.291015625,\n 20.92039691397189\n ],\n [\n -156.97265625,\n 19.932041306115536\n ],\n [\n -155.9619140625,\n 18.8543103618898\n ],\n [\n -155.56640625,\n 18.771115062337024\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.060546875,\n 18.020527657852337\n ],\n [\n -66.2255859375,\n 17.916022703877665\n ],\n [\n -65.6103515625,\n 17.97873309555617\n ],\n [\n -65.2587890625,\n 18.124970639386515\n ],\n [\n -65.5224609375,\n 18.458768120015126\n ],\n [\n -66.11572265625,\n 18.542116654448996\n ],\n [\n -66.95068359374999,\n 18.60460138845525\n ],\n [\n -67.34619140625,\n 18.542116654448996\n ],\n [\n -67.2802734375,\n 17.99963161491187\n ],\n [\n -67.060546875,\n 18.020527657852337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Associate Director, Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database (https://doi.org/10.5066/F7P55KJN) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip213","usgsCitation":"Miller, M.P., Burley, T.E., and McCallum, B.E., 2022, Visit the U.S. Geological Survey's National Water Dashboard: U.S. Geological Survey General Information Product 213, 2 p., https://doi.org/10.3133/gip213.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-127330","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":396097,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/213/coverthb.jpg"},{"id":396098,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/213/gip213.pdf","text":"Report","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 213"}],"contact":"Associate Director, Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Hydroclimate on ‘Uvea (Wallis et Futuna) is controlled by rainfall associated with the South Pacific Convergence Zone (SPCZ), the southern hemisphere's largest precipitation feature. To extend the short observational precipitation record, the hydrogen isotopic composition of the algal lipid biomarker dinosterol (δ2Hdinosterol) was measured in sediment cores from two volcanic crater lakes on ‘Uvea. The modern lakes differ morphologically and chemically but both contain freshwater within the photic zone, support phytoplankton communities inclusive of dinosterol-producing dinoflagellates, and experience identical climate conditions. δ2Hdinosterol values track lake water isotope ratios, ultimately controlled in the tropics by precipitation amount and evaporative enrichment. However, in 88-m-deep Lac Lalolalo a steadily decreasing trend in sedimentary δ2Hdinosterol values from −227‰ around year 988 CE to modern values as low as −303‰, suggests this lake's evolution from an active volcanic setting to the present system strongly influenced δ2Hdinosterol values. Although current hydrology and water isotope systematics may now reflect precipitation and evaporation in this lake, the interaction between these processes and large changes in basin morphology, geochemistry, and hydrology obstruct the recovery of a climate signal from Lac Lalolalo's sedimentary δ2Hdinosterol records. This work emphasizes the importance of site replication and the use of complementary climate reconstruction tools, especially when using molecular proxies that may be sensitive to more than one environmental parameter. Contrary to its neighbor, duplicate δ2Hdinosterol records from 23-m-deep Lac Lanutavake varied between −277‰ and −297‰ and indicate slightly drier conditions during the time-period known as the Medieval Climate Anomaly (MCA, 950–1250 CE). The δ2Hdinosterol signal in Lac Lanutavake was muted compared to published records from ‘Upolu (Samoa) and Efate (Vanuatu) indicating that ‘Uvea's location is not as sensitive to precipitation variability at sites farther from the SPCZ central axis. Lithogenic runoff proxies combined with δ2Hdinosterol support the interpretation of a relatively dry MCA on ‘Uvea, ‘Upolu, and Efate, potentially due to less intense precipitation, a contracted, or a more zonally oriented SPCZ.
We sampled juvenile wild Steelhead Trout Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize population attributes and investigate life-history metrics, particularly migratory patterns, and early life-stage survival. We used passive integrated transponder (PIT) tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles and adults. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead Trout have been released in the Wind River subbasin since 1997, and hatchery adults are estimated at less than one percent of spawners in most years. Over twenty years of Steelhead Trout status and trend monitoring and research in the subbasin is contributing to understanding of population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, which had an outdated adult ladder and contributed to increased water temperatures reducing performance of juvenile Steelhead Trout.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research, Monitoring, and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (https://www.cbfish.org/ProgramStrategy.mvc/Index). Specifically, this work addresses the substrategies of 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and Uncertainties Research regarding differing life histories of a wild Steelhead Trout population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives.
During summer and fall 2020, we PIT-tagged 1,415 Steelhead parr (age-0 and age-1) in the Trout Creek and upper Wind River watersheds. Recaptures and detections of PIT-tagged Steelhead Trout parr happened through repeat headwater sampling, smolt trap operations, and instream PTISs and Columbia River PIT-tag detection infrastructure. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead Trout parr, smolts, and adults, providing data to population assessments, and life-cycle research and modeling.
Detection data from PIT-tagged adult Steelhead Trout at PTISs allow assessment of adult escapement to tributary watersheds within the Wind River subbasin. Adult Steelhead Trout detection efficiency estimates at our primary PTIS in Trout Creek have been greater than 92 percent during eight of the past nine years and have exceeded 90% at our primary PTIS in the Wind River the past three years. Adult escapement estimates to tributary watersheds are helping evaluate the efficacy of the 2009 removal of Hemlock Dam from rkm 2.0 of Trout Creek. The dam had potential negative effects on Steelhead Trout populations in Trout Creek due to hydrologic impairment, increased temperatures, and adult passage issues. Hemlock Dam was laddered for adult passage, but not to modern standards, which likely resulted in avoidance by some adult Steelhead Trout.
We continue to improve our PTISs in the Wind River subbasin. The improvements in siting and addition of grid power to the upper Wind River PTIS (site code WRU, rkm 27.6) during 2016 and 2017, and the addition of the Mine Reach site (site code MIN, rkm 36.0) have much improved PIT-tagged fish monitoring in the upper Wind River watershed. The paired PTIS design in the upper Wind River watershed (sites WRU and MIN), matches that in the Trout Creek watershed (sites TRC and TC4) and will allow comparisons of Steelhead Trout population metrics between the two watersheds as response to Hemlock Dam removal continues and future restoration efforts occur in Trout Creek. We installed two new PTISs during 2020. Both were installed downstream of our primary interrogation sites on Trout Creek and in the mainstem Wind River. We hope the two new sites will provide interrogations information that will allow us to better estimate detection efficiencies of downstream moving juvenile Steelhead Trout at the primary interrogation sites. The additional interrogations will be particularly important for those fish tagged with 9-mm PIT tags as less information from downstream locations is available from them. These sites and other status and trend data will allow evaluation of further planned restoration within the watershed, particularly that proposed for the headwaters of Trout Creek.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. We have estimated that from 15 to 51% of parr tagged as age-0 fish in headwater areas make downstream migrations at age 1 for additional rearing during both spring and fall. We have estimated that up to 27% of Steelhead Trout parr, tagged as age-1 fish, make downstream migrations during fall. These findings raise questions about where parr most successfully rear and whether migrations are density or habitat quality driven. Broader monitoring programs would give a more comprehensive understanding of juvenile Steelhead Trout production and rearing and productivity contribution.
