The North American Wetlands Conservation Act (16 U.S.C. 4401-4412) provided funding and administration for wetland management and conservation projects. The North American Wetland Conservation Fund, enabled in 1989 with the Act, provides financial resources. Resource allocation decisions are based, in part, on regional experts, particularly migratory bird Joint Ventures (JVs) (i.e., partnerships for cooperative planning and coordinated management of the continent’s waterfowl populations and habitats). The JVs evaluate funding proposals submitted with their respective regions each year and make funding recommendations to decision makers. Proposal evaluation procedures differ among JVs, however, it could be helpful to consider a transparent, repeatable, and data-driven framework for prioritization within regions. We used structured decision making and linear additive value models for ranking proposals within JV regions. We used two JVs as case studies and constructed two different value models using JV-specific objectives and weights. The framework was developed through a collaborative process with JV staff and stakeholders. Models were written in Microsoft Excel. To test these models, we used six NAWCA proposals submitted to the Upper Mississippi / Great Lakes Joint Venture in 2016 and seven proposals submitted to the Gulf Coast Joint Venture in 2017. We compared proposal ranks assigned by the value model to ranks assigned by each JV’s management board. Ranks assigned by the value model differed from ranks assigned by the board for the Upper Mississippi / Great Lakes Joint Venture, but not for the Gulf Coast Joint Venture. However, ranks from the value model could change markedly with different objective weights and value functions. The weighted linear value model was beneficial for ranking NAWCA proposals because it allows JVs to treat the ranking as a multiple objective problem and tailor the ranking to their specific regional concerns. We believe a structured decision making approach could be adapted by JV staff to facilitate a systematic and transparent process for proposal ranking by their management boards.
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States\"}}]}","volume":"13","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Krainyk, Anastasia 0000-0002-3100-9011","orcid":"https://orcid.org/0000-0002-3100-9011","contributorId":214391,"corporation":false,"usgs":true,"family":"Krainyk","given":"Anastasia","email":"","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soulliere, Gregory J.","contributorId":172329,"corporation":false,"usgs":false,"family":"Soulliere","given":"Gregory","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":844087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coluccy, John M.","contributorId":111382,"corporation":false,"usgs":true,"family":"Coluccy","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844088,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Barry C.","contributorId":12968,"corporation":false,"usgs":true,"family":"Wilson","given":"Barry","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":844089,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brasher, Michael G.","contributorId":214393,"corporation":false,"usgs":false,"family":"Brasher","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":36215,"text":"Ducks Unlimited","active":true,"usgs":false}],"preferred":false,"id":844090,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Al-Saffar, Mohammed A","contributorId":292215,"corporation":false,"usgs":false,"family":"Al-Saffar","given":"Mohammed","email":"","middleInitial":"A","affiliations":[{"id":62842,"text":"USWFS","active":true,"usgs":false}],"preferred":false,"id":844091,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Humburg, Dale D.","contributorId":79357,"corporation":false,"usgs":false,"family":"Humburg","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":13073,"text":"Ducks Unlimited, Inc.","active":true,"usgs":false}],"preferred":false,"id":844092,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231934,"text":"70231934 - 2022 - Can coastal habitats rise to the challenge? Resilience of estuarine habitats, carbon accumulation, and economic value to sea-level rise in a Puget Sound estuary","interactions":[],"lastModifiedDate":"2022-10-31T14:24:53.776047","indexId":"70231934","displayToPublicDate":"2022-06-03T08:09:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Can coastal habitats rise to the challenge? Resilience of estuarine habitats, carbon accumulation, and economic value to sea-level rise in a Puget Sound estuary","docAbstract":"Sea-level rise (SLR) and obstructions to sediment delivery pose challenges to the persistence of estuarine habitats and the ecosystem services they provide. Restoration actions and sediment management strategies may help mitigate such challenges by encouraging the vertical accretion of sediment in and horizontal migration of tidal forests and marshes. We used a process-based soil accretion model (Coastal Wetland Equilibrium Model) combined with a habitat classification model (MOSAICS) to estimate the effects of SLR, suspended sediment, and inland habitat migration on estuarine habitats, soil carbon accumulation, and economic value of climate change mitigation of carbon accumulation (social cost of carbon dioxide) in a macrotidal estuary in the northwest USA over 100 years (2011 to 2110). Under present-day sediment levels, we projected that after 100 years, most high salt marsh would remain with < 100 cm SLR, but substantial area converted to transitional (low) salt marsh and mudflat with ≥ 100 cm SLR. Increasing sediment availability increased the projected resilience of transitional salt marsh to SLR but did not prevent declines in high marsh area. Projected total carbon accumulation plateaued or declined with ≥ 100 cm SLR, yet the economic value of carbon accumulation continued to rise over time, suggesting that the value of this ecosystem service was resilient to SLR. Doubling or tripling sediment availability increased projected carbon accumulation up to 7.69 and 14.2 kg m−2 and increased total economic value up to $373,000 and $710,000, respectively. Allowing marsh migration supported conversion of upland to freshwater marsh, with slight increases in carbon accumulation. These results inform climate adaptation planning for wetland managers seeking to understand the resilience of estuarine habitats and ecosystem services to SLR under multiple management strategies.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-022-01087-5","usgsCitation":"Moritsch, M.M., Byrd, K.B., Davis, M.J., Good, A.J., Drexler, J.Z., Morris, J.T., Woo, I., Windham-Myers, L., Grossman, E.E., Nakai, G., Poppe, K.L., and Rybczyk, J.M., 2022, Can coastal habitats rise to the challenge? Resilience of estuarine habitats, carbon accumulation, and economic value to sea-level rise in a Puget Sound estuary: Estuaries and Coasts, v. 45, p. 2293-2309, https://doi.org/10.1007/s12237-022-01087-5.","productDescription":"17 p.","startPage":"2293","endPage":"2309","ipdsId":"IP-134918","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447559,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-022-01087-5","text":"Publisher Index Page"},{"id":435828,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ONIUCK","text":"USGS data release","linkHelpText":"Projected future habitat, elevation change, and carbon accumulation of coastal wetlands in the Nisqually River Delta, Washington"},{"id":401676,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.72981643676756,\n 47.05269803965603\n ],\n [\n -122.67969131469727,\n 47.05515408550348\n ],\n [\n -122.68999099731445,\n 47.06521099790879\n ],\n [\n -122.6905059814453,\n 47.07526601334617\n ],\n [\n -122.67539978027342,\n 47.089760603235646\n ],\n [\n -122.67127990722655,\n 47.1067049202684\n ],\n [\n -122.7366828918457,\n 47.10647124222482\n ],\n [\n -122.7385711669922,\n 47.10647124222482\n ],\n [\n -122.7389144897461,\n 47.07175866526645\n ],\n [\n -122.7344512939453,\n 47.06684799015446\n ],\n [\n -122.72981643676756,\n 47.05269803965603\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Moritsch, Monica Mei Jeen 0000-0002-3890-1264","orcid":"https://orcid.org/0000-0002-3890-1264","contributorId":225210,"corporation":false,"usgs":true,"family":"Moritsch","given":"Monica","email":"","middleInitial":"Mei Jeen","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":844144,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":844145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":844146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Good, Anthony J. 0000-0002-0276-136X","orcid":"https://orcid.org/0000-0002-0276-136X","contributorId":203553,"corporation":false,"usgs":true,"family":"Good","given":"Anthony","email":"","middleInitial":"J.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":844147,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844148,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morris, James T.","contributorId":29118,"corporation":false,"usgs":true,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":844149,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":844150,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844151,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":844152,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"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":844153,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Poppe, Katrina L. 0000-0003-2830-4578","orcid":"https://orcid.org/0000-0003-2830-4578","contributorId":292257,"corporation":false,"usgs":false,"family":"Poppe","given":"Katrina","email":"","middleInitial":"L.","affiliations":[{"id":62851,"text":"Western Washington University, Department of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":844154,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rybczyk, John M.","contributorId":89794,"corporation":false,"usgs":true,"family":"Rybczyk","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844155,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70231846,"text":"sir20215113 - 2022 - Long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i","interactions":[],"lastModifiedDate":"2026-04-02T19:46:57.445783","indexId":"sir20215113","displayToPublicDate":"2022-06-03T08:07:20","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":"2021-5113","displayTitle":"Long-Term Groundwater Availability in the Waihe‘e, ‘Īao, and Waikapū Aquifer Systems, Maui, Hawai‘i","title":"Long-term groundwater availability in the Waihe‘e, ‘Īao, and Waikapū aquifer systems, Maui, Hawai‘i","docAbstract":"Groundwater levels have declined since the 1940s in the Wailuku area of central Maui, Hawai‘i, on the eastern flank of West Maui volcano, mainly in response to increased groundwater withdrawals. Available data since the 1980s also indicate a thinning of the freshwater lens and an increase in chloride concentrations of pumped water from production wells. These trends, combined with projected increases in demand for groundwater in central Maui, have led to concerns over groundwater availability and have highlighted a need to improve general understanding of the hydrologic effects of proposed groundwater withdrawals in the Waihe‘e, ‘Īao, and Waikapū areas of central Maui.
