Steady-state numerical groundwater-flow models were constructed for the islands of Kaua‘i, O‘ahu, and Maui to enable quantification of the hydrologic consequences of withdrawals and other stresses that can place limits on groundwater availability. The volcanic aquifers of Hawai‘i supply nearly all drinking water for the islands’ residents, freshwater for diverse industries, and natural discharge to springs, streams, and nearshore areas that support ecosystems, cultural practices, aesthetics, and recreation. Increases in groundwater withdrawal and changes in climate can cause water-table depression, saltwater rise, and reduction of natural groundwater discharge—all of which can limit fresh groundwater availability. The numerical models described in this report are designed to quantify these consequences. Separate models were created for each island using MODFLOW-2005 with the Seawater Intrusion package, which allows simulation of freshwater and saltwater in ocean-island aquifers. Calibration resulted in models that generally replicate observed water-level, stream base-flow, and spring-flow data, and simulate groundwater-flow directions and fresh groundwater thicknesses that are consistent with conceptual models. The calibrated models use hydraulic properties that are consistent with the ranges reported in previous studies. The models show that the relative distribution of fresh groundwater discharge to the ocean, streams, and springs and withdrawals for human use differ substantially among the three islands studied here. These differences indicate that consequences that limit the availability of fresh groundwater for human use are likely to differ among the three islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205126","usgsCitation":"Izuka, S.K., Rotzoll, K., and Nishikawa, T., 2021, Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui: U.S. Geological Survey Scientific Investigations Report 2020-5126, 63 p., https://doi.org/10.3133/sir20205126.","productDescription":"Report: viii, 63 p.; Data Release","numberOfPages":"63","ipdsId":"IP-071367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5126/covrthb.jpg"},{"id":383612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5126/sir20205126.pdf","text":"Report","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K4DK2P","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater availability in volcanic aquifers on Kaua‘i, O‘ahu, and Maui, Hawai‘i"},{"id":416444,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20155164","text":"Scientific Investigations Report 2015-5164","description":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models"},{"id":416445,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1876","text":"Professional Paper 1876","description":"Izuka, S.K., and Rotzoll, K., 2023, Volcanic aquifers of Hawaiʻi—Contributions to assessing groundwater availability on Kauaʻi, Oʻahu, and Maui: U.S. Geological Survey Professional Paper 1876, 100 p., https://doi.org/10.3133/pp1876.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Contributions to Assessing Groundwater Availability on Kaua‘i, O‘ahu, and Maui"},{"id":417944,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233010","text":"Fact Sheet 2023-3010","description":"Izuka, S.K., and Rotzoll, K., 2023, Availability of groundwater from the volcanic aquifers of the Hawaiian Islands: U.S. Geological Survey Fact Sheet 2023-3010, 4 p., https://doi.org/10.3133/fs20233010.","linkHelpText":"- Availability of Groundwater from the Volcanic Aquifers of the Hawaiian Islands"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua'i, Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.73095703125,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 20.57365332356332\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.33221435546875,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.235622362422877\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.85931396484375,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 21.830906665069758\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
Loch Vale watershed was instrumented in 1983 with initial support from the National Acid Precipitation Assessment Program to ask whether ecosystems of Rocky Mountain National Park (RMNP) were affected by acidic atmospheric deposition. Research and monitoring activities were expanded in 1991 by the U.S. Geological Survey Water, Energy, and Biogeochemical Budgets program to understand the processes, and their interactions, controlling water, energy, and biogeochemical fluxes. With help from many collaborators we have characterized trends and patterns in atmospheric deposition, climate, and hydrology, including glaciers and other ice features. Instead of acidic deposition, we documented high atmospheric inputs of reactive nitrogen (Nr), and have studied the ecological consequences in soils, surface water, and vegetation. Using paleolimnology, we documented the onset of human-caused change to lake primary producers ca. 1950 in response to increased Nr deposition and warming. Our results provided the basis for the Colorado Nitrogen Deposition Reduction Plan, a state policy that aims to reduce Nr emissions to protect resources in RMNP by 2032. Carbon cycle research revealed mountain wetlands now release more carbon than they store, and respiration and methane flux occurs even during winter through deep snow packs. Trend analyses found export of Nr to be closely tied to atmospheric inputs, but can lag in response to drought. Current research explores consequences of the combination of warming, changes in precipitation dynamics, and atmospheric deposition of Nr and dust on stream and lake CO2 dynamics, lake biology and trophic state, and soil carbon composition. Dramatic increases in park visitors have prompted studies on the effects of recreational use on water quality. New tools such as remote sensing and high frequency instream water quality sensors are being applied to lake and stream studies. Monitoring, combined with experiments, models, and spatial comparisons is an essential foundation for science-based resource management.