Repeat sampling at consistent locations in the subbasin has enabled assessment of juvenile Steelhead Trout growth patterns. Growth rates (relative change in weight) of age-0 PITtagged parr during summer were similar across the subbasin but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly relative growth for parr tagged at age-0 is similar across the subbasin.
Non-native Brook Trout Salvelinus confluentus are present in the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Mean percent-of-catch that is Brook Trout, at four sample sites in Trout Creek, has declined from the period 1998 – 2003 to the period 2011 – 2020. Percent-of-catch and number of Brook Trout at the Trout Creek sites from 2011 through 2020 declined, though both metrics increased in 2018.
Evaluation and planning of restoration efforts are critical to ensure efficient use of resources. Assessing Steelhead Trout life history variation in the Wind River subbasin will inform research and tracking of many populations and help inform habitat restoration and water allocation planning. Movement of Steelhead Trout parr from natal areas to other rearing areas raises questions regarding juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead Trout parr allow investigation of such questions. Increasingly detailed viable salmonid population information, such as that provided by PIT-tagging and instream PTIS networks like those in the Wind River can provide data to inform fisheries policy and management and understand life-history strategies and limiting factors. Such efforts also provide assessment of long-term effects of habitat restoration actions such as the removal of Hemlock Dam on Trout Creek, and the proposed Stage-0 restoration effort for upper Trout Creek, which would be a large-scale effort to reset sections of stream within their floodplain, restoring connectivity and interaction with surrounding landscape.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I., 2022, Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020, 71 p.","productDescription":"71 p.","ipdsId":"IP-137356","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483119,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P190880","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.1275,\n 46\n ],\n [\n -122.1275,\n 45.75\n ],\n [\n -121.8,\n 45.75\n ],\n [\n -121.8,\n 46\n ],\n [\n -122.1275,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217811,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930257,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230685,"text":"70230685 - 2022 - Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States","interactions":[],"lastModifiedDate":"2022-05-13T15:16:03.306357","indexId":"70230685","displayToPublicDate":"2022-03-01T06:53:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States","docAbstract":"Benthic cyanobacteria are widespread in streams and rivers and have the potential to release toxins. In large numbers, these microorganisms and their toxins present a risk to human health. Cyanobacterial abundance in stream biofilms is typically related to single or a limited set of environmental factors, mainly light availability, water temperature, and nutrient concentrations. However, these factors may act synergistically with watershed characteristics and other stressors, such as anthropogenic pollutants, to affect cyanobacteria. We investigated the influence of multiple regional and local variables on the abundance of benthic cyanobacterial genera in streams using all subsets generalized additive modeling. We examined watershed factors (topography, geology, and climate) alongside in-stream factors (geomorphology, hydrology, pH, specific conductance, nutrients, organic contaminants, and dissolved metals) from 76 sites along an urban gradient in the northeast United States. Each genus responded to a distinct combination of environmental variables, demonstrating strong intergeneric variation in environmental selection of realized niches. Four of the 7 potentially toxigenic genera that we modeled were positively influenced by water temperature or nutrients. Nonetheless, watershed characteristics, streamflow, and/or other water quality pollutants were equally or more influential for the potentially toxigenic genera. Additionally, the relationships between cyanobacterial abundance and environmental factors varied in shape and direction across many genera. In particular, with increasing concentrations of herbicides, polychlorinated biphenyls, or metals, the abundance of roughly half of the affected genera decreased, while the others increased. These results likely demonstrate novel toxic effects of the pollutants on cyanobacterial genera in the environment, while indicating that unmeasured biotic interactions may lead to positive responses for other genera. Our results emphasize the need to consider variables beyond those that are most frequently measured or implicated (e.g., water temperature and nutrients) to more fully understand the environmental conditions that influence the distributions and abundance of potentially harmful cyanobacteria.
High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-21-0045.1","usgsCitation":"Stern, M.A., Flint, L.E., Flint, A.L., Boynton, R.M., Stewart, J.A., Wright, J.W., and Thorne, J.H., 2022, Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions: Journal of Hydrometeorology, v. 23, no. 3, p. 293-308, https://doi.org/10.1175/JHM-D-21-0045.1.","productDescription":"16 p.","startPage":"293","endPage":"308","ipdsId":"IP-127192","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-21-0045.1","text":"Publisher Index Page"},{"id":414886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boynton, Ryan M 0000-0002-3952-2573","orcid":"https://orcid.org/0000-0002-3952-2573","contributorId":303743,"corporation":false,"usgs":false,"family":"Boynton","given":"Ryan","email":"","middleInitial":"M","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":867969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Joseph A E","contributorId":247751,"corporation":false,"usgs":false,"family":"Stewart","given":"Joseph","email":"","middleInitial":"A E","affiliations":[{"id":49638,"text":"USGS WERC & UC Davis","active":true,"usgs":false}],"preferred":false,"id":867970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, Jessica W","contributorId":303744,"corporation":false,"usgs":false,"family":"Wright","given":"Jessica","email":"","middleInitial":"W","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":867971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thorne, James