A numerical groundwater model was constructed to simulate the flow and salinity of groundwater in central Maui. The model simulates the effects of changes in groundwater withdrawals and recharge on water levels, freshwater-lens thicknesses, and chloride concentrations of pumped water from production wells. The model incorporates updated water-budget estimates of groundwater recharge from infiltration and direct recharge, seepage in stream channels, and inflow from inland areas. Mean annual groundwater recharge from infiltration and direct recharge was estimated using a daily water-budget model and the most current data, including the distributions of monthly rainfall and potential evapotranspiration, for the study area for nine historical periods from 1926 through 2012: 1926–69, 1970–79, 1980–84, 1985–89, 1990–94, 1995–99, 2000–04, 2005–09, and 2010–12. The water-budget model also estimated groundwater recharge based on one hypothetical scenario that used 1980–2010 rainfall and 2017 land cover. For the nine historical periods, estimated recharge from infiltration and direct recharge within the area of the groundwater model ranged from 30.4 million gallons per day (Mgal/d) during 2010–12 to 98.7 Mgal/d during 1926–69. Variability in recharge during these periods mainly reflects changes in rainfall and irrigation over time. Between 2010 and 2014, streamflow restoration in previously diverted streams resulted in an estimated increase in recharge from seepage in stream channels of about 12.5 Mgal/d. Average groundwater inflow of about 39.6 Mgal/d from inland, dike-intruded areas to the main area of interest was estimated from an existing island-wide numerical groundwater-flow model, which is at a larger scale and incorporates a greater number of simplifying assumptions.
The numerical groundwater model developed for this study was calibrated to 1926–2012 transient water levels, vertical salinity profiles, and chloride concentrations of water pumped by production wells in the study area. The model was then used to evaluate one future recharge and six selected withdrawal scenarios, developed in consultation with the Maui Department of Water Supply, in terms of long-term changes in water level and 50-percent ocean-water salinity surface. The groundwater model was also used to simulate the future salinity of water withdrawn by existing and proposed production wells. The simulations were run to steady-state conditions, providing an estimate of the long-term effects of changes in withdrawal and recharge on the groundwater resource. Results of the simulated future withdrawal scenarios indicate that, relative to 2017–18 rates, the scenarios’ long-term effect of increased withdrawals ultimately leads to lower water levels and a higher 50-percent ocean-water salinity surface indicating a thinning of the freshwater lens. Results also indicate that the increased withdrawals produce some groundwater with chloride concentration below 250 milligrams per liter and some groundwater with higher chloride concentration. The amount of drawdown near production wells and the quality of water withdrawn from production wells is dependent on the rate and spatial distribution of the withdrawals.
The model was also used to evaluate how groundwater availability may be affected for a drier recharge scenario based on a published study of future climate. Model results of the future recharge scenario indicate that the rate of groundwater recharge is a controlling factor for (1) water levels, (2) the 50-percent ocean-water salinity surface, and (3) the quality of water withdrawn from production wells in the Wailuku area. Coupled with reduced groundwater recharge (with all other factors remaining equal), the modeled future withdrawals in the scenario would tend to cause lower water levels, a higher 50-percent ocean-water salinity surface, and increased salinity of water withdrawn from production wells.
The three-dimensional numerical groundwater model developed for this study utilizes the latest available hydrologic and geologic information and is a useful tool for understanding the long-term hydrologic effects of additional groundwater withdrawals in central Maui. The model has several limitations, including its non-uniqueness and inability to account for local-scale heterogeneities. Short-term effects of changes in recharge and withdrawals—and optimization of pumping rates to meet increased demand for water with acceptable salinity—are possible conditions for future simulation analyses.
Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
We present and demonstrate a recursive-estimation framework to infer groundwater/surface-water exchange based on temperature time series collected at different vertical depths below the sediment/water interface. We formulate the heat-transport problem as a state-space model (SSM), in which the spatial derivatives in the convection/conduction equation are approximated using finite differences. The SSM is calibrated to estimate time-varying specific discharge using the Extended Kalman Filter (EKF) and Extended Rauch-Tung-Striebel Smoother (ERTSS). Whereas the EKF is suited to real-time (“online”) applications and uses only the past and current measurements for estimation (filtering), the ERTSS is intended for near-real time or batch-processing (“offline”) applications and uses a window of data for batch estimation (smoothing). The two algorithms are demonstrated with synthetic and field-experimental data and are shown to be efficient and rapid for the estimation of time-varying flux over seasonal periods; further, the recursive approaches are effective in the presence of rapidly changing flux and (or) nonperiodic thermal boundary conditions, both of which are problematic for existing approaches to heat tracing of time-varying groundwater/surface-water exchange.
No abstract available.
","language":"English","publisher":"International Association for Great Lakes Research","usgsCitation":"Adams, J.V., 2022, Sailing into new waters with IAGLR: IAGLR Lakes Letter, no. Summer 2022.","productDescription":"1 p.","startPage":"2","ipdsId":"IP-143767","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":425745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425739,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://iaglr.org/lakesletter/issues/","linkFileType":{"id":5,"text":"html"}}],"issue":"Summer 2022","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":857805,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70231845,"text":"ofr20221013 - 2022 - Water-budget accounting for tropical regions model (WATRMod) documentation","interactions":[],"lastModifiedDate":"2026-03-27T19:49:35.907978","indexId":"ofr20221013","displayToPublicDate":"2022-06-01T11:17:20","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":"2022-1013","displayTitle":"Water-Budget Accounting for Tropical Regions Model (WATRMod) Documentation","title":"Water-budget accounting for tropical regions model (WATRMod) documentation","docAbstract":"Regional groundwater recharge commonly is estimated using a threshold-type water-budget approach in which groundwater recharge is assumed to occur when water in the plant-root zone exceeds the soil’s moisture storage capacity. A water budget of the plant-soil system accounts for water inputs (rainfall, fog interception, irrigation, septic-system leachate, and other inputs), water outputs (runoff, evaporation, transpiration, and recharge), and changes in stored water during a specified time interval. Water budgets can be computed on any desired interval, including annual, monthly, daily, and subdaily intervals. In general, uncertainty in recharge estimates is expected to be lower using daily or subdaily intervals relative to monthly and annual intervals. Average recharge rates computed over a period of a year or multiple years are commonly determined from water budgets computed using a daily computation interval capable of capturing rainfall and land-cover changes during the period.