Hydrologic and irrigation regimes mediate the timing of selenium (Se) mobilization to rivers, but the extent to which patterns in Se uptake and trophic transfer through recipient food webs reflect the temporal variation in Se delivery is unknown. We investigated Se mobilization, partitioning, and trophic transfer along approximately 60 river miles of the selenium-impaired segment of the Lower Gunnison River (Colorado, USA) during six sampling trips between June 2015 and October 2016. We found temporal patterns in Se partitioning and trophic transfer to be independent of those in dissolved Se concentrations and that the recipient food web sustained elevated Se concentrations from earlier periods of high Se mobilization. Using an ecosystem-scale Se accumulation model tailored to the Lower Gunnison River, we predicted that the endangered Razorback Sucker (Xyrauchen texanus) and Colorado Pikeminnow (Ptychocheilus lucius) achieve whole-body Se concentrations exceeding aquatic life protection criteria during periods of high runoff and irrigation activity (April–August) that coincide with susceptible phases of reproduction and early-life development. The results of this study challenge assumptions about Se trophodynamics in fast-flowing waters and introduce important considerations for the management of Se risks for biota in river ecosystems.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06582","usgsCitation":"Brandt, J., Roberts, J., Stricker, C.A., Rogers, H., Nease, P., and Schmidt, T., 2021, Temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river: Environmental Science and Technology, v. 55, no. 6, p. 3645-3656, https://doi.org/10.1021/acs.est.0c06582.","productDescription":"12 p.","startPage":"3645","endPage":"3656","ipdsId":"IP-122278","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":453343,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.0c06582","text":"Publisher Index Page"},{"id":436495,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TD4THX","text":"USGS data release","linkHelpText":"Dataset for temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river"},{"id":389070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River","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 38.0\n ],\n [\n -107.25,\n 38.0\n ],\n [\n -107.25,\n 39.16666667\n ],\n [\n -108.666,\n 39.16666667\n ],\n [\n -108.666,\n 38.0\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandt, Jessica E","contributorId":257351,"corporation":false,"usgs":false,"family":"Brandt","given":"Jessica E","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":823037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Holly hrogers@usgs.gov","contributorId":174358,"corporation":false,"usgs":true,"family":"Rogers","given":"Holly","email":"hrogers@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":823040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nease, Patricia","contributorId":265586,"corporation":false,"usgs":false,"family":"Nease","given":"Patricia","email":"","affiliations":[],"preferred":false,"id":823041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - 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The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.
In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose zone and saturated zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variables to determine mean regional vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 and 3.75 m yr−1, respectively). The statistical machine learning results are consistent with two primary recharge processes in this western Nebraska aquifer, namely (1) diffuse recharge from irrigation and precipitation across the landscape and (2) focused recharge from leaking irrigation conveyance canals. The vadose zone mean velocity yielded a mean recharge rate (0.46 m yr−1) consistent with previous estimates from groundwater age dating in shallow wells (0.38 m yr−1). The saturated zone mean velocity yielded a recharge rate (1.31 m yr−1) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age dating in intermediate-depth wells (1.22 m yr−1). Collectively, the statistical machine learning model results are consistent with previous observations of relatively high water fluxes and short transit times for water and nitrate in the primarily oxic aquifer. Partial dependence plots from the model indicate a sharp threshold in which high groundwater nitrate concentrations are mostly associated with total travel times of 7 years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include the non-uniqueness of different transport rate combinations when comparing model performance and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-25-811-2021","usgsCitation":"Wells, M.J., Gilmore, T., Nelson, N., Mittelstet, A., and Bohlke, J., 2021, Determination of vadose zone and saturated zone nitrate lag times using long-term groundwater monitoring data and statistical machine learning: Hydrology and Earth System Sciences, v. 25, p. 811-829, https://doi.org/10.5194/hess-25-811-2021.","productDescription":"19 p.","startPage":"811","endPage":"829","ipdsId":"IP-118404","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":453386,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-25-811-2021","text":"Publisher Index Page"},{"id":383417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","county":"Scotts Bluff County, Sioux County","otherGeospatial":"Dutch Flats","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.03228759765625,\n 41.27367811566259\n ],\n [\n -102.39257812499999,\n 41.27367811566259\n ],\n [\n -102.39257812499999,\n 42.407234661551875\n ],\n [\n -104.03228759765625,\n 42.407234661551875\n ],\n [\n -104.03228759765625,\n 41.27367811566259\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Martin J.","contributorId":251868,"corporation":false,"usgs":false,"family":"Wells","given":"Martin","email":"","middleInitial":"J.","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilmore, Troy E.","contributorId":251869,"corporation":false,"usgs":false,"family":"Gilmore","given":"Troy E.","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Natalie","contributorId":251870,"corporation":false,"usgs":false,"family":"Nelson","given":"Natalie","affiliations":[{"id":50407,"text":"North Carolina State U","active":true,"usgs":false}],"preferred":false,"id":810737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mittelstet, Aaron","contributorId":251871,"corporation":false,"usgs":false,"family":"Mittelstet","given":"Aaron","affiliations":[{"id":50406,"text":"U Nebraska","active":true,"usgs":false}],"preferred":false,"id":810738,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":810739,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224974,"text":"70224974 - 2021 - An attention U-Net model for detection of fine-scale hydrologic streamlines","interactions":[],"lastModifiedDate":"2021-10-11T12:42:39.84326","indexId":"70224974","displayToPublicDate":"2021-02-19T07:38:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"An attention U-Net model for detection of fine-scale hydrologic streamlines","docAbstract":"Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation, and monitoring environmental changes. Conventional approaches to detecting such streamlines cannot adequately incorporate information from the complex three-dimensional (3D) environment of streams and land surface features. Such information is vital to accurately delineate streamlines. In recent years, high accuracy lidar data has become increasingly available for deriving both 3D information and terrestrial surface reflectance. This study develops an attention U-net model to take advantage of high-accuracy lidar data for finely detailed streamline detection and evaluates model results against a baseline of multiple traditional machine learning methods. The evaluation shows that the attention U-net model outperforms the best baseline machine learning method by an average F1 score of 11.25% and achieves significantly better smoothness and connectivity between classified streamline channels. These findings suggest that our deep learning approach can harness high-accuracy lidar data for fine-scale hydrologic streamline detection, and in turn produce desirable benefits for many scientific domains.