H.","contributorId":139144,"corporation":false,"usgs":false,"family":"Thorne","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":12659,"text":"U C Davis","active":true,"usgs":false}],"preferred":false,"id":867972,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70262057,"text":"70262057 - 2022 - Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a","interactions":[],"lastModifiedDate":"2025-01-10T16:15:07.781209","indexId":"70262057","displayToPublicDate":"2022-02-25T10:05:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a","title":"Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a","docAbstract":"We conducted a macroscale study of 2210 shallow lakes (mean depth ≤ 3 m or a maximum depth ≤ 5 m) in the Upper Midwestern and Northeastern USA. We asked the following: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4360 non-shallow lakes, and do results differ by hydrologic connectivity class? Spatial patterns and Bayesian hierarchical models indicated that shallow lakes had higher TP and CHLa than non-shallow lakes, connected shallow lakes were more productive than unconnected shallow lakes, and there was regional variation in these patterns. Important predictors of TP and CHLa included lake-specific watershed:lake area ratio, forested land use/cover, and baseflow; unconnected lakes were more difficult to predict than connected lakes; and region-specific predictors were mostly unimportant. Shallow lake TP–CHLa relationships were less steep than for non-shallow lakes and these relationships varied regionally. Our results, combined with the facts that only 23% of lakes in the study extent have depth data and that shallow and unconnected lakes are undersampled, have important implications for estimates of lake contributions to global cycles that are based mainly on large (and deeper) lakes.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-022-04811-1","usgsCitation":"Spence Cheruvelil, K., Webster, K., King, K., Poisson, A., and Wagner, T., 2022, Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a: Hydrobiologia, v. 849, p. 3663-3677, https://doi.org/10.1007/s10750-022-04811-1.","productDescription":"15 p.","startPage":"3663","endPage":"3677","ipdsId":"IP-130204","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":465993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, Wisconsin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-71.860513,41.320248],[-72.184122,41.323997],[-72.386629,41.261798],[-72.701806,41.244752],[-72.895445,41.243697],[-72.916827,41.282033],[-73.111052,41.150797],[-73.177774,41.166697],[-73.643478,41.002171],[-73.815574,40.835129],[-73.776032,40.795275],[-73.730675,40.8654],[-73.485365,40.946397],[-73.229285,40.905121],[-73.110368,40.971938],[-72.585327,40.997587],[-72.278789,41.158722],[-72.237731,41.156434],[-72.32663,41.132162],[-72.317238,41.088659],[-72.260515,41.042065],[-72.153857,41.051859],[-72.10216,40.991509],[-71.959595,41.071237],[-71.856214,41.070598],[-73.054963,40.666371],[-73.934512,40.545175],[-74.03959,40.612934],[-74.024543,40.709436],[-74.189106,40.643832],[-74.261889,40.464706],[-73.998505,40.410911],[-74.014031,40.476471],[-73.978282,40.440208],[-74.096906,39.76303],[-74.334804,39.432001],[-74.614481,39.244659],[-74.864458,38.94041],[-74.971995,38.94037],[-74.905181,39.174945],[-75.035672,39.215415],[-75.136548,39.179425],[-75.251806,39.299913],[-75.536431,39.460559],[-75.512732,39.578],[-75.559102,39.629056],[-75.405337,39.796213],[-75.518444,39.836311],[-75.662822,39.82115],[-75.788359,39.721811],[-80.519342,39.721403],[-80.518991,40.638801],[-80.667957,40.582496],[-80.595494,40.475266],[-80.633596,40.390467],[-80.599895,40.317669],[-80.738604,40.075672],[-80.758304,39.910426],[-80.806018,39.91713],[-80.790761,39.86728],[-80.869092,39.766364],[-80.829723,39.714041],[-80.88036,39.620706],[-81.217315,39.38759],[-81.375961,39.341697],[-81.456143,39.409274],[-81.557547,39.338774],[-81.570247,39.267675],[-81.683627,39.270939],[-81.755754,39.180976],[-81.747253,39.095378],[-81.813855,39.079278],[-81.762659,38.924121],[-81.825026,38.946603],[-81.898541,38.874582],[-81.933186,38.987659],[-82.035963,39.025478],[-82.221566,38.787187],[-82.177267,38.603784],[-82.291271,38.578983],[-82.330335,38.4445],[-82.579976,38.41013],[-82.696621,38.542112],[-82.844306,38.590862],[-82.889193,38.756076],[-83.294193,38.596588],[-83.520953,38.703045],[-83.659304,38.628592],[-83.762445,38.652103],[-83.86653,38.7602],[-84.212904,38.805707],[-84.304698,39.006455],[-84.42573,39.053059],[-84.449793,39.117754],[-84.620112,39.073457],[-84.754449,39.146658],[-84.897364,39.057378],[-84.829857,38.969385],[-84.877029,38.909016],[-84.785234,38.880439],[-84.812877,38.786087],[-84.990006,38.778383],[-85.172528,38.688082],[-85.275454,38.741172],[-85.434065,38.729455],[-85.423077,38.531581],[-85.607629,38.439295],[-85.683561,38.295469],[-85.829364,38.276769],[-85.908764,38.161169],[-85.925418,38.023456],[-86.038188,37.95935],[-86.261273,38.052721],[-86.271802,38.137874],[-86.360377,38.198796],[-86.323453,38.139032],[-86.463248,38.119278],[-86.430091,38.078638],[-86.521825,38.038327],[-86.50662,37.930719],[-86.586542,37.922285],[-86.638265,37.842718],[-86.650087,37.910616],[-86.73146,37.89434],[-86.820071,37.999392],[-87.033444,37.906593],[-87.111133,37.782512],[-87.418585,37.944763],[-87.511499,37.906426],[-87.601416,37.972542],[-87.628416,37.92145],[-87.588729,37.860984],[-87.645858,37.825899],[-87.67573,37.90193],[-87.830578,37.876516],[-87.898062,37.927514],[-87.940839,37.883338],[-87.90681,37.807624],[-87.95259,37.771742],[-88.02803,37.799224],[-88.159372,37.661847],[-88.064234,37.484548],[-88.476592,37.386875],[-88.515939,37.284043],[-88.424403,37.152428],[-88.458948,37.073796],[-88.545403,37.070003],[-88.916934,37.224291],[-89.029981,37.211144],[-89.181369,37.046305],[-89.099007,36.961389],[-89.178888,36.831368],[-89.119198,36.759802],[-89.199798,36.734217],[-89.15908,36.666352],[-89.236542,36.566824],[-89.365548,36.625059],[-89.464153,36.457189],[-89.479093,36.568206],[-89.563185,36.568749],[-89.509558,36.375065],[-89.620255,36.323006],[-89.534745,36.252576],[-89.703511,36.243412],[-89.591605,36.144096],[-89.678821,36.084636],[-89.706932,36.000981],[-90.37789,35.995683],[-90.06398,36.303038],[-90.064514,36.382085],[-90.138512,36.413952],[-90.152481,36.497952],[-94.617919,36.499414],[-94.591933,39.155003],[-94.823791,39.209874],[-94.905329