This report documents the Water-budget Accounting for Tropical Regions Model, or WATRMod, code that can be used to estimate spatially variable, daily water-budget components in tropical-island and other appropriate settings. The purpose of this report is to provide descriptions of WATRMod’s (1) approach to computing a daily water budget, (2) represented processes, (3) limitations, and (4) execution procedure, input requirements, output files, and example files. The model computes a daily water budget for each hydrologically independent subarea within the overall study area. A subarea is defined by its climatic, soil, land-cover, and human-related (for example, adding irrigation or other water) characteristics. The water-budget model can represent processes including rainfall, fog interception, irrigation, septic-system leachate, direct recharge that bypasses the plant-soil system, runoff, canopy evaporation in forested areas, evapotranspiration, and groundwater recharge. The water-budget model can represent either one of the following different accounting orders: (1) accounting for loss of water by evapotranspiration before accounting for recharge, and (2) accounting for recharge before accounting for evapotranspiration. WATRMod’s limitations include: (1) uncharacterized, subdaily transient changes in water inputs and outputs from the plant-soil system, (2) unrepresented precipitation in the form of snow and sublimation, and (3) routing runoff from one subarea to an adjacent subarea that is not directly represented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221013","usgsCitation":"Oki, D.S., 2022, Water-budget accounting for tropical regions model (WATRMod) documentation: U.S. Geological Survey Open-File Report 2022-1013, 77 p., https://doi.org/10.3133/ofr20221013.","productDescription":"Report: viii, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","ipdsId":"IP-126805","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501758,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113077.htm","linkFileType":{"id":5,"text":"html"}},{"id":401381,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPAY41","text":"WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files","description":"Oki, D.S., 2022, WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files: U.S. Geological Survey data release, https://doi.org/10.5066/P9VPAY41."},{"id":401379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1013/covrthb.jpg"},{"id":401380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1013/ofr20221013.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1013"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.3848876953125,\n 20.555652403773365\n ],\n [\n -156.0003662109375,\n 20.6379249854131\n ],\n [\n -155.9454345703125,\n 20.776659051878816\n ],\n [\n -156.26678466796875,\n 20.964004409178308\n ],\n [\n -156.47003173828125,\n 20.925527866647226\n ],\n [\n -156.610107421875,\n 21.056307701901847\n ],\n [\n -156.72271728515625,\n 20.94604992010052\n ],\n [\n -156.67327880859375,\n 20.822875478868443\n ],\n [\n -156.55792236328122,\n 20.761250430919652\n ],\n [\n -156.48651123046875,\n 20.771523019513364\n ],\n [\n -156.4617919921875,\n 20.622502259344817\n ],\n [\n -156.3848876953125,\n 20.555652403773365\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The annual Lake Ontario April bottom trawl survey and Alewife, Alosa pseudoharengus, population assessment provide science to inform management decisions related to predator-prey balance and fish community dynamics. The 2022 survey was conducted from March 31 to April 26, included 235 trawls in the main lake and embayments, and sampled depths from 5 to 219 m (16 – 723 ft). The survey captured 311,770 fish from 30 species with a total weight of 7,740 kg (17,028 lbs.). Alewife were 85% of the catch by number while Rainbow Smelt, Osmerus mordax, Round Goby, Neogobius melanostomus, and Deepwater Sculpin, Myoxocephalus thompsonii, comprised 6%, 4%, and 4% of the catch, respectively. The 2022 biomass index for Rainbow Smelt decreased 80% relative to the high values observed in 2021 as did the value for Cisco, Coregonus artedi, (46% decline). Emerald Shiner, Notropis atherinoides, biomass index increased in 2021 and Threespine Stickleback, Gasterosteus aculeatus, biomass remained low. No Bloater, Coregonus hoyi, were captured during the 2022 survey.
In 2022, Alewife biomass in U.S. waters (58.1 kilograms per hectare, kg·ha-1) was substantially higher than Canadian waters (26.3 kg·ha-1). The 2022 Alewife biomass index (41.6 kg·ha-1) decreased 10% from 2021 while the 2022 density index decreased 62% from 2021. Prediction modeling indicated the growth of the abundant 2020 Alewife year class, sampled as age-1 fish in 2021, would cause the adult Alewife biomass to increase in 2022. Although the adult Alewife biomass did increase relative to 2021 (61%), the increase was lower than predicted. The difference between the predictions and observations was because survival of age-1 fish from 2021 to 2022 was lower than had previously been observed. In the three previous years of observations the proportion of age-1 Alewife surviving to age 2 ranged from 0.33 to 0.53; however, that proportion was only 0.21 from 2021 to 2022. Survival estimates of Alewife age-5 through age-8 were higher than previously observed, possibly because salmonid predation focused on the abundant younger Alewife. The catch of age-1 Alewife in 2022, which is a measure of reproductive success in 2021, was below average and similar to the abundances of the 2018 and 2019 year classes. Simulation modeling results indicated the adult Alewife biomass is likely to increase slightly in 2023, whereas predictions for 2024 are less certain.
Hydroacoustic sampling was used to estimate prey fish densities in open-water, pelagic habitats not sampled by the bottom trawl. Bottom trawl-based densities from the lake bottom were at least 25 times greater than densities of prey fish in the water column above the trawl. These results support the idea that, in April, when the warmest, most dense water is on the lake bottom, Alewife and most other pelagic prey fish primarily inhabit deep, near bottom habitats and can be effectively sampled with bottom trawling.
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The aerospace and defense sectors remain large players in the State’s economy to this day. Boeing, Lockheed Martin, General Electric Aviation, and others maintain a presence in the Tar Heel State, and military installations dot the landscape.
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Carolina\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The granular formulation of Bayluscide [Bayluscide 3.2% Granular Sea Lamprey Larvicide, granular Bayluscide (gB)] is applied in lentic and lotic systems to survey (assessment) and kill (treatment) larval sea lampreys (Petromyzon marinus; Linnaeus, 1758) in the Great Lakes basin. Granules are spread on the water surface, settle to the sediment surface, and dissolve. The potential risk of niclosamide exposure (2′,5-dichloro-4′-nitrosalicylanilide), the active ingredient of gB, to non-target organisms located downstream of survey plots, is a concern of partner agencies (state-level natural resource departments, U.S. Fish and Wildlife Service Ecological Services, and Fisheries and Oceans Canada Species at Risk Branch). Spatiotemporal distribution of niclosamide in the water column and sediment was evaluated in and downstream of five larval survey plots in two rivers following the application of gB. Water samples were collected at 0.25, 2, 4, 6, 8, and 24 h from three depths in the water column (10 cm above the sediment, ½ the water column depth, and the water surface) at three locations inside each survey plot, and 1 meter upstream from three sediment sample grids positioned 10, 30, and 100 m downstream. Sediment samples were collected from inside the grids at 0.25, 2, 4, 6, 8, and 24 h, and from inside the survey plots, 8 and 24 h after gB application. Niclosamide was detected in the sediment and water at all sample locations. From 2 to 24 h after application, average water concentrations 1) varied between study sites, 2) decreased from the survey plots to 100 m downstream, 3) varied by depth in the water column, and 4) decreased over time. Average sediment concentrations varied by distance downstream and time post-application, but not by study site or river. Data suggest there would be negligible exposure to non-target organisms downstream of a gB survey plot based on low niclosamide concentrations measured in the water and sediment. The depletion rate of niclosamide was also evaluated in St. Clair River sediment dosed at the field application rate. Niclosamide concentration decreased at a rate of 2.28% per hour over the 24 hours measured, equating to a half-life of 1.27 days. This indicates the length of time an organism in the sediment in a survey plot might be exposed. Underwater cameras were placed along the edge of two St. Clair River survey plots to document gB distribution on the sediment and any potential target and non-target effects. Video was inconclusive in tracking gB through the water column. Larval sea lamprey and non-target mortality were not observed. Additional video footage of one St. Clair River survey plot showed large areas of river bottom without gB.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Kaye, C., Bernardy, J.A., Schueller, J., Schloesser, N., Henson, M., Andresen, C.K., and Kirkeeng, C., 2022, Distribution of niclosamide following granular Bayluscide applications in lotic systems: Project Completion Report, 51 p.","productDescription":"51 p.","ipdsId":"IP-124289","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":430758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430738,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Michigan","county":"Alger County, St. Clair County","otherGeospatial":"Au Train River, St. Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.57645899273449,\n 42.61812499759989\n ],\n [\n -82.64420763126448,\n 42.60751672353578\n ],\n [\n -82.69105509407704,\n 42.5650651070078\n ],\n [\n -82.58510775509997,\n 42.58947764353198\n ],\n [\n -82.57645899273449,\n 42.61812499759989\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.85011260876064,\n 46.41879658960383\n ],\n [\n -86.81383102571745,\n 46.41879658960383\n ],\n [\n -86.81383102571745,\n 46.43674452905637\n ],\n [\n -86.85011260876064,\n 46.43674452905637\n ],\n [\n -86.85011260876064,\n 46.41879658960383\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kaye, Cheryl","contributorId":167292,"corporation":false,"usgs":false,"family":"Kaye","given":"Cheryl","affiliations":[{"id":6599,"text":"U.S. Fish and Wildlife Service, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":905523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernardy, Jeffry A 0000-0001-7443-1995","orcid":"https://orcid.org/0000-0001-7443-1995","contributorId":296763,"corporation":false,"usgs":false,"family":"Bernardy","given":"Jeffry","email":"","middleInitial":"A","affiliations":[{"id":64165,"text":"former USGS, UMESC employee (retired)","active":true,"usgs":false}],"preferred":false,"id":905524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henson, Mary P.","contributorId":339882,"corporation":false,"usgs":false,"family":"Henson","given":"Mary P.","affiliations":[{"id":81410,"text":"USFWS, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":905527,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andresen, Chad K.","contributorId":335684,"corporation":false,"usgs":false,"family":"Andresen","given":"Chad","email":"","middleInitial":"K.","affiliations":[{"id":80467,"text":"Marquette Biological Station, US Fish and Wildlife Service, Marquette, Michigan, USA","active":true,"usgs":false}],"preferred":false,"id":905528,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kirkeeng, Courtney A. 0000-0002-7141-1216","orcid":"https://orcid.org/0000-0002-7141-1216","contributorId":237026,"corporation":false,"usgs":true,"family":"Kirkeeng","given":"Courtney","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905529,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237644,"text":"70237644 - 2022 - Using structured decision making to evaluate potential management responses to detection of dreissenid mussel (Dreissena spp.) environmental DNA","interactions":[],"lastModifiedDate":"2022-10-18T13:57:05.161824","indexId":"70237644","displayToPublicDate":"2022-06-01T08:44:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Using structured decision making to evaluate potential management responses to detection of dreissenid mussel (Dreissena spp.) environmental DNA","title":"Using structured decision making to evaluate potential management responses to detection of dreissenid mussel (Dreissena spp.) environmental DNA","docAbstract":"Environmental (e)DNA tools are sensitive and cost-effective for early detection of invasive species. However, the uncertainty associated with the interpretation of positive eDNA detections makes it challenging to determine appropriate natural resource management responses. Multiple sources of error can give rise to positive detections of eDNA in a sample when individuals of that species are not present at the site or a widespread infestation is not imminent. Acting on an erroneous eDNA inference could result in needless costs or reductions in desirable resources. Alternatively, failure to rapidly act on eDNA results that truly indicate invader presence could compound negative impacts and lead to high, long-term costs to manage infestations. We used a structured decision making (SDM) process, which incorporates tradeoffs and uncertainties, to evaluate appropriate response actions following hypothetical eDNA detections of invasive dreissenid mussel (Dreissena spp.) eDNA in Jordanelle Reservoir, Utah (USA). We worked with decision-makers and stakeholders to identify objectives and discrete management action alternatives to assess consequences and tradeoffs. Alternatives ranged from no action to intensive and expensive control efforts. The best performing alternative was delayed containment described by immediate attempts to confirm the eDNA detections using non-molecular sampling techniques followed by mandatory watercraft exit inspections to prevent dreissenid mussel spread to regional water bodies. Non-molecular sampling increased public support for management by demonstrating a commitment to monitor the invasion state before action, whereas containment decreased likelihood of regional spread to other waters. Delayed containment had the lowest downside risk, and the highest upside gains relative to other alternative actions. Sensitivity analyses showed our results to be robust to parameter and outcome uncertainty.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2022.13.2.06","usgsCitation":"Sepulveda, A., Smith, D.R., O'Donnell, K., Owens, N., White, B., Richter, C.A., Merkes, C.M., Wolf, S., Rau, M., Neilson, M., Daniel, W., Dumoulin, C.E., and Hunter, M., 2022, Using structured decision making to evaluate potential management responses to detection of dreissenid mussel (Dreissena spp.) environmental DNA: Management of Biological Invasions, v. 13, no. 2, p. 344-368, https://doi.org/10.3391/mbi.2022.13.2.06.","productDescription":"25 p.","startPage":"344","endPage":"368","ipdsId":"IP-133646","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447583,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2022.13.2.06","text":"Publisher Index Page"},{"id":435831,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y2IQTS","text":"USGS data release","linkHelpText":"Predicted consequences of detecting dreissenid mussel eDNA in Jordanelle Reservoir Utah, 2021"},{"id":408476,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":854796,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Donnell, Katherine M. 0000-0001-9023-174X","orcid":"https://orcid.org/0000-0001-9023-174X","contributorId":289575,"corporation":false,"usgs":false,"family":"O'Donnell","given":"Katherine M.","affiliations":[{"id":62192,"text":"Compass Resource Management","active":true,"usgs":false}],"preferred":false,"id":854798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Owens, Nathan","contributorId":297990,"corporation":false,"usgs":false,"family":"Owens","given":"Nathan","email":"","affiliations":[],"preferred":false,"id":854799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"White, Brittany","contributorId":297992,"corporation":false,"usgs":false,"family":"White","given":"Brittany","email":"","affiliations":[],"preferred":false,"id":854803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":854801,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":854944,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolf, Skylar","contributorId":279472,"corporation":false,"usgs":false,"family":"Wolf","given":"Skylar","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":854802,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rau, Mike","contributorId":297993,"corporation":false,"usgs":false,"family":"Rau","given":"Mike","email":"","affiliations":[],"preferred":false,"id":854804,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Neilson, Matthew 0000-0002-5139-5677","orcid":"https://orcid.org/0000-0002-5139-5677","contributorId":219310,"corporation":false,"usgs":true,"family":"Neilson","given":"Matthew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854806,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854805,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dumoulin, Christine E. 0000-0001-7587-9417","orcid":"https://orcid.org/0000-0001-7587-9417","contributorId":298038,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":854943,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214742,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854800,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70249673,"text":"70249673 - 2022 - Inversion of induced polarization-affected towed-transient electromagnetic data in a lateritic regolith geology: A case study from western Tanzania","interactions":[],"lastModifiedDate":"2023-10-24T11:47:23.229236","indexId":"70249673","displayToPublicDate":"2022-06-01T06:37:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Inversion of induced polarization-affected towed-transient electromagnetic data in a lateritic regolith geology: A case study from western Tanzania","docAbstract":"For several decades, induced polarization (IP) effects on transient electromagnetic (TEM) responses have been observed. These effects can manifest as late-time negative transients or as rapidly decaying curves and are usually associated with highly polarizable bodies. If neglected, IP effects can lead to erroneous resistivity models. Recent work allows IP effects to be incorporated into the inversion of TEM data on a more routine basis. In a recent field survey in western Tanzania, strongly IP-affected TEM signals are observed using a towed-transient electromagnetic (tTEM) system. The survey have been carried out to locate drinking water resources in a weathered regolith setting. In these settings, an inversion of tTEM data using a resistivity-only forward model (i.e., IP neglected) cannot fit the data and severely limits the value of the TEM data for hydrogeologic interpretation. To account for IP effects, we have applied a modified version of the Cole-Cole model called the maximum phase angle (MPA) model to invert IP-affected tTEM data. The MPA model incorporates four inversion model parameters: resistivity (