Streams in semi‐arid urban and agricultural environments are often heavily diverted for anthropogenic purposes. However, they simultaneously receive substantial inflows from a variety of ungaged sources including stormwater returns, tile drainage, and irrigation runoff that help sustain flow during dry periods. Due to the inability to identify sources or directly gage many of these inflows, there is a clear need for methods to understand source origination while quantifying potential gains and losses over highly impacted reaches. In the context of the Logan River Observatory, historical gage data illustrate the importance of ungaged and unidentified inflows on maintaining or enhancing flows in both urban and agricultural reaches containing large diversions. To understand the inflows in this portion of the Logan River, we first analysed water samples for ions collected from a subset of representative inflow sources and applied clustering analyses to establish inflow source classifications and associated ion concentration ranges. These representative concentration ranges, combined with mainstem flow and river ion samples taken at sub‐reach scales, allow for the application of flow and mass balances to quantify inflow rates from different sources as well as any losses. These calculations demonstrate significant gains and losses occurring in many sub‐reaches during three sampling events. The dominant land use (urban or agriculture) and flow regime at the time of sampling were the primary drivers of gains and losses. These exchanges were found to be most important below large diversions during low flow conditions. This highlights the need to classify inflow sources (urban or agriculture, surface or groundwater) and estimate their contributions to anticipate instream consequences of land use and water management decisions. As irrigation and water conveyance practices become more efficient, a portion of these ungaged inflows could be diminished or eliminated, thus further depleting streamflow during dry periods.
Coastal margins are important areas of materials flux that link terrestrial and marine ecosystems. Consequently, climate-mediated changes to coastal terrestrial ecosystems and hydrologic regimes have high potential to influence nearshore ocean chemistry and food web dynamics. Research from tightly coupled, high-flux coastal ecosystems can advance understanding of terrestrial–marine links and climate sensitivities more generally. In the present article, we use the northeast Pacific coastal temperate rainforest as a model system to evaluate such links. We focus on key above- and belowground production and hydrological transport processes that control the land-to-ocean flow of materials and their influence on nearshore marine ecosystems. We evaluate how these connections may be altered by global climate change and we identify knowledge gaps in our understanding of the source, transport, and fate of terrestrial materials along this coastal margin. Finally, we propose five priority research themes in this region that are relevant for understanding coastal ecosystem links more broadly.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biaa171","usgsCitation":"Bidlack, A.L., Bisbing, S., Buma, B., Diefenderfer, H., Fellman, J., Floyd, W., Giesbrecht, I., Lally, A., Lertzman, K., Perakis, S.S., Butman, D., D'Amore, D., Fleming, S.W., Hood, E.W., Hunt, B.K., Kiffney, P., McNicol, G., Menounos, B., and Tank, S.E., 2021, Climate-mediated changes to linked terrestrial and marine ecosystems across the northeast Pacific coastal temperate rainforest margin: BioScience, biaa171, 15 p., https://doi.org/10.1093/biosci/biaa171.","productDescription":"biaa171, 15 p.","ipdsId":"IP-107280","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453506,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biaa171","text":"Publisher Index 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Scientists from the U.S. Geological Survey Western Ecological Research Center (WERC), Point Blue Conservation Science (Point Blue), and the U.S. Fish and Wildlife Service (USFWS) partnered to learn how wetland and flooded agricultural habitats used by waterfowl and shorebirds change during the non-breeding season (July–April) particularly during drought. During extreme drought conditions, the ability to provide sufficient water for wildlife often depends on the timing of water deliveries to managed wetlands and winter-flooded crop fields and decisions on whether to fallow croplands. Waterfowl and shorebirds could be particularly affected by these decisions because they typically rest and feed in flooded habitats. Poor habitat conditions resulting from spatially or temporally suboptimal water deliveries (that is, mismatch between waterfowl habitat needs and timing and location of flooded habitats) could reduce waterfowl hunting opportunities and body condition. Point Blue scientists developed a system for near real-time tracking of habitats used by waterfowl, shorebirds, and some other wetland-dependent “waterbirds” (www.pointblue.org/watertracker) and to assess the impacts of drought on habitat availability and on waterfowl and shorebird bioenergetics. The WERC researchers linked these data with near real-time tracking (telemetry) data of duck locations throughout the Valley. The team used these two datasets to relate duck locations to open-water characteristics and to learn how waterfowl use habitats under spatially and temporally changing conditions during drought and non-drought periods. We found that recent extreme drought (2013–15) significantly changed the timing and magnitude of flooding and consequently reduced the availability of habitats used by waterfowl