,39.311952],[-94.885026,39.389801],[-94.942039,39.389499],[-95.109304,39.542285],[-94.965318,39.739065],[-94.862943,39.742994],[-94.934262,39.773642],[-94.875944,39.813294],[-94.929574,39.888754],[-95.085003,39.861883],[-95.201935,39.904053],[-95.315271,40.01207],[-95.413588,40.038424],[-95.393347,40.119212],[-95.479193,40.185652],[-95.477501,40.24272],[-95.657328,40.310856],[-95.623728,40.346567],[-95.699969,40.505275],[-95.655848,40.546609],[-95.76692,40.531563],[-95.749685,40.606842],[-95.888907,40.731855],[-95.809474,40.891228],[-95.882415,41.060411],[-95.883489,41.154898],[-95.841288,41.174998],[-95.92599,41.195698],[-95.927491,41.298397],[-95.871489,41.295797],[-95.956691,41.345496],[-95.922529,41.455766],[-96.011757,41.476212],[-96.005079,41.544004],[-96.040701,41.507076],[-96.09409,41.539265],[-96.121401,41.688522],[-96.073063,41.705004],[-96.106425,41.73789],[-96.064537,41.793002],[-96.161756,41.90182],[-96.1289,41.969727],[-96.271427,42.044988],[-96.2689,42.11359],[-96.349688,42.172043],[-96.328905,42.254734],[-96.417786,42.351449],[-96.385407,42.473094],[-96.476509,42.493595],[-96.516338,42.630435],[-96.638621,42.734921],[-96.500308,42.959391],[-96.518431,43.042068],[-96.436589,43.120842],[-96.475571,43.221054],[-96.568505,43.231554],[-96.581052,43.297118],[-96.530392,43.300034],[-96.521572,43.38564],[-96.602608,43.449649],[-96.598929,43.500441],[-96.453049,43.500415],[-96.453067,45.298115],[-96.521787,45.375645],[-96.692541,45.417338],[-96.857751,45.605962],[-96.583085,45.820024],[-96.554507,46.083978],[-96.599761,46.330386],[-96.718074,46.438255],[-96.798823,46.658071],[-96.759241,46.918223],[-96.834221,47.006671],[-96.852826,47.597891],[-97.141401,48.14359],[-97.11657,48.279661],[-97.163105,48.543855],[-97.097708,48.68395],[-97.238387,48.982631],[-95.153711,48.998903],[-95.15335,49.383079],[-94.957465,49.370186],[-94.816222,49.320987],[-94.750218,48.999992],[-94.683069,48.883929],[-94.694312,48.789352],[-94.645083,48.744143],[-94.452332,48.692444],[-94.290737,48.707747],[-94.224276,48.649527],[-93.840754,48.628548],[-93.794454,48.516021],[-93.467504,48.545664],[-93.465199,48.590659],[-93.254854,48.642784],[-92.954876,48.631493],[-92.634931,48.542873],[-92.630644,48.500917],[-92.712562,48.463013],[-92.456325,48.414204],[-92.469948,48.351836],[-92.369174,48.220268],[-92.269742,48.248241],[-92.306309,48.316442],[-92.26228,48.354933],[-92.055228,48.359213],[-91.957798,48.232989],[-91.714931,48.19913],[-91.712226,48.116883],[-91.559272,48.108268],[-91.567254,48.043719],[-91.26638,48.078713],[-90.88548,48.245784],[-90.839176,48.239511],[-90.751608,48.090968],[-90.136191,48.112136],[-89.897414,47.987599],[-89.489226,48.014528],[-89.974296,47.830514],[-90.537105,47.703055],[-90.86827,47.5569],[-91.477351,47.125667],[-92.094089,46.787839],[-91.961889,46.682539],[-91.107323,46.857469],[-90.855874,46.962232],[-90.750952,46.890293],[-90.885021,46.756341],[-90.853644,46.694464],[-90.951476,46.597033],[-90.794775,46.624941],[-90.73726,46.692267],[-90.436512,46.561748],[-90.028392,46.67439],[-89.790663,46.818469],[-89.415154,46.843983],[-89.128698,46.992599],[-88.972802,47.002096],[-88.890708,47.099024],[-88.573997,47.245989],[-88.418841,47.371058],[-88.18182,47.457657],[-87.929672,47.478743],[-87.756739,47.460717],[-87.710471,47.4062],[-87.957058,47.38726],[-87.94336,47.335899],[-88.227552,47.199938],[-88.232164,47.145975],[-88.443901,46.972251],[-88.462349,46.786711],[-88.142807,46.966302],[-88.175197,46.90458],[-87.900695,46.909682],[-87.681561,46.842392],[-87.464108,46.614811],[-87.383961,46.59307],[-87.352448,46.501324],[-87.008724,46.532723],[-86.850111,46.434114],[-86.768516,46.479072],[-86.698139,46.438624],[-86.678182,46.561039],[-86.586168,46.463324],[-86.161681,46.669475],[-85.50951,46.675786],[-84.989497,46.772403],[-84.95158,46.769488],[-85.027513,46.697451],[-85.056133,46.52652],[-85.015211,46.479712],[-84.829491,46.444071],[-84.63102,46.484868],[-84.551496,46.418522],[-84.420274,46.501077],[-84.128925,46.530119],[-84.097766,46.256512],[-84.219494,46.231992],[-84.251424,46.175888],[-84.125022,46.180209],[-84.026536,46.131648],[-84.072398,46.09669],[-84.027861,46.054784],[-83.873147,45.993426],[-83.765277,46.018363],[-83.815826,46.108529],[-83.581315,46.089613],[-83.473946,45.988558],[-83.510623,45.929324],[-84.111174,45.978675],[-84.376429,45.931962],[-84.656567,46.052654],[-84.738849,45.945792],[-84.702256,45.850557],[-84.746985,45.835597],[-85.01399,46.010774],[-85.499422,46.09692],[-85.697203,45.960158],[-85.893196,45.967253],[-85.910264,45.922112],[-86.072067,45.965313],[-86.278007,45.942057],[-86.351658,45.798132],[-86.580936,45.71192],[-86.616893,45.606796],[-86.718191,45.67732],[-86.557215,45.808172],[-86.541464,45.890234],[-86.645998,45.833888],[-86.78208,45.860195],[-86.838746,45.722307],[-86.964275,45.672761],[-87.031435,45.837238],[-87.059953,45.708893],[-87.196852,45.636275],[-87.327749,45.425307],[-87.600796,45.146842],[-87.630298,44.976865],[-87.820281,44.950358],[-87.983065,44.72073],[-88.042414,44.566589],[-87.943801,44.529693],[-87.756048,44.649117],[-87.609784,44.838514],[-87.437084,44.892718],[-87.384821,44.865532],[-87.398368,44.925226],[-87.231925,45.172316],[-87.119972,45.191103],[-87.057627,45.292838],[-86.970355,45.278455],[-86.985973,45.215872],[-87.040909,45.211535],[-87.048213,45.089124],[-87.163477,45.004913],[-87.204815,44.877199],[-87.313363,44.794237],[-87.467089,44.553557],[-87.545382,44.321385],[-87.512903,44.192808],[-87.6458,44.105222],[-87.71817,43.939498],[-87.702685,43.687596],[-87.911787,43.250406],[-87.866487,43.074419],[-87.896157,43.017486],[-87.766675,42.784896],[-87.819407,42.617327],[-87.828569,42.269922],[-87.671462,42.058334],[-87.524044,41.708335],[-87.278437,41.619736],[-87.027888,41.674661],[-86.679672,41.844579],[-86.356218,42.254166],[-86.261573,42.443894],[-86.206834,42.719424],[-86.232707,43.015762],[-86.540916,43.633158],[-86.43114,43.815569],[-86.514704,44.057672],[-86.26871,44.345324],[-86.220697,44.566742],[-86.254996,44.691935],[-86.09074,44.740544],[-86.066745,44.905685],[-85.992535,44.900026],[-85.9316,44.968788],[-85.807403,44.949814],[-85.618639,45.186771],[-85.551072,45.210742],[-85.531461,45.177247],[-85.614319,45.127562],[-85.56613,45.043633],[-85.621878,45.004529],[-85.602034,44.926743],[-85.652355,44.849092],[-85.640781,44.775561],[-85.593833,44.768651],[-85.532931,44.87319],[-85.564509,44.895246],[-85.475204,44.991053],[-85.576566,44.760208],[-85.527216,44.748235],[-85.3958,44.931018],[-85.371593,45.270