Round Goby Neogobius melanostomus, a nonnative fish species to North America, has been rapidly expanding through the connected waterways of the Laurentian Great Lakes. Herein, we document the eastward and southern expansion of Round Goby into the Hudson River, New York, an iconic coastal estuary that drains to Long Island Sound and the Atlantic seaboard. In summer and early fall 2021, routine fish monitoring conducted by the New York State Department of Environmental Conservation documented a population of Round Goby in the tidal portion of the Hudson River. Over the course of monitoring in 2021, personnel collected 112 Round Goby between Albany, New York, and Poughkeepsie, New York, with the southernmost collection occurring approximately 140 km downstream of invasion front as reported in 2020 within the New York State Canal System. Although Round Goby previously colonized large rivers and streams in the Great Lakes watershed, there is little information about the invasion success and ecological impacts of Round Goby in estuarine environments in North America. We discuss the distribution and biological characteristics of the Hudson River Round Goby population as well as the potential ecological implications and areas of future research and monitoring for this range expansion.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-22-012","usgsCitation":"Pendleton, R., Berdan, R., George, S.D., Kenney, G., and Sethi, S., 2022, Round Goby captured in a North American estuary: Status and implications in the Hudson River, New York: Journal of Fish and Wildlife Management, v. 13, no. 2, p. 524-533, https://doi.org/10.3996/jfwm-22-012.","productDescription":"10 p.","startPage":"524","endPage":"533","ipdsId":"IP-138487","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-22-012","text":"Publisher Index Page"},{"id":480950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Hudson 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York\",\"nation\":\"USA \"}}]}","volume":"13","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Pendleton, Richard","contributorId":348720,"corporation":false,"usgs":false,"family":"Pendleton","given":"Richard","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":923718,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berdan, Russell","contributorId":348721,"corporation":false,"usgs":false,"family":"Berdan","given":"Russell","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":923719,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":923720,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenney, Gregg","contributorId":348722,"corporation":false,"usgs":false,"family":"Kenney","given":"Gregg","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":923721,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923722,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237968,"text":"70237968 - 2022 - River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin","interactions":[],"lastModifiedDate":"2022-11-02T11:49:35.521099","indexId":"70237968","displayToPublicDate":"2022-05-31T06:47:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12802,"text":"ESSOAr","active":true,"publicationSubtype":{"id":10}},"title":"River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin","docAbstract":"Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.
","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511255.1","usgsCitation":"Choi, J., Quion, K.M., Reed, A., and Harvey, J., 2022, River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin: ESSOAr, 22 p., https://doi.org/10.1002/essoar.10511255.1.","productDescription":"22 p.","ipdsId":"IP-139945","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435833,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TEBOUR","text":"USGS data release","linkHelpText":"RiverMET: Workflow and scripts for river metabolism estimation including Illinois River Basin application, 2005 - 2020"},{"id":409056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Illinois River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.901423683579,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 42.70071815175049\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quion, Katherine Michelle Bernabe 0000-0003-2388-7508","orcid":"https://orcid.org/0000-0003-2388-7508","contributorId":298787,"corporation":false,"usgs":true,"family":"Quion","given":"Katherine","email":"","middleInitial":"Michelle Bernabe","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Ariel 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":298788,"corporation":false,"usgs":false,"family":"Reed","given":"Ariel","affiliations":[],"preferred":false,"id":856405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":856406,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231871,"text":"70231871 - 2022 - Variability in marsh migration potential determined by topographic rather than anthropogenic constraints in the Chesapeake Bay region","interactions":[],"lastModifiedDate":"2022-08-02T14:22:39.473636","indexId":"70231871","displayToPublicDate":"2022-05-31T06:32:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Variability in marsh migration potential determined by topographic rather than anthropogenic constraints in the Chesapeake Bay region","docAbstract":"Sea level rise (SLR) and saltwater intrusion are driving inland shifts in coastal ecosystems. Here, we make high-resolution (1 m) predictions of land conversion under future SLR scenarios in 81 watersheds surrounding Chesapeake Bay, United States, a hotspot for accelerated SLR and saltwater intrusion. We find that 1050–3748 km2 of marsh could be created by 2100, largely at the expense of forested wetlands. Predicted marsh migration exceeds total current tidal marsh area and is ~ 4× greater than historical observations. Anthropogenic land use in marsh migration areas is concentrated within a few watersheds and minimally impacts calculated metrics of marsh resilience. Despite regional marsh area maintenance, local ecosystem service replacement within vulnerable watersheds remains uncertain. However, our work suggests that topography rather than land use drives spatial variability in wetland vulnerability regionally, and that rural land conversion is needed to compensate for extensive areal losses on heavily developed coasts globally.
Water resources are limited in arid locations such as Tucson Basin. Residential development in the Tucson Mountains to the west of Tucson, Arizona, is limited by groundwater resources. Groundwater samples were collected from fractured bedrock and alluvial aquifers surrounding the Tucson Mountains to assess water quality and recharge history through measurement of stable O, H, and S isotopes; tritium; and 14C. Most groundwater is a mixture of different ages but is commonly several thousand years old. A few sampling locations indicated a component of water recharged after the above-ground nuclear testing of the mid 1950s, and these sites may represent locations near where the aquifer receives present-day recharge. The Tucson Mountains also host sulfide deposits associated with fractures and replacement zones; these locally contribute to poor-quality groundwater. Projections of future climate predict intensifying drought in southwestern North America. In the study area, a combination of strategies such as rainwater harvesting, exploitation of renewable water, and low groundwater use could be used for sustainable use of the groundwater supply.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1936-704X.2021.3369.x","usgsCitation":"Eastoe, C.J., and Beisner, K.R., 2022, Understanding the water resources of a mountain-block aquifer: Tucson Mountains, Arizona: Journal of Contemporary Water Research & Education, v. 175, no. 1, https://doi.org/10.1111/j.1936-704X.2021.3369.x.","productDescription":"14 p.","startPage":"1-14","ipdsId":"IP-130604","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":447613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1936-704x.2021.3369.x","text":"Publisher Index Page"},{"id":401364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Tucson Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.27296447753905,\n 32.155268542097815\n ],\n [\n -110.99761962890625,\n 32.155268542097815\n ],\n [\n -110.99761962890625,\n 32.377062004744786\n ],\n [\n -111.27296447753905,\n 32.377062004744786\n ],\n [\n -111.27296447753905,\n 32.155268542097815\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Eastoe, Christopher J.","contributorId":173510,"corporation":false,"usgs":false,"family":"Eastoe","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":843936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843937,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231823,"text":"70231823 - 2022 - Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","interactions":[],"lastModifiedDate":"2022-07-08T13:36:32.552842","indexId":"70231823","displayToPublicDate":"2022-05-30T15:24:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","title":"Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","docAbstract":"Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted tidal exchange in vast areas of coastal wetlands. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls and scaling of carbon exchange in these understudied ecosystems is critical for informing climate consequences of blue carbon restoration and/or management interventions. Here, we (1) examine how carbon fluxes vary across a salinity gradient (4–25 psu) in impounded and natural, tidally unrestricted Phragmites wetlands using static chambers and (2) probe drivers of carbon fluxes within an impounded coastal wetland using eddy covariance at the Herring River in Wellfleet, MA, United States. Freshening across the salinity gradient led to a 50-fold increase in CH4 emissions, but effects on carbon dioxide (CO2) were less pronounced with uptake generally enhanced in the fresher, impounded sites. The impounded wetland experienced little variation in water-table depth or salinity during the growing season and was a strong CO2 sink of −352 g CO2-C m−2 year−1 offset by CH4 emission of 11.4 g CH4-C m−2 year−1. Growing season CH4 flux was driven primarily by temperature. Methane flux exhibited a diurnal cycle with a night-time minimum that was not reflected in opaque chamber measurements. Therefore, we suggest accounting for the diurnal cycle of CH4 in Phragmites, for example by applying a scaling factor developed here of ~0.6 to mid-day chamber measurements. Taken together, these results suggest that although freshened, impounded wetlands can be strong carbon sinks, enhanced CH4 emission with freshening reduces net radiative balance. Restoration of tidal flow to impounded ecosystems could limit CH4 production and enhance their climate regulating benefits.