and shorebirds more than other recent historic droughts 2000–11. Drought reduced irrigations of moist soil seed plants and thus there was lower food energy available for waterfowl. Analyses using bioenergetics models indicated that, overall, extreme drought increased food energy deficits (total number of deficit days) for shorebirds and waterfowl. Our analysis indicated a strong direct relationship between duck locations and classified habitat derived from open-water data during the wintering period (October–March). This result helps confirm the application of dynamic water data to identify flooded areas that provide waterfowl habitat. Presence of open water at a 1-hectare resolution can be used effectively to identify flooded landscape areas available as habitat for ducks. Our discoveries from evaluating use of space by ducks also indicated that nighttime feeding locations of ducks were concentrated nearby primary roosts and that foraging distances could depend on hydrologic dynamics of location (Suisun Marsh versus California excluding Suisun Marsh) and time of season (early, middle, late). Other results indicated that some areas on the California landscape with extremely reliable water supplies could receive consistent use by ducks year after year (in essence, almost drought proof). The Water Tracker is set up to automatically track wetland habitat and food availability each year and is making these data available to water and wetland managers. Results from this research are a significant step toward understanding how waterfowl and shorebird habitats can be optimally managed on the landscape to support desired populations of these migratory birds during extreme drought.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201102","collaboration":"Prepared in cooperation with the Southwest Climate Adaptation Science Center of the U.S. Geological Survey and the Regional Inventory and Monitoring Program of the U.S. Fish and Wildlife Service","usgsCitation":"Matchett, E.L., Reiter, M., Overton, C.T., Jongsomjit, D., and Casazza, M.L., 2021, Using high resolution satellite and telemetry data to track flooded habitats, their use by waterfowl, and evaluate effects of drought on waterfowl and shorebird bioenergetics in California: U.S. Geological Survey Open-File Report 2020–1102, 59 p., https://doi.org/10.3133/ofr20201102.","productDescription":"Report: xi, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","ipdsId":"IP-102884","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":383074,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2020/1102/images"},{"id":383073,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P922KDU6","linkHelpText":"Classification of waterfowl habitat and quantification of interannual space use and movement distance from primary roosts to night feeding locations by waterfowl in California for October–March of 2015 through 2018"},{"id":383071,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1102/covrthb.jpg"},{"id":383072,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2020/1102/ofr20201102.xml"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 36.06686213257888\n ],\n [\n -119.44335937499999,\n 35.137879119634185\n ],\n [\n -118.828125,\n 34.813803317113155\n ],\n [\n -118.30078125,\n 35.137879119634185\n ],\n [\n -118.49853515625,\n 35.71083783530009\n ],\n [\n -119.39941406249999,\n 37.33522435930639\n ],\n [\n -120.47607421874999,\n 38.16911413556086\n ],\n [\n -120.89355468749999,\n 38.58252615935333\n ],\n [\n -121.22314453124999,\n 39.11301365149975\n ],\n [\n -121.640625,\n 39.977120098439634\n ],\n [\n -121.97021484374999,\n 40.74725696280421\n ],\n [\n -122.3876953125,\n 41.0130657870063\n ],\n [\n -122.84912109375,\n 40.613952441166596\n ],\n [\n -122.87109375,\n 40.07807142745009\n ],\n [\n -122.6953125,\n 38.993572058209466\n ],\n [\n -122.08007812499999,\n 37.68382032669382\n ],\n [\n -121.37695312499999,\n 36.96744946416934\n ],\n [\n -120.234375,\n 35.99578538642032\n ],\n [\n -120.234375,\n 36.06686213257888\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
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3020 State University Drive East
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Dissolved organic carbon (DOC) impacts water quality, the carbon cycle, and the ecology of aquatic systems. Understanding what controls DOC is therefore critical for improving large-scale models and best management practices for aquatic ecosystems. The two main processes of DOC transformation and removal, photochemical and microbial DOC degradation, work in tandem to modify and remineralize DOC within natural waters. Here, we examined both the photo- and microbial remineralization of DOC (photolability and biolability), and the indirect phototransformation of DOC into biolabile DOC (photoprimed biolability) for samples that capture the spatiotemporal and hydrological gradients of the Connecticut River watershed. The majority of DOC exported from this temperate watershed was photolabile and the concentration of photolabile DOC correlated with UV absorbance at 254 nm (r2 = 0.86). Phototransformation of DOC also increased biolability, and the total photolabile DOC (sum of photolabile and photoprimed biolabile DOC) showed a stronger correlation with UV absorbance at 254 nm (r2 = 0.92). We estimate that as much as 49% (SD = 3.3%) and 10% (SD = 1.1%) of annual DOC export from the Connecticut River is directly photolabile and photoprimable, respectively. Thus, 2.82 Gg C year−1 (SD = 0.67 Gg C year−1) or 1.13 Mg C km−2 year−1 (SD = 0.27 km−2 year−1) of total photolabile DOC escapes photochemical degradation within the river network to be exported from the Connecticut River each year.