834],[-85.196704,45.360641],[-84.91585,45.393115],[-85.040936,45.436701],[-85.115479,45.539406],[-85.07491,45.629242],[-84.942636,45.714292],[-85.014509,45.760329],[-84.806642,45.746171],[-84.734065,45.788205],[-84.46168,45.652404],[-84.215268,45.634767],[-84.095905,45.497298],[-83.939261,45.493189],[-83.599273,45.352561],[-83.488826,45.355872],[-83.381743,45.268983],[-83.41241,45.238905],[-83.265896,45.026844],[-83.399255,45.070364],[-83.454168,45.03188],[-83.433032,44.93289],[-83.320503,44.880571],[-83.274747,44.714893],[-83.332533,44.340464],[-83.53771,44.248171],[-83.58409,44.056748],[-83.877047,43.959351],[-83.954347,43.750647],[-83.897078,43.664022],[-83.666052,43.591292],[-83.470053,43.723418],[-83.26153,43.973525],[-82.915976,44.070503],[-82.746255,43.996037],[-82.643166,43.852468],[-82.523086,43.225361],[-82.412965,42.977041],[-82.518782,42.613888],[-82.686417,42.518597],[-82.713042,42.597904],[-82.630851,42.673341],[-82.813518,42.640833],[-82.782414,42.564834],[-82.874416,42.523535],[-82.894013,42.389437],[-83.096521,42.290138],[-83.133511,42.088143],[-83.439612,41.813162],[-83.455626,41.727445],[-82.934369,41.514353],[-82.834101,41.587587],[-82.481214,41.381342],[-82.011966,41.515639],[-81.738755,41.48855],[-81.288892,41.758945],[-80.329976,42.036168],[-80.117368,42.166341],[-80.06108,42.144857],[-79.148723,42.553672],[-79.04886,42.689158],[-78.851355,42.791758],[-78.918859,42.946857],[-79.019964,42.994756],[-79.01053,43.064389],[-79.074467,43.077855],[-79.042366,43.143655],[-79.070469,43.262454],[-78.370221,43.376505],[-77.760231,43.341161],[-77.551022,43.235763],[-77.341092,43.280661],[-76.958402,43.270005],[-76.69836,43.344436],[-76.417581,43.521285],[-76.235834,43.529256],[-76.205436,43.718751],[-76.28272,43.858601],[-76.125023,43.912773],[-76.139086,43.962111],[-76.264294,43.978009],[-76.300532,44.057188],[-76.360306,44.070907],[-76.312647,44.199044],[-75.912985,44.368084],[-75.413885,44.76889],[-74.992756,44.977449],[-74.826578,45.01585],[-71.502487,45.013367],[-71.39781,45.203553],[-71.443882,45.235462],[-71.296509,45.29919],[-71.13943,45.242958],[-71.01081,45.34725],[-70.857042,45.22916],[-70.795009,45.428145],[-70.634661,45.383608],[-70.723167,45.507606],[-70.688214,45.563981],[-70.400404,45.719834],[-70.417641,45.79377],[-70.259117,45.890755],[-70.240177,45.943729],[-70.31628,45.963113],[-70.284571,45.995384],[-70.317629,46.01908],[-70.237947,46.147378],[-70.292736,46.191599],[-70.191412,46.348072],[-70.057061,46.415036],[-69.997086,46.69523],[-69.22442,47.459686],[-69.043947,47.427634],[-69.050334,47.256621],[-68.902425,47.178839],[-68.578551,47.287551],[-68.378678,47.287561],[-68.378616,47.343144],[-68.329879,47.36023],[-68.15515,47.32542],[-67.955669,47.199542],[-67.789461,47.062544],[-67.750422,45.917898],[-67.803318,45.883718],[-67.755068,45.82367],[-67.806598,45.794723],[-67.781892,45.731189],[-67.817892,45.693705],[-67.64581,45.613597],[-67.429716,45.583773],[-67.416416,45.503515],[-67.503088,45.489688],[-67.418747,45.37726],[-67.489464,45.282653],[-67.345585,45.126392],[-67.283619,45.192022],[-67.157919,45.161004],[-66.950569,44.814539],[-67.293403,44.599265],[-67.308538,44.707454],[-67.376742,44.681852],[-67.405492,44.594236],[-67.551133,44.621938],[-67.568159,44.531117],[-67.685861,44.537155],[-67.733986,44.496252],[-67.753854,44.543661],[-67.79726,44.520685],[-67.839896,44.558771],[-67.855108,44.419434],[-68.010719,44.407464],[-68.049334,44.33073],[-68.117746,44.475038],[-68.261708,44.484062],[-68.299063,44.437893],[-68.247438,44.433276],[-68.173608,44.328397],[-68.289409,44.283858],[-68.317588,44.225101],[-68.430946,44.298624],[-68.3581,44.392337],[-68.3791,44.430049],[-68.427874,44.3968],[-68.455095,44.447498],[-68.466109,44.377245],[-68.485415,44.434326],[-68.565161,44.39907],[-68.525302,44.227554],[-68.733004,44.328388],[-68.827197,44.31216],[-68.783679,44.473879],[-68.927452,44.448039],[-68.990767,44.415033],[-68.954465,44.32405],[-69.100863,44.104529],[-69.031878,44.079036],[-69.214205,43.935583],[-69.398455,43.971804],[-69.50329,43.837673],[-69.543912,43.881615],[-69.588551,43.81836],[-69.594705,43.858878],[-69.649798,43.836287],[-69.653337,43.79103],[-69.705838,43.823024],[-69.838689,43.70514],[-69.884066,43.778035],[-70.041351,43.738053],[-70.009869,43.859315],[-70.190014,43.771866],[-70.254144,43.676839],[-70.196911,43.565146],[-70.361214,43.52919],[-70.349684,43.442032],[-70.553854,43.321886],[-70.638355,43.114182],[-70.810069,42.909549],[-70.778671,42.693622],[-70.689402,42.653319],[-70.630077,42.692699],[-70.594014,42.63503],[-70.654727,42.582234],[-70.871382,42.546404],[-70.835991,42.490496],[-70.934993,42.457896],[-70.901992,42.420297],[-70.96047,42.446166],[-70.990595,42.407098],[-70.953022,42.343973],[-71.01568,42.326019],[-70.98909,42.267449],[-70.910941,42.265412],[-70.882764,42.30886],[-70.722269,42.207959],[-70.63848,42.081579],[-70.710034,41.999544],[-70.552941,41.929641],[-70.54103,41.815754],[-70.471552,41.761563],[-70.259205,41.713954],[-70.024734,41.787364],[-70.000188,41.886938],[-70.044995,41.930049],[-70.064084,41.878924],[-70.095595,42.032832],[-70.245385,42.063733],[-70.058531,42.040363],[-69.935952,41.809422],[-69.928261,41.6917],[-69.988215,41.554704],[-70.007011,41.671579],[-70.265424,41.609333],[-70.351634,41.634687],[-70.948431,41.409193],[-70.658659,41.543385],[-70.661475,41.681756],[-70.623652,41.707398],[-70.718739,41.73574],[-70.719575,41.685002],[-70.813286,41.65567],[-70.85222,41.589223],[-70.887643,41.632422],[-70.929722,41.609479],[-70.931545,41.540169],[-71.19302,41.457931],[-71.240709,41.619225],[-71.24071,41.474872],[-71.337695,41.448902],[-71.275234,41.619444],[-71.19564,41.67509],[-71.224798,41.710498],[-71.25956,41.642595],[-71.299159,41.649531],[-71.291217,41.702666],[-71.350057,41.727835],[-71.37791,41.666646],[-71.449318,41.687401],[-71.40377,41.589321],[-71.447712,41.5804],[-71.418404,41.472652],[-71.483295,41.371722],[-71.860513,41.320248]]],[[[-70.59628,41.471905],[-70.501306,41.385391],[-70.450431,41.420703],[-70.451084,41.348161],[-70.709826,41.341723],[-70.775665,41.300982],[-70.838777,41.347209],[-70.59628,41.471905]]],[[[-70.092142,41.297741],[-70.031332,41.339332],[-70.049053,41.391702],[-69.960181,41.264546],[-70.118669,41.242351],[-70.275526,41.310464],[-70.092142,41.297741]]],[[[-68.453236,44.189998],[-68.384903,44.154955],[-68.438518,44.11618],[-68.502096,44.152388],[-68.453236,44.189998]]],[[[-68.680773,44.279242],[-68.605906,44.230772],[-68.675056,44.137131],[-68.722956,44.219607],[-68.680