Climate change and anthropogenic activities are altering the body sizes of fishes, yet our understanding of factors influencing body size for many taxa remains incomplete. We evaluated the relationships between climate, environmental, and landscape attributes and the body size of different taxa of freshwater trout (Salmonidae) in the USA. Hierarchical spatial modeling across a gradient of habitats (5221 sites) illustrated the importance of watershed effects, which explained 17%–45% of the of the variation in body size across taxa. Stream size had a strong, positive relationship with body size, yet there was approximately tenfold difference in the strength of the relationship across taxa. Trout body size consistently declined with increasing density across taxa. Despite reliance on cold water, we found positive relationships between summer stream temperature and trout body size across most taxa. Our results highlight how providing trout access to larger, productive rivers for the expression of growth and life-history variation would promote body size diversity within and across populations.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2021-0343","usgsCitation":"Al-Chokhachy, R.K., Letcher, B., Muhlfeld, C.C., Dunham, J., Cline, T.J., Hitt, N.P., Roberts, J., and Schmetterling, D., 2022, Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions: Canadian Journal of Fisheries and Aquatic Sciences, v. 79, no. 10, p. 1729-1744, https://doi.org/10.1139/cjfas-2021-0343.","productDescription":"16 p.","startPage":"1729","endPage":"1744","ipdsId":"IP-131094","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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USA","interactions":[],"lastModifiedDate":"2022-06-01T12:19:18.602617","indexId":"70231868","displayToPublicDate":"2022-05-29T07:16:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Geologic controls on groundwater salinity reversal in North Coles Levee Oil Field, southern San Joaquin Valley, California, USA","docAbstract":"This paper documents a reversal in the groundwater salinity depth gradient in the North Coles Levee Oil Field in the San Joaquin Valley, California. Salinity, measured in mg/L, was mapped with water quality data from groundwater and oil and gas wells and salinity estimated from oil and gas well borehole geophysical logs using Archie's equation. The resulting three-dimensional salinity volume shows groundwater salinity increasing with depth through the Tulare and San Joaquin Formations to about 50,000 mg/L at 1100 m depth, then decreasing to 10,000–31,000 mg/L in the Etchegoin Formation at 1400 m depth. The high salinity zone occurs near the base of the San Joaquin Formation in sand lenses in shales that have been interpreted as representing a mudflat environment. The groundwater and produced water geochemistry show formation waters lie on the seawater dilution line, indicating the salinity structure is largely the result of dilution or evaporation of seawater and not due to water–rock interactions. Instead, changing depositional environments linked to decreasing sea level may be responsible for variably saline water at or near the time of deposition, leading to a salinity reversal preserved in connate waters. The steepness of the salinity reversal varies laterally, possibly due to post-depositional freshwater recharge allowed by thick sands, alternatively, by a change in connate water composition due to a lateral facies change present at the time of deposition. These results illustrate geologic and paleogeographic processes that drive the vertical salinity structure of groundwater in shallow alluvial basins.
We use the Multiple Element Limitation (MEL) model to examine responses of twelve ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water-use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.
This report describes the findings of a U.S. Geological Survey study, completed in cooperation with the Bureau of Land Management, focused on better understanding the present-day (1975–2019) hydrogeology and groundwater quality of the San Agustin Basin in west-central New Mexico to support sustainable groundwater resource management. The basin hosts a relatively undeveloped basin-fill and alluvium aquifer system and is topographically divided into east and west subbasins by the McClure Hills. Groundwater chemistry and groundwater elevation data were compiled, collected, and interpreted in the context of groundwater flow and quality. The analyses presented in this report consider groundwater chemistry data collected within the last decade (2010–19) and groundwater elevation data collected from 1975 through 2019 to provide insight into present-day conditions. Groundwater elevations show that groundwater typically moves from the highlands to the lowlands, with a prominent east to west regional trend. Groundwater elevations were lowest in the southwestern portion of the west subbasin, where estimated flow directions suggest underflow through the local highlands into the northern East Fork Gila River watershed, which is further supported by historical groundwater elevation data from the northern East Fork Gila River watershed. Gradual groundwater elevation gradients (about 2 feet per mile) near the east and west subbasin divide suggest that groundwater slowly flows from the east subbasin to the west subbasin.
Quantitative analyses of groundwater chemistry data show that groundwater in both subbasins has similar chemical characteristics. A systematic east to west groundwater evolution in water chemistry was not observed despite evidenced subbasin connectivity. The absence of this pattern suggests that groundwater mixing is regionally prevalent, sediment reactivity is low and variable, and (or) recharge conditions are comparable in both subbasins. Groundwater chemistry was generally independent of aquifer type, suggesting that the aquifers are hydrologically well connected. Corrected carbon-14 groundwater age estimates in the basin ranged from 232 to 13,916 years before present with a median of 5,409 years. A wide range of groundwater ages is therefore present in the basin, with waters commonly being thousands of years old, thereby supporting generally slow regional groundwater movement. A component of relatively young groundwater, for which estimated ages could not be accurately computed, is also present in the basin, and it may commonly mix with older waters. The spatial distribution of categorical and quantitative groundwater ages indicates that most recharge likely occurs in the highlands through mountain-block recharge and as focused recharge within arroyos, although evidence of modern (1953 and after) groundwater was minimal at sampled sites.