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In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.583509","usgsCitation":"Shukla, S., Landsfeld, M., Anthony, M., Budde, M., Husak, G., Rowland, J., and Funk, C., 2021, Enhancing the application of Earth observations for improved environmental decision-making using the Early Warning eXplorer (EWX): Frontiers in Climate, v. 2, 583509, 16 p., https://doi.org/10.3389/fclim.2020.583509.","productDescription":"583509, 16 p.","ipdsId":"IP-120483","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453527,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.583509","text":"Publisher Index Page"},{"id":429328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":145841,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","affiliations":[{"id":16255,"text":"Climate Hazards Group University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901558,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":901559,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anthony, Michelle 0000-0001-6646-2134","orcid":"https://orcid.org/0000-0001-6646-2134","contributorId":336955,"corporation":false,"usgs":false,"family":"Anthony","given":"Michelle","affiliations":[{"id":80923,"text":"KBR Technical Support Services Contract (TSSC)","active":true,"usgs":false}],"preferred":false,"id":901560,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901561,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Husak, Greg 0000-0003-2647-7870","orcid":"https://orcid.org/0000-0003-2647-7870","contributorId":331302,"corporation":false,"usgs":false,"family":"Husak","given":"Greg","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":901562,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901563,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":901564,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219165,"text":"70219165 - 2021 - Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales","interactions":[],"lastModifiedDate":"2021-03-29T13:00:49.94313","indexId":"70219165","displayToPublicDate":"2021-02-09T07:56:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales","docAbstract":"Understanding patterns of biodiversity is a key goal of ecology and is especially pressing in the current human‐caused biodiversity crisis. In wetland ecosystems, human impacts are centered around hydrologic manipulation including the common practice of wetland diking and impoundment. Constraining how wetland management influences plant biodiversity patterns across spatial scales will provide information on how best to modify actions to preserve biodiversity and ecosystem function in managed wetlands. Here, we compare patterns of plant diversity and species presence, abundance, and community composition at several spatial scales among tidal wetlands along an estuarine salinity gradient and managed wetlands that were formerly tidal. Managed impounded wetlands had decreased alpha and gamma diversity of rare species, with less than 60% of the species richness found in tidal brackish wetlands at several spatial scales. There was little change in the overall pattern of alpha, beta, and gamma diversity for common species in impounded wetlands; however, dominant tidal brackish species, primarily perennial rhizomatous graminoids, were replaced with management target plants and non‐native annual grasses in impounded wetlands. This species replacement led to over 60% of impounded sites being classified as containing novel plant assemblages. An additional 25% of impounded sites were classified as containing tidal saline plant assemblages, suggesting potential soil salinization. Along the estuarine gradient, patchiness and codominance of common plant species drove high diversity and turnover in tidal brackish wetlands, while it remains unclear whether tidal fresh or brackish wetlands maximize rare plant diversity. With reduced species richness, altered functional dominants, and novel or saline assemblages, impounded brackish wetlands may require careful water management to balance native plant biodiversity, associated ecosystem processes, and waterfowl requirements.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3366","usgsCitation":"Jones, S., Janousek, C.N., Casazza, M.L., Takekawa, J., and Thorne, K., 2021, Seasonal impoundment alters patterns of tidal wetland plant diversity across spatial scales: Ecosphere, v. 12, no. 2, e03366, 19 p., https://doi.org/10.1002/ecs2.3366.","productDescription":"e03366, 19 p.","ipdsId":"IP-121980","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453532,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3366","text":"Publisher Index Page"},{"id":436516,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZG1Y72","text":"USGS data release","linkHelpText":"Impounded and tidal wetland plant diversity and composition across spatial scales, San Francisco Bay-Delta, California, USA (2016-2018)"},{"id":384713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.93701171874999,\n 36.89719446989036\n ],\n [\n -121.57470703125,\n 36.89719446989036\n ],\n [\n -121.57470703125,\n 38.976492485539396\n ],\n [\n -122.93701171874999,\n 38.976492485539396\n ],\n [\n -122.93701171874999,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Scott 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":215602,"corporation":false,"usgs":true,"family":"Jones","given":"Scott","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":813087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Takekawa, John Y. 0000-0003-0217-5907","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":203805,"corporation":false,"usgs":false,"family":"Takekawa","given":"John Y.","affiliations":[{"id":36724,"text":"Audubon California, Richardson Bay Audubon Center and Sanctuary, Tiburon, CA","active":true,"usgs":false}],"preferred":false,"id":813089,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":813090,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217744,"text":"sir20205144 - 2021 - Hydrologic and hydraulic analyses of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan","interactions":[],"lastModifiedDate":"2021-02-04T00:38:47.266829","indexId":"sir20205144","displayToPublicDate":"2021-02-03T17:00:00","publicationYear":"2021","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":"2020-5144","displayTitle":"Hydrologic and Hydraulic Analyses of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan","title":"Hydrologic and hydraulic analyses of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan","docAbstract":"The U.S. Geological Survey (USGS) completed hydrologic and hydraulic analyses for selected reaches of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan, in cooperation with the city of Lansing. The study comprised a 3.1-mile reach of the Grand River, a 30.3-mile reach of the Red Cedar River, and a 12.0-mile reach of Sycamore Creek. The information produced from the study can be used to update and expand an existing Federal Emergency Management Agency Flood Insurance Study for Ingham County, Mich.
Historical streamflow data from USGS streamgages on Grand River at Lansing, Mich. (station number 04113000); Red Cedar River at East Lansing, Mich. (station number 04112500); Red Cedar River near Williamston, Mich. (station number 04111379); and Sycamore Creek at Holt Road near Holt, Mich. (station number 04112850) were used to estimate instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities (AEPs) and a “1-percent plus” AEP.