773,44.279242]]],[[[-68.792139,44.237819],[-68.780055,44.203129],[-68.839422,44.236547],[-68.792139,44.237819]]],[[[-68.498637,44.369686],[-68.489641,44.313705],[-68.530394,44.333583],[-68.498637,44.369686]]],[[[-68.618212,44.012367],[-68.657031,44.003823],[-68.661594,44.075837],[-68.6181,44.096706],[-68.584074,44.070578],[-68.618212,44.012367]]],[[[-68.785601,44.053503],[-68.874139,44.025359],[-68.944597,44.11284],[-68.825067,44.186338],[-68.785601,44.053503]]],[[[-68.942826,44.281073],[-68.868444,44.38144],[-68.95189,44.218719],[-68.942826,44.281073]]],[[[-86.093536,45.007838],[-86.156689,45.010535],[-86.117908,45.048478],[-86.093536,45.007838]]],[[[-86.033174,45.15842],[-85.976803,45.138363],[-85.976883,45.06266],[-86.058653,45.100776],[-86.033174,45.15842]]],[[[-88.684434,48.115785],[-88.418244,48.18037],[-88.670073,48.011446],[-88.968903,47.901675],[-88.899698,47.902445],[-89.157738,47.824015],[-89.255202,47.876102],[-88.684434,48.115785]]],[[[-84.612845,45.834528],[-84.646876,45.884642],[-84.578328,45.820092],[-84.35602,45.771895],[-84.405852,45.722417],[-84.484128,45.73071],[-84.612845,45.834528]]],[[[-85.524448,45.829794],[-85.450206,45.776452],[-85.507263,45.778237],[-85.524448,45.829794]]],[[[-85.696872,45.69725],[-85.701809,45.736129],[-85.651866,45.743139],[-85.696872,45.69725]]],[[[-85.566441,45.760222],[-85.501267,45.754415],[-85.487026,45.621211],[-85.561634,45.572213],[-85.630016,45.598166],[-85.566441,45.760222]]],[[[-86.626187,45.573581],[-86.648792,45.543243],[-86.712328,45.610939],[-86.626187,45.573581]]],[[[-74.144428,40.53516],[-74.254588,40.502303],[-74.1894,40.642121],[-74.075884,40.648101],[-74.059184,40.593502],[-74.144428,40.53516]]],[[[-72.132225,41.104387],[-72.084207,41.101524],[-72.086975,41.058292],[-72.132225,41.104387]]],[[[-82.835118,41.708971],[-82.782719,41.694003],[-82.842099,41.628323],[-82.835118,41.708971]]],[[[-71.3312,41.580318],[-71.366165,41.66098],[-71.30555,41.622523],[-71.3312,41.580318]]],[[[-71.58955,41.196557],[-71.561093,41.224207],[-71.547051,41.153684],[-71.611706,41.153239],[-71.58955,41.196557]]],[[[-90.403306,47.026693],[-90.464079,46.994636],[-90.438734,47.072557],[-90.395367,47.077175],[-90.403306,47.026693]]],[[[-90.730883,46.873096],[-90.667776,46.890037],[-90.756052,46.830595],[-90.730883,46.873096]]],[[[-90.568938,46.847391],[-90.787751,46.753301],[-90.622048,46.872872],[-90.568938,46.847391]]],[[[-90.572383,46.958835],[-90.508157,46.956836],[-90.654796,46.919249],[-90.572383,46.958835]]],[[[-87.335299,45.211327],[-87.327284,45.157363],[-87.376777,45.177298],[-87.335299,45.211327]]],[[[-90.757147,47.03372],[-90.544875,47.017383],[-90.671581,46.948973],[-90.757147,47.03372]]],[[[-86.880572,45.331467],[-86.899891,45.295185],[-86.95499,45.34128],[-86.943041,45.41525],[-86.810055,45.422619],[-86.880572,45.331467]]]]},\"properties\":{\"name\":\"Connecticut\",\"nation\":\"USA \"}}]}","volume":"849","noUsgsAuthors":false,"publicationDate":"2022-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Spence Cheruvelil, Kendra","contributorId":348079,"corporation":false,"usgs":false,"family":"Spence Cheruvelil","given":"Kendra","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webster, Katherine","contributorId":348080,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Katelyn","contributorId":348081,"corporation":false,"usgs":false,"family":"King","given":"Katelyn","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poisson, Autumn C.","contributorId":348082,"corporation":false,"usgs":false,"family":"Poisson","given":"Autumn C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922926,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229101,"text":"70229101 - 2022 - Lessons learned from 20 y of monitoring suburban development with distributed stormwater management in Clarksburg, Maryland, USA","interactions":[],"lastModifiedDate":"2022-09-15T14:05:49.817277","indexId":"70229101","displayToPublicDate":"2022-02-25T06:18:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Lessons learned from 20 y of monitoring suburban development with distributed stormwater management in Clarksburg, Maryland, USA","docAbstract":"Urban development is a well-known stressor for stream ecosystems, presenting a challenge to managers tasked with mitigating its effects. For the past 20 y, streamflow, water quality, geomorphology, and benthic communities were monitored in 5 watersheds in Montgomery County, Maryland, USA. This study presents a synthesis of multiple studies of monitoring efforts in the study area and new analysis of more recent monitoring data to document the primary lessons learned from monitoring. The monitored watersheds include a forested control, an urban control with centralized stormwater management, and 3 suburban treatment watersheds featuring low-impact development and a high density of infiltration-focused stormwater facilities distributed across the watershed. Treatment watersheds were monitored before development, during construction, and after development. Monitoring was initiated to inform adaptive management of stormwater and impervious cover limits within the study area, with a focus on the impacts of distributed stormwater management. Results from our synthesis indicate that distributed stormwater management is advantageous compared with centralized stormwater management in numerous ways. Hydrologic benefits were greater with distributed stormwater infrastructure, demonstrating the ability to mitigate runoff volumes and peak flows and, for small storms, replicate predevelopment conditions. Baseflow temporarily increased during the construction phase in the treatment watersheds. Water-quality benefits were mixed, with declines in baseflow nitrate concentrations but limited changes to nitrate export and increases in specific conductance after development. Substantial topographic changes occurred during construction in the treatment watersheds, including changes within the riparian zone, despite riparian buffer protections. Ecological monitoring indicated that even though index of biotic integrity scores rebounded in some cases, sensitive benthic macroinvertebrate families did not fully recover in the treatment watersheds. Lessons learned from this synthesis highlight the importance of tracking multiple indicators of stream health and considering past land use and that more stormwater facilities distributed across the watershed is beneficial but cannot mitigate the effects of all urban stressors on aquatic ecosystems.