Median annual gradients (groundwater elevation change over time) indicate that most groundwater elevations in the lowlands changed little (−0.2 to 0.2 foot per year) from 1975 through 2019. Groundwater elevations in the highlands varied more annually, which is likely due to recharge from precipitation events. These more variable groundwater elevations in the highlands compared with the lowlands, along with groundwater ages, provide further evidence that most groundwater recharge takes place in the highlands, with minimal recharge in the lowlands. Median groundwater elevation change for all sites was −0.05 foot per year. Temporal consistency of lowland groundwater elevations suggests that regional groundwater dynamics have been more or less stable through time under current climate and development conditions, although median annual gradients indicate that groundwater elevations may have slightly declined on average between 1975 and 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225029","collaboration":"Prepared in cooperation with Bureau of Land Management and in collaboration with New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Pepin, J.D., Travis, R.E., Blake, J.M., Rinehart, A., and Koning, D., 2022, Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019: U.S. Geological Survey Scientific Investigations Report 2022–5029, 61 p., 4 app., https://doi.org/10.3133/sir20225029.","productDescription":"Report: x, 61 p.; 6 Tables; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120066","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":401145,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401143,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.csv","text":"Table 3.1","size":"29.5 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 3.1"},{"id":401142,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.xlsx","text":"Table 3.1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 3.1"},{"id":401141,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.csv","text":"Table 2.1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 2.1"},{"id":401138,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.xlsx","text":"Table 1.1","size":"116 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 1.1"},{"id":401137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5029/images"},{"id":401140,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.xlsx","text":"Table 2.1","size":"27.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 2.1"},{"id":401136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.XML"},{"id":401139,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.csv","text":"Table 1.1","size":"146 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 1.1"},{"id":401135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.pdf","text":"Report","size":"8.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5029"},{"id":401134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5029/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Agustin Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 34.5\n ],\n [\n -107.333,\n 34.5\n ],\n [\n -107.333,\n 33.333\n ],\n [\n -108.666,\n 33.333\n ],\n [\n -108.666,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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Groundwater discharge is vitally important for maintaining or restoring valuable ecosystems in surface water and at the underlying groundwater-surface-water ecotone. Detecting and quantifying groundwater discharge is challenging because rates of flow can be very small and difficult to measure, exchange is commonly highly heterogeneous both in space and time, and surface-water hydrodynamics can influence the exchange and hinder measurements. Fortunately, a growing number of methods developed during the last several decades has led to advancements in our capabilities to identify and quantify groundwater discharge to surface water, including better use of seepage meters, application of tracers such as heat or isotopes, and improved groundwater-modeling capabilities. This progress has led to coalescence in characterizing the complex mix of hydrological, biological, and chemical processes that occur at the groundwater-surface water interface, along with relevant societal effects. Still, many uncertainties and assumptions show an incomplete knowledge of these processes, including the lack of studies in many regions of the world, insufficient sharing of practical methodologies between scientific disciplines, incomplete understanding of processes and parameters specific to the sediment-water interface, and challenges associated with measuring exchange at multiple scales of time and space.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/w14111698","usgsCitation":"Duque, C., and Rosenberry, D., 2022, Advances in the study and understanding of groundwater discharge to surface water: Water, v. 14, no. 11, 1698, 5 p., https://doi.org/10.3390/w14111698.","productDescription":"1698, 5 p.","ipdsId":"IP-141343","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14111698","text":"Publisher Index Page"},{"id":401295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Duque, Carlos 0000-0001-5833-8483","orcid":"https://orcid.org/0000-0001-5833-8483","contributorId":245349,"corporation":false,"usgs":false,"family":"Duque","given":"Carlos","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":843722,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":257638,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":843723,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231804,"text":"ofr20221048 - 2022 - January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i","interactions":[],"lastModifiedDate":"2026-03-27T20:21:24.379492","indexId":"ofr20221048","displayToPublicDate":"2022-05-26T12:53:32","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":"2022-1048","displayTitle":"January 18, 2022, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i","title":"January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i","docAbstract":"On January 18, 2022, groundwater levels were measured in selected wells in the Hālawa area, O‘ahu, Hawai‘i, constituting a synoptic groundwater-level survey (shortened herein to “synoptic survey”) of the area. Groundwater levels were measured mainly from 9:00 a.m. to 12:00 p.m. (times listed in Hawai‘i standard time) and provide a snapshot of groundwater levels during the survey period. Following a reported fuel release that affected groundwater quality in the Red Hill area, several production wells were shut down in the weeks prior to the synoptic survey. These wells include the Red Hill Shaft (shut down on November 28, 2021) and the Hālawa Shaft (shut down on December 3, 2021, except for weekly, short-duration operations for water-quality sampling). Groundwater levels measured in wells during the synoptic survey ranged from 16.81 to 20.19 feet above mean sea level. The groundwater levels measured on January 18, 2022, were about 0.3 to 0.6 feet higher than those measured at common sites during a synoptic groundwater-level survey on December 23, 2021.
The groundwater levels collected during the multiagency synoptic survey contain uncertainty because of several potential sources of error associated with (1) the accuracy of the measuring tapes used, (2) the accuracy of the measuring-point altitude at the top of each well, (3) well plumbness and alignment, (4) human error, and (5) changing conditions during the survey period. Because of these potential sources of error, comparability of groundwater-level measurements may be affected. Some of the sources of uncertainty can be addressed and lead to improved accuracy and comparability of the groundwater levels. For example, uncertainty associated with the measuring-point altitudes can be addressed by resurveying measuring-point altitudes to a common vertical datum using consistent surveying methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221048","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1048, 11 p., https://doi.org/10.3133/ofr20221048.","productDescription":"Report: v, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-138445","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":401209,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1048/covrthb.jpg"},{"id":401210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1048/ofr20221048.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":401211,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the nation","description":"U.S. Geological Survey, 2022, USGS water data for the nation: U.S. Geological Survey National Water Information System database, https://doi.org/10.5066/F7P55KJN."},{"id":401230,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221018","text":"Open-File Report 2022–1018","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., https://doi.org/10.3133/ofr20221018..","linkHelpText":"- December 23, 2021, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404437,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221069","text":"Open-File Report 2022-1069","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, Groundwater-level monitoring from January 17 to March 3, 2022, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1069, 29 p., https://doi.org/10.3133/ofr20221069.","linkHelpText":"- Groundwater-Level Monitoring from January 17 to March 3, 2022, Hālawa Area, O‘ahu, Hawai‘i"},{"id":501778,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113078.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Hālawa Area, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.97103881835938,\n 21.317522325157526\n ],\n [\n -157.84194946289062,\n 21.317522325157526\n ],\n [\n -157.84194946289062,\n 21.410883719938866\n ],\n [\n -157.97103881835938,\n 21.410883719938866\n ],\n [\n -157.97103881835938,\n 21.317522325157526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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1845 Wasp Blvd., B176
Honolulu, HI 96818
Groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit (SacMetro-DSA) was studied from August to November 2017 as part of the second phase of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in parts of Amador, Placer, Sacramento, and Sutter Counties, and the extent of the study unit was defined by the location of three California Department of Water Resources groundwater subbasins: the North American, the South American, and the Cosumnes. The SacMetro-DSA focused on groundwater resources used for domestic drinking-water supply, which generally correspond to shallower parts of aquifer systems than those of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included two components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources. The first component of this study—the status assessment—was based on water-quality data collected from 49 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2017. The samples were analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that had concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, groundwater age, and geochemical and hydrologic conditions of the domestic-supply aquifer and related these data to constituents identified in the status assessment for further evaluation.
In the SacMetro-DSA study unit, arsenic was the only inorganic constituent detected above health-based benchmarks and was detected in 10 percent of the domestic-supply aquifer system. Inorganic constituents were detected above the non-health-based California State Water Resources Control Board—Division of Drinking Water secondary maximum contaminant levels (SMCL-CA) in 16 percent of the system. The inorganic constituents detected above the SMCL-CA were chloride, iron, manganese, and total dissolved solids (TDS). Organic constituents (volatile organic compounds and pesticides) with health-based benchmarks were not detected above health-based benchmarks; however, chloroform was detected at concentrations higher than 10 percent of the health-based benchmark (80 micrograms per liter) in 2 percent of the domestic-supply aquifer system. Of the 310 organic constituents analyzed, 16 constituents were detected; however, only bentazon and chloroform had detection frequencies greater than 10 percent.