The Hydrologic Engineering Center’s River Analysis System step-backwater model was used to determine water-surface elevation profiles for the 10-, 4-, 2-, 1-, and 0.2-percent AEP floods, the 1-percent plus AEP flood, and a regulatory floodway for each stream reach. The hydraulic models were calibrated based on stage-streamflow ratings at USGS streamgages. Flood-inundation boundaries for the 1- and 0.2-percent annual exceedance probability floods and regulatory floodway were created for each stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205144","collaboration":"Prepared in cooperation with the city of Lansing, Michigan","usgsCitation":"Whitehead, M.T., and Ostheimer, C.J., 2021, Hydrologic and hydraulic analyses of the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan: U.S. Geological Survey Scientific Investigations Report 2020–5144, \n17 p., https://doi.org/10.3133/sir2020–5144.","productDescription":"Report: iv, 17 p.; Data Realease","onlineOnly":"Y","ipdsId":"IP-118378","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":382823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5144/coverthb.jpg"},{"id":382824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5144/sir20205144.pdf","text":"Report","size":"3.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5144"},{"id":382825,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91CQ755","text":"USGS data release","linkHelpText":"Geospatial datasets and hydraulic models for the Grand River, Red Cedar River, and Sycamore Creek near Lansing, Michigan"}],"country":"United States","state":"Michigan","otherGeospatial":"Grand River, Red Cedar River, Sycamore Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.57550048828125,\n 42.48526384858916\n ],\n [\n -83.9959716796875,\n 42.48526384858916\n ],\n [\n -83.9959716796875,\n 42.76465818533266\n ],\n [\n -84.57550048828125,\n 42.76465818533266\n ],\n [\n -84.57550048828125,\n 42.48526384858916\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229
A new methodology that uses recent advances in unsaturated soil mechanics and hydrology was developed and tested. The approach consists of using soil suction and moisture content field information in the prediction of the likelihood of landslide movement. The testing ground was an active landslide on I-70 west of the Eisenhower/Johnson Memorial Tunnels. A joint effort between Colorado School of Mines, CDOT, and USGS performed detailed site characterization, set up and calibrated a hydro-mechanical model of the site based on seven years of field data, and performed a stability analysis of the slope. Results indicate that consecutive years of high or low infiltration have a compounding effect so that the slope stability is influenced by the preceding years. Additionally, a new drainage system is proposed based on analysis of the current horizontal drains.
","language":"English","publisher":"Colorado Department of Transportation","usgsCitation":"Wayllace, A., Lu, N., and Mirus, B., 2021, In-situ monitoring of infiltration-induced instability of I-70 embankment west of the Eisenhower-Johnson Memorial Tunnels, phase III: Colorado Department of Transportation Report 2021-08, 84 p.","productDescription":"84 p.","ipdsId":"IP-126891","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":395894,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395834,"type":{"id":11,"text":"Document"},"url":"https://www.codot.gov/programs/research/pdfs/2021/in-situ-monitoring-of-infiltration-induced-instability-of-i-70-embankment-west-of-the-eisenhower-johnson-memorial-tunnels-phase-iii.pdf"}],"country":"United States","state":"Colorado","otherGeospatial":"Straight Creek slide location","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.96334934234619,\n 39.67218123730546\n ],\n [\n -105.95322132110596,\n 39.67218123730546\n ],\n [\n -105.95322132110596,\n 39.678853450286766\n ],\n [\n -105.96334934234619,\n 39.678853450286766\n ],\n [\n -105.96334934234619,\n 39.67218123730546\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wayllace, Alexandra","contributorId":203213,"corporation":false,"usgs":false,"family":"Wayllace","given":"Alexandra","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":834488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lu, Ning","contributorId":267914,"corporation":false,"usgs":false,"family":"Lu","given":"Ning","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":834489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":834490,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230771,"text":"70230771 - 2021 - Fluid-earthquake and earthquake-earthquake interactions in southern Kansas, USA","interactions":[],"lastModifiedDate":"2022-04-26T15:36:02.791587","indexId":"70230771","displayToPublicDate":"2021-02-01T10:28:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Fluid-earthquake and earthquake-earthquake interactions in southern Kansas, USA","docAbstract":"An increase in injection activity associated with energy production in southern Kansas starting in 2013 has been linked to the occurrence of more than 130,000 earthquakes (M −1.5 to 4.9) between 2014 and 2017. Studies suggest that the dramatic increase in seismicity rate is related to wastewater injection into the highly permeable Arbuckle formation. Most of the seismicity is located in the underlying crystalline basement, for which hydrological properties and specific fault geometries are unknown. Additionally, some earthquake clusters occurred relatively far (tens of kilometers) from the main injection wells. Therefore, the effect of pore pressure diffusion may be insufficient to explain the relation between the volume of injected fluids and the spatiotemporal evolution of seismicity. Combining physical models (static stress and poroelasticity) and a statistical cluster analysis applied to a high-resolution relocated catalog, we analyze the evolution of seismicity in southern Kansas. We find that pore pressure changes (Δp) and Coulomb stress changes (ΔCFS) due to fluid diffusion smaller than 0.1 MPa are enough to initiate seismic sequences, which then evolve depending on their distance from the major injection wells. However, we find that earthquake sequences have different seismogenic responses to Δp and ΔCFS in terms of triggering threshold. In regions located close to disposal wells (Harper area) our cluster analysis suggests that both earthquake interactions and fluid diffusion control the evolution of seismicity. On the other hand, at greater distances (Milan area), where clustering behavior suggests greater earthquake interactions, we find that coseismic ΔCFS are larger than Δp.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB020384","usgsCitation":"Verdecchia, A., Cochran, E.S., and Harrington, R.M., 2021, Fluid-earthquake and earthquake-earthquake interactions in southern Kansas, USA: JGR Solid Earth, v. 126, e2020JB020384, 17 p., https://doi.org/10.1029/2020JB020384.","productDescription":"e2020JB020384, 17 p.","ipdsId":"IP-124580","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":453604,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb020384","text":"Publisher Index Page"},{"id":399674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.3,\n 36.75\n ],\n [\n -97.2,\n 36.75\n ],\n [\n -97.2,\n 37.5\n ],\n [\n -98.3,\n 37.5\n ],\n [\n -98.3,\n 36.