High fisheries production within estuaries is associated with coastal upwelling, tidal mixing, and land-based runoff facing increasing impacts from climate and human activities. Active river deltas receive large riverine inflows compared to inactive river deltas, providing contrasting estuaries to compare impacts of river inflow on estuarine nekton. We quantified nekton assemblages and stable isotopes (δ13C, δ15N) of commercially important blue crab Callinectes sapidus Rathbun, 1896 within an active and inactive delta in coastal Louisiana to explore the impacts of differing riverine inflow. Crustaceans dominated estuarine assemblages, differing only by season and not delta type, with summer and fall supporting highest densities. Fish density and assemblages differed by the interaction of season and delta due to differences during the 2019 record high spring river inflow. During this period, the active delta supported reduced fish densities and richness compared to the inactive delta. Nekton densities across deltas and seasons reflect a combination of species life history characteristics and habitat conditions. The high spring river discharge in 2019 impacted habitat availability (reduced presence of submerged aquatic vegetation), water conditions (decreased temperature and salinity), and potentially displaced nekton to unsampled habitat areas (i.e. interior marsh surface) within the active delta. While differences in nekton density and assemblages were only evident during the high spring river discharge, δ15N values of blue crabs were approximately 1.5 times higher in the active delta, potentially indicating more terrestrial influence. Understanding how altered inflow impacts environmental variables supporting estuarine nekton production remains critical for supporting management within these hydrologically managed regions.
","language":"English","publisher":"Inter-Research","doi":"10.3354/ab00748","usgsCitation":"Taylor, C.B., Nyman, J.A., and La Peyre, M., 2022, Nekton community dynamics within active and inactive deltas in a major river estuary: Potential implications for altered hydrology regimes: Aquatic Biology, v. 31, p. 1-18, https://doi.org/10.3354/ab00748.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-132543","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":448690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/ab00748","text":"Publisher Index Page"},{"id":397188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Delta Basin, Terrebonne Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.4337158203125,\n 29.16655229520015\n ],\n [\n -89.2694091796875,\n 29.16655229520015\n ],\n [\n -89.2694091796875,\n 30.088107753367257\n ],\n [\n -91.4337158203125,\n 30.088107753367257\n ],\n [\n -91.4337158203125,\n 29.16655229520015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Caleb B.","contributorId":288505,"corporation":false,"usgs":false,"family":"Taylor","given":"Caleb","email":"","middleInitial":"B.","affiliations":[{"id":61780,"text":"School of Renewable Natural Resources","active":true,"usgs":false}],"preferred":false,"id":838032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nyman, John Andrew","contributorId":288506,"corporation":false,"usgs":false,"family":"Nyman","given":"John","email":"","middleInitial":"Andrew","affiliations":[{"id":61780,"text":"School of Renewable Natural Resources","active":true,"usgs":false}],"preferred":false,"id":838033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"La Peyre, Megan K. 0000-0001-9936-2252","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":264343,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838031,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228918,"text":"70228918 - 2022 - Analyzing the effects of land cover change on the water balance for case study watersheds in different forested ecosystems in the USA","interactions":[],"lastModifiedDate":"2022-02-24T18:00:28.672968","indexId":"70228918","displayToPublicDate":"2022-02-21T11:57:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing the effects of land cover change on the water balance for case study watersheds in different forested ecosystems in the USA","docAbstract":"We analyzed impacts of interannual disturbance on the water balance of watersheds in different forested ecosystem case studies across the United States from 1985 to 2016 using a remotely sensed long-term land cover monitoring record (U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science products), gridded precipitation and evaporation data, and streamgaging data using paired watersheds (high and low disturbance). LCMAP products were used to quantify the timing and degree of interannual disturbance and to gain a better understanding of how land cover change affects the water balance of disturbed watersheds. In this paper, we present how LCMAP science products can be used to improve knowledge for hydrologic modeling, climate research, and forest management. Anthropogenic influences (e.g., dams and irrigation diversions) often minimize the impacts of land cover change on water balance dynamics when compared to interannual fluctuations of hydroclimatic events (e.g., drought and flooding). Our findings show that each watershed exhibits a complex suite of influences involving climate variables and other factors that affect each of their water balances differently when land cover change occurs. In this study, forests within arid to semi-arid climates experience greater water balance effects from land cover change than watersheds where water is less limited.
","language":"English","publisher":"MDPI","doi":"10.3390/land11020316","usgsCitation":"Healey, N.C., and Rover, J., 2022, Analyzing the effects of land cover change on the water balance for case study watersheds in different forested ecosystems in the USA: Land, v. 11, no. 2, 316, 43 p., https://doi.org/10.3390/land11020316.","productDescription":"316, 43 p.","ipdsId":"IP-130474","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":448718,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land11020316","text":"Publisher Index Page"},{"id":396438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"11","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Healey, Nathan C. 0000-0002-8516-2636","orcid":"https://orcid.org/0000-0002-8516-2636","contributorId":280023,"corporation":false,"usgs":false,"family":"Healey","given":"Nathan","email":"","middleInitial":"C.","affiliations":[{"id":57411,"text":"KBR, Inc.","active":true,"usgs":false}],"preferred":false,"id":835894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":835895,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228902,"text":"70228902 - 2022 - Wildfire probability models calibrated using past human and lightning ignition patterns can inform mitigation of post-fire hydrologic hazards","interactions":[],"lastModifiedDate":"2022-02-23T12:42:18.701658","indexId":"70228902","displayToPublicDate":"2022-02-17T06:40:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1799,"text":"Geomatics, Natural Hazards and Risk","active":true,"publicationSubtype":{"id":10}},"title":"Wildfire probability models calibrated using past human and lightning ignition patterns can inform mitigation of post-fire hydrologic hazards","docAbstract":"Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport. We used historical ignition data (1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
Given limited funding, natural resources decision making is riddled with tradeoffs, including which species or landscapes to prioritize for management action. Florida’s Everglades wetland is home to numerous indicator species, some of which are endangered. But with a multitude of species comes differing hydrologic requirements to yield appropriate foraging and breeding conditions for each. The Everglades ecosystem is highly managed, with water being moved across the landscape to meet the habitat and reproductive needs of species of concern. Predictive modeling can help water managers understand potential consequences to targeted water conditions. EverForecast is a novel spatially explicit, hydrologic, and ecological operational forecast developed to inform conservation management decisions. Not only does EverForecast provide probable near-term water conditions, but also predicted species responses to those hydrologic conditions. Using examples from two focal regions of the Everglades, we show the magnitude of impacts to a suite of species and an almost 70% decline in suitable conditions for one species when prioritizing water management to meet the needs of another species. Although EverForecast is a relatively new decision support tool, its hydrologic outputs are already commonly used to make water management recommendations because it provides near-term hydrologic forecasts that scientists and managers need for water operations decision making. Because species management decisions have historically been made to target a single species at a time, it may take longer for full utility of EverForecast’s ability to quantify tradeoffs among species to become integrated into decision making.