Inorganic constituents with health-based benchmarks that were evaluated in the understanding assessment included arsenic and hexavalent chromium. Arsenic and hexavalent chromium are natural constituents of aquifer sediments in the study unit and did not appear to be influenced by anthropogenic processes; rather, the presence of arsenic and hexavalent chromium appeared to be related to geochemical conditions controlled by oxidation–reduction reactions in the aquifer system. Naturally occurring inorganic constituents with SMCL-CAs evaluated in the understanding assessment were the trace elements iron and manganese, the major ion chloride, and TDS. Like arsenic and hexavalent chromium, the presence of iron and manganese was most strongly related to geochemical conditions in the aquifer system, specifically reducing conditions, which were most common near the western edge of the study unit close to the Sacramento River. Concentrations of chloride and TDS are indicators of salinity and were correlated with variables related to well location and included redox, agricultural land use, and elevation. Chloride and TDS were positively correlated to reducing conditions, and agricultural land use was negatively correlated to elevation and well depth. Observed correlations among variables were likely driven by the characteristics of the western part of the study unit, such as its higher proportion of agricultural land use and its relatively low elevation. A large portion of the western edge of the study unit is located in the center of the Sacramento Valley, defined by the location of the Sacramento River. The special-interest constituent perchlorate, also included in the understanding assessment, has natural and anthropogenic sources. Perchlorate was detected frequently and at moderate relative concentrations. In some areas of the study unit, concentrations of perchlorate were higher than what might be expected in nature; therefore, anthropogenic introduction of perchlorate or anthropogenically induced migration of native perchlorate could be occurring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225021","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Bennett, G.L., V, 2022, Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2022–5021, 52 p., https://doi.org/10.3133/sir20225021.","productDescription":"Report: xi, 52 p.; Data Release","numberOfPages":"52","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-125530","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401191,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2022–5021"},{"id":401167,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H4P0XF","text":"Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","description":"Bennett, G.L., V, 2022, Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey data release, available at https://doi.org/10.5066/P9H4P0XF."},{"id":401163,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5021/covrthb.jpg"},{"id":401166,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5021/images"},{"id":401165,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.xml"},{"id":401164,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2022–5021"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Metropolitan Domestic-Supply Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 37.87485339352928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
Anthropogenic barriers to main-stem and tributary passage are one of the primary threats associated with declining populations of Pacific Lamprey (Entosphenus tridentatus) in the Columbia River Basin. Juvenile lamprey are of special interest because their downstream migration to the ocean may be affected by barriers such as dams or water diversions. Telemetry studies that describe the movement and passage of juvenile lamprey have not been possible until the recent development of a micro-transmitter specifically for use in juvenile lamprey and eels. Through a collaborative research approach, we used these prototype transmitters and acoustic monitoring arrays installed for a juvenile salmon (Oncorhynchus spp.) migration study to evaluate juvenile lamprey movements in the Yakima River (river kilometer 179 to the river mouth) in 2019 and 2020. We tagged and released 152 juvenile lamprey from April 30 to June 5, 2019, and on June 9, 2020. Lamprey were released 6.9 kilometers (km) upstream from Wapato Dam, 1.2 km upstream from Prosser Dam, and into the canal and tailrace at Prosser Dam. Most tagged lamprey did not initiate downstream movements within the 18 days of tag life, as evidenced by our detections of lamprey in the highest numbers at the first monitoring site downstream from their release site, with limited or no detections at sites farther downstream. There was no evidence of missed detections (lamprey detected at a downstream site without corresponding detections upstream). Overall detections of tagged lamprey were low: 27.0 percent in 2019 and 48.0 percent in 2020. River flows were less than the 10-year average during the monitoring period and water temperatures were variable. Lamprey arrived at detections sites predominantly during periods of darkness (85.3–96.6 percent) following daytime releases. Travel rates through the study area ranged from 0.2 to 45.3 kilometers per day, and lamprey generally remained at each detection station for less than about 20 minutes. Groups of lamprey released together generally had similar travel rates with a small number of fish that moved more quickly or slowly than the remainder of the group. In addition to monitoring the migration and behavior of juvenile lamprey, we also assessed some assumptions of survival models (determining downstream drift of purposely killed fish and empirically measuring transmitter operating life) to benefit future evaluations focused on migration survival of juvenile lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221052","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Yakama Nation Fisheries, McNary Fisheries Compensation Committee, Bonneville Power Administration, and the Pacific Northwest National Laboratory","usgsCitation":"Liedtke, T.L., Lampman, R.T., Monk, P., Hansen, A.C., Kock, T.J., Beals, T.E., Deng, D.Z., and Porter, M.S., 2022, Monitoring the movements of juvenile Pacific Lamprey (Entosphenus tridentatus) in the Yakima River, Washington, using acoustic telemetry, 2019–20: U.S. Geological Survey Open-File Report 2022–1052, 28 p., https://doi.org/10.3133/ofr20221052.","productDescription":"Report: viii, 28 p.; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-133893","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401158,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1052/images"},{"id":401157,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://app.streamnet.org/files/822/","text":"Pacific States Marine Fisheries Commission, StreamNet—Fish Data for the Northwest data files"},{"id":401156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1052/ofr20221052.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1052"},{"id":401155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1052/covrthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.684814453125,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.01985337287631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The U.S. Geological Survey (USGS), in cooperation with the Caddo Nation of Oklahoma and Bureau of Indian Affairs, assessed four groundwater-withdrawal scenarios and their potential effects on the Rush Springs aquifer in and near the Caddo Nation of Oklahoma Tribal jurisdictional area in western Oklahoma. Increases in industrial and public water supply needs have led to increased development of water resources within the Rush Springs aquifer. As new areas within the aquifer are developed, increased water withdrawals may result in decreases in available groundwater resources and conflicts among water users.
For this study, a previously published numerical groundwater-flow model of the Rush Springs aquifer was modified to simulate the potential effects of four groundwater withdrawal scenarios. For the previously published calibrated model, groundwater flow was simulated from 1979 through 2015. In this study, groundwater flow simulations were extended through 2035. The period from 2016 through 2035 is referred to as the “20-year projection.” Four groundwater withdrawal scenarios starting in 2007 and continuing through 2035 were evaluated. Scenario 1 simulated no groundwater withdrawals; scenario 2 simulated no withdrawals allocated for out-of-basin water-use transfers; scenario 3 simulated withdrawals based on reported withdrawals during the 2007–15 simulation period and compounded annual increases in groundwater use during the subsequent 20-year projection; and scenario 4 simulated maximum permitted withdrawals for allocation to out-of-basin water-use transfers. Out-of-basin water transfers were classified as withdrawals that are not returned back to the aquifer.
At the springs of interest, changes in water-level altitudes in response to different groundwater withdrawal scenarios were simulated by comparing the results from different model cells. Between 2007 and 2015, scenarios 2–4 yielded similar simulated water-level altitudes in the model cells containing springs of interest, with water-level altitudes decreasing to below the land surface altitude at 13 of the total 25 springs of interest, whereas under scenario 1 there were only two model cells containing springs of interest where the simulated water-level altitudes of a spring decreased to below land surface altitude. For the 20-year projection, water-level altitudes at springs simulated in model cells in scenarios 2–4 decreased to below land surface altitude for 13 of the total 25 model cells containing springs of interest, whereas under scenario 1 there were only two model cells containing springs of interest where the simulated water-level altitudes of a spring decreased to below land surface altitude.
The potential effects of groundwater withdrawals were evaluated by comparing changes in groundwater storage between the four scenarios. The 2007–15 groundwater withdrawal scenarios were used to simulate the potential effects of groundwater withdrawal rates on groundwater storage of the Rush Springs aquifer. The simulated groundwater storage change in the Rush Springs aquifer ranged from an increase of 2.8 percent for scenario 1 to an increase of 1.0 percent for scenario 4. Projected 20-year groundwater withdrawal scenarios were used to simulate the potential effects of selected groundwater withdrawal rates on groundwater storage of the Rush Springs aquifer. Simulated groundwater storage changes ranged from a decrease of 0.5 percent for scenario 1 to a decrease of 0.7 percent for scenario 4.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225044","collaboration":"Prepared in cooperation with the Caddo Nation of Oklahoma and Bureau of Indian Affairs","usgsCitation":"Labriola, L.G., Russell, C.A., and Ellis, J.H., 2022, Potential effects of out-of-basin groundwater transfers on spring discharge, base flow, and groundwater storage pertaining to the Rush Springs aquifer in and near the Caddo Nation of Oklahoma Tribal jurisdictional area, western Oklahoma: U.S. Geological Survey Scientific Investigations Report 2022–5044, 32 p., https://doi.org/10.3133/sir20225044.","productDescription":"Report: vii, 32 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-128617","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":401055,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225044/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400915,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":400910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5044/coverthb.jpg"},{"id":400911,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5044/sir20225044.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5044"},{"id":400912,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5044/sir20225044.XML"},{"id":400914,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92DYE98","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate the potential effects of out-of-basin transfers for the Rush Springs aquifer in the Caddo Nation of Oklahoma Tribal jurisdictional area, western Oklahoma"},{"id":400913,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5044/images"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Rush Springs Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.767578125,\n 34.45221847282654\n ],\n [\n -98.5693359375,\n 36.491973470593685\n ],\n [\n -99.66796875,\n 35.817813158696616\n ],\n [\n -99.0966796875,\n 35.137879119634185\n ],\n [\n -98.61328125,\n 34.488447837809304\n ],\n [\n -97.6904296875,\n 34.34343606848294\n ],\n [\n -96.767578125,\n 34.45221847282654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501