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Verdecchia, A.","contributorId":221418,"corporation":false,"usgs":false,"family":"Verdecchia","given":"A.","affiliations":[{"id":40369,"text":"Institute of Geology, Mineralogy and Geophysics, Ruhr-University Bochum, Bochum, Germany; Department of Earth and Environmental Sciences, Ludwig-Maximilians University, Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":841338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":841339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrington, R. M","contributorId":156299,"corporation":false,"usgs":false,"family":"Harrington","given":"R.","email":"","middleInitial":"M","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":841340,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218170,"text":"70218170 - 2021 - Joint species distribution models of Everglades wading birds to inform restoration planning","interactions":[],"lastModifiedDate":"2023-07-07T14:08:20.276686","indexId":"70218170","displayToPublicDate":"2021-01-28T10:04:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Joint species distribution models of Everglades wading birds to inform restoration planning","docAbstract":"Restoration of the Florida Everglades, a substantial wetland ecosystem within the United States, is one of the largest ongoing restoration projects in the world. Decision-makers and managers within the Everglades ecosystem rely on ecological models forecasting indicator wildlife response to changes in the management of water flows within the system. One such indicator of ecosystem health, the presence of wading bird communities on the landscape, is currently assessed using three species distribution models that assume perfect detection and report output on different scales that are challenging to compare against one another. We sought to use current advancements in species distribution modeling to improve models of Everglades wading bird distribution. Using a joint species distribution model that accounted for imperfect detection, we modeled the presence of nine species of wading bird simultaneously in response to annual hydrologic conditions and landscape characteristics within the Everglades system. Our resulting model improved upon the previous model in three key ways: 1) the model predicts probability of occupancy for the nine species on a scale of 0–1, making the output more intuitive and easily comparable for managers and decision-makers that must consider the responses of several species simultaneously; 2) through joint species modeling, we were able to consider rarer species within the modeling that otherwise are detected in too few numbers to fit as individual models; and 3) the model explicitly allows detection probability of species to be less than 1 which can reduce bias in the site occupancy estimates. These improvements are essential as Everglades restoration continues and managers require models that consider the impacts of water management on key indicator wildlife such as the wading bird community.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0245973","usgsCitation":"D’Acunto, L., Pearlstine, L.G., and Romanach, S., 2021, Joint species distribution models of Everglades wading birds to inform restoration planning: PLoS ONE, v. 16, no. 1, e0245973, 21 p.; Data Release, https://doi.org/10.1371/journal.pone.0245973.","productDescription":"e0245973, 21 p.; Data Release","ipdsId":"IP-119201","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":453665,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0245973","text":"Publisher Index Page"},{"id":383272,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418748,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P934K8T0","text":"EverWaders species distribution model development and output in the Greater Everglades from 2000-2009","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.419921875,\n 25.209911213827688\n ],\n [\n -80.474853515625,\n 25.27450351782018\n ],\n [\n -80.562744140625,\n 25.348990395713393\n ],\n [\n -80.562744140625,\n 25.55730943578312\n ],\n [\n -80.299072265625,\n 26.288490072961164\n ],\n [\n -80.1617431640625,\n 26.426308999847024\n ],\n [\n -80.26611328125,\n 26.711266913515747\n ],\n [\n -80.4473876953125,\n 26.716173757934094\n ],\n [\n -80.5133056640625,\n 26.554136386183785\n ],\n [\n -80.79345703125,\n 26.504988828743404\n ],\n [\n -81.0296630859375,\n 26.455820238459893\n ],\n [\n -81.00082397460938,\n 26.292183791046618\n ],\n [\n -81.47872924804688,\n 26.223215001067558\n ],\n [\n -81.64352416992188,\n 26.172694044887898\n ],\n [\n -81.78085327148438,\n 26.078987535225927\n ],\n [\n -81.74514770507812,\n 25.90617390922084\n ],\n [\n -81.6339111328125,\n 25.808545671771615\n ],\n [\n -81.38259887695312,\n 25.720735134412106\n ],\n [\n -81.24252319335938,\n 25.492868271257127\n ],\n [\n -81.18209838867188,\n 25.370086680063082\n ],\n [\n -81.17660522460938,\n 25.21488107113259\n ],\n [\n -81.03790283203125,\n 25.069429002821355\n ],\n [\n -80.58059692382812,\n 25.14652775303499\n ],\n [\n -80.419921875,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810303,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearlstine, Leonard G.","contributorId":34751,"corporation":false,"usgs":false,"family":"Pearlstine","given":"Leonard","email":"","middleInitial":"G.","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":810304,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":810305,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217664,"text":"sir20205121 - 2021 - Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018","interactions":[],"lastModifiedDate":"2021-01-28T01:29:43.632301","indexId":"sir20205121","displayToPublicDate":"2021-01-27T16:00:00","publicationYear":"2021","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":"2020-5121","displayTitle":"Spring Types and Contributing Aquifers from Water-Chemistry and Multivariate Statistical Analyses for Seeps and Springs in Theodore Roosevelt National Park, North Dakota, 2018","title":"Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018","docAbstract":"Water resources in Theodore Roosevelt National Park, North Dakota, support wildlife, visitors, and staff, and play a vital role in supporting the native ecology of the park. The U.S. Geological Survey, in cooperation with the National Park Service, completed field work in 2018 for a study to address concerns about water availability and possible sources of groundwater contamination for seeps and springs in Theodore Roosevelt National Park. The objective of the study was to improve hydrologic knowledge and determine the water composition of 11 seeps and springs in the park by collecting water-chemistry data at springs, streams, wells, and rain collectors.