The Atchafalaya River Basin is the largest remaining forested wetland in the contiguous United States. Since 1960, dredging and channel erosion in the Basin have resulted in changes to the hydrologic connectivity that have not been quantified. Analyses were conducted to determine the hydraulic and geomorphic factors that have changed since discharge became controlled that may have decreased river/floodplain connectivity. We examined: (1) stage/discharge relationships from 1960 to 2014; (2) hydroperiods across the floodplain; (3) discharge distribution to the floodplain by comparing discharge measurements from 1959–1968 to 2005–2012; and (4) channel cross-sections and floodplain elevations. Our results indicate that much of the floodplain no longer receives headwater discharge (upstream to downstream, > 200 km2) or receives too little discharge to alleviate stagnancy and hypoxia in the forested wetland at lower stages. Large portions of the Basin (400 km2) have low water levels controlled by channel geomorphology and sea-level rise that inundate the forested floodplain for more than 50% of the calendar year. This extended duration of inundation contributes to hypoxia and likely reduces nutrient retention. The confinement of discharge to a large efficient channel compromises the ability of this system to respond to sea-level rise and subsidence. This study provides insight to the effects of flood management projects along Coastal Plain rivers and deltas.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5347","usgsCitation":"Kroes, D., Demas, C.R., Allen, Y., Day, R., Roberts, S.W., and Varisco, J., 2022, Hydrologic modification and channel evolution degrades connectivity on the Atchafalaya River floodplain: Earth Surface Processes and Landforms, v. 47, no. 7, p. 1790-1807, https://doi.org/10.1002/esp.5347.","productDescription":"18 p.","startPage":"1790","endPage":"1807","ipdsId":"IP-094587","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":448787,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5347","text":"Publisher Index Page"},{"id":435966,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94GULXE","text":"USGS data release","linkHelpText":"Mean bed elevations of waterbodies on the Atchafalaya River floodplain"},{"id":398209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Atchafalaya River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.09814453125,\n 29.36302703778376\n ],\n [\n -90.977783203125,\n 29.36302703778376\n ],\n [\n -90.977783203125,\n 31.956823015897207\n ],\n [\n -93.09814453125,\n 31.956823015897207\n ],\n [\n -93.09814453125,\n 29.36302703778376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroes, Daniel 0000-0001-9104-9077 dkroes@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-9077","contributorId":3830,"corporation":false,"usgs":true,"family":"Kroes","given":"Daniel","email":"dkroes@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":839850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demas, Charles R","contributorId":289813,"corporation":false,"usgs":false,"family":"Demas","given":"Charles","email":"","middleInitial":"R","affiliations":[{"id":38437,"text":"Retired, U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":839851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Yvonne A.","contributorId":289815,"corporation":false,"usgs":false,"family":"Allen","given":"Yvonne A.","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":839852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":222817,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Steve W","contributorId":289819,"corporation":false,"usgs":false,"family":"Roberts","given":"Steve","email":"","middleInitial":"W","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":839854,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Varisco, Jeff","contributorId":289821,"corporation":false,"usgs":false,"family":"Varisco","given":"Jeff","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":839855,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226911,"text":"ofr20211104A - 2022 - Potential effects of climate change on snail kites (Rostrhamus sociabilis plumbeus) in Florida","interactions":[{"subject":{"id":70226911,"text":"ofr20211104A - 2022 - Potential effects of climate change on snail kites (Rostrhamus sociabilis plumbeus) in Florida","indexId":"ofr20211104A","publicationYear":"2022","noYear":false,"chapter":"A","displayTitle":"Potential Effects of Climate Change on Snail Kites (Rostrhamus sociabilis plumbeus) in Florida","title":"Potential effects of climate change on snail kites (Rostrhamus sociabilis plumbeus) in Florida"},"predicate":"IS_PART_OF","object":{"id":70228323,"text":"ofr20211104 - 2022 - Effects of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"id":1}],"isPartOf":{"id":70228323,"text":"ofr20211104 - 2022 - Effects of climate change on fish and wildlife species in the United States","indexId":"ofr20211104","publicationYear":"2022","noYear":false,"title":"Effects of climate change on fish and wildlife species in the United States"},"lastModifiedDate":"2023-10-23T20:02:06.711898","indexId":"ofr20211104A","displayToPublicDate":"2022-02-15T08:11:16","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1104","chapter":"A","displayTitle":"Potential Effects of Climate Change on Snail Kites (Rostrhamus sociabilis plumbeus) in Florida","title":"Potential effects of climate change on snail kites (Rostrhamus sociabilis plumbeus) in Florida","docAbstract":"The snail kite (Rostrhamus sociabilis plumbeus), an endangered, wetland-dependent raptor, is highly sensitive to changes in hydrology. Climate-driven changes in water level will likely affect snail kite populations—altering reproductive success and survival rates. Identifying the mechanisms mediating the direct and indirect effects of climate on snail kite populations and the range of future climate conditions is important to the conservation of this species. When water levels are low, snail kite nest initiation and nest success decrease owing to decreased availability of their primary prey applesnails (Pomacea spp.), unstable nesting sites, and increased predator access. Dry events also lead to decreased adult and juvenile survival. In the next 80 years, temperatures and potential evapotranspiration are projected to increase in central and southern Florida. Although future precipitation volume is more uncertain, increased temperatures and evaporative loss may lead to increased frequency, duration, and severity of low-water events. Additionally, rapidly rising water levels have adverse effects on snail kite reproductive success—destroying nests, preventing access to apple snails, and reducing apple snail productivity. Finally, it is likely that future climate will favor more frequent dry conditions and extreme heavy rainfall events, both of which are directly linked to decreased reproductive success and survival. The potential effects of climate change may be buffered by the availability of alternative prey (non-native applesnails) that are more tolerant of anticipated conditions. In highly controlled southern Florida waterbodies, regional water-management decisions may buffer or exacerbate waterbody accession and recession rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211104A","usgsCitation":"Lyons, M.P., LeDee, O.E., and Boyles, R., 2021, Potential effects of climate change on snail kites (Rostrhamus sociabilis plumbeus) in Florida: U.S. Geological Survey Open-File Report 2021–1104–A, 12 p., https://doi.org/10.3133/ofr20211104A.","productDescription":"vi, 12 p.","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-131203","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":393183,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1104/a/images"},{"id":393182,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1104/a/ofr20211104A.XML","size":"75.8 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1104 XML"},{"id":393181,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1104/a/ofr20211104A.pdf","text":"Report","size":"5.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1104"},{"id":393180,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1104/a/coverthb3.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park, Lake Okeechobee, Kissimmee Chain of Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.0791015625,\n 25.64152637306577\n ],\n [\n -80.408935546875,\n 25.64152637306577\n ],\n [\n -80.408935546875,\n 26.204734267107604\n ],\n [\n -81.0791015625,\n 26.204734267107604\n ],\n [\n -81.0791015625,\n 25.64152637306577\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.287841796875,\n 26.598351182358293\n ],\n [\n -80.496826171875,\n 26.598351182358293\n ],\n [\n -80.496826171875,\n 27.293689224852407\n ],\n [\n -81.287841796875,\n 27.293689224852407\n ],\n [\n -81.287841796875,\n 26.598351182358293\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.73553466796874,\n 27.761329874505204\n ],\n [\n -80.96649169921874,\n 27.761329874505204\n ],\n [\n -80.96649169921874,\n 28.497660832963447\n ],\n [\n -81.73553466796874,\n 28.497660832963447\n ],\n [\n -81.73553466796874,\n 27.761329874505204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Midwest Climate Adaptation Science Center
U.S. Geological Survey
1954 Buford Avenue
St Paul, MN 55108