Water samples were collected at 26 sites at springs, streams, wells, and rain collectors in the North and South Units of Theodore Roosevelt National Park. Samples in the North Unit were collected at 5 springs, 1 stream, 2 wells, and 1 rain collector. Samples in the South Unit were collected at 6 springs, 2 streams, 8 wells, and 1 rain collector. Samples from springs, streams, and wells were collected in May, July, and September 2018. Samples from rain collectors were collected when enough daily precipitation accumulated in the collectors. Sampled precipitation events during the study period were in May, June, July, August, and September 2018. Physical properties of sampled water—temperature, pH, and specific conductance—were measured in the field. Water samples were analyzed for stable isotopes of oxygen and hydrogen and for chloride concentration. Recharge rates for aquifers supplying springs were determined using precipitation volume and chloride concentrations for a 12-day period before the sample-collection date. Multivariate statistical analysis methods used on water-chemistry data included principal component analysis, cluster analysis, and end-member mixing analysis.
Water composition was used to determine the spring type and contributing aquifers for 11 springs in the North and South Units of Theodore Roosevelt National Park from analyses of water-chemistry data between May and September 2018. In the North Unit, Achenbach Spring was classified as a filtration spring with water from an unconfined part of the upper Fort Union aquifer and infiltration of precipitation. Hagen Spring, Mandal Spring, and Stevens Spring were classified as contact springs supplied by semiconfined parts of the upper Fort Union aquifer. Overlook Spring at one time may have been a natural spring or seep but now is a developed spring that behaves like a flowing artesian well completed in a confined part of the upper Fort Union aquifer. In the South Unit, six springs were classified into two spring types: filtration and contact springs. Boicourt Spring and Sheep Butte Spring were classified as filtration springs that have water supplied by unconfined parts of the upper Fort Union aquifer and infiltrated precipitation. Big Plateau Spring, Lone Tree Spring, Sheep Pasture Spring, and Southeast Corner Spring were classified as contact springs that receive waters from a semiconfined part of the upper Fort Union aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205121","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., and Eldridge, W.G., 2021, Spring types and contributing aquifers from water-chemistry and multivariate statistical analyses for seeps and springs in Theodore Roosevelt National Park, North Dakota, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5121, 48 p., https://doi.org/10.3133/sir20205121.","productDescription":"Report: viii, 48 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115769","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":382693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5121/coverthb.jpg"},{"id":382694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5121/sir20205121.pdf","text":"Report","size":"4.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"sir2020-5121"},{"id":382695,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"North Dakota","otherGeospatial":"Theodore Roosevelt National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63334655761719,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 46.87990702860922\n ],\n [\n -103.29757690429686,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 47.02801434856074\n ],\n [\n -103.63334655761719,\n 46.87990702860922\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.48983764648438,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.52832925298343\n ],\n [\n -103.216552734375,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.65428791076272\n ],\n [\n -103.48983764648438,\n 47.52832925298343\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.63677978515625,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.22726254715105\n ],\n [\n -103.60965728759764,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.250106104326235\n ],\n [\n -103.63677978515625,\n 47.22726254715105\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
This report provides a groundwater-flow conceptualization that integrates geologic, hydrologic, hydraulic-property, and radionuclide data in the Pahute Mesa–Oasis Valley (PMOV) groundwater basin, southern Nevada. Groundwater flow in the PMOV basin is of interest because 82 underground nuclear tests were detonated, most near or below the water table. A potentiometric map and nine sets of hydrostratigraphic and hydrologic cross sections supplement the conceptualization.
Potentiometric contours indicate that groundwater in the PMOV basin generally flows south-southwest and discharges at Oasis Valley. Groundwater encounters an alternating sequence of low- and high-transmissivity rocks, referred to as dams and pools, respectively, as it moves from east to west across eastern Pahute Mesa. Flow from all Pahute Mesa nuclear tests is to Oasis Valley and is well-constrained by water-level data. Flow converges along a corridor of high transmissivity between Pahute Mesa and Oasis Valley.
The location of the lateral PMOV basin boundary is well defined, and this boundary, with a few minor exceptions, represents a no-flow boundary. Some boundary uncertainty exists in the northeastern part of the basin, but potential flow-rate estimates across the northeastern boundary resulting from this uncertainty are small relative to the basin groundwater budget.
Recharge in the PMOV basin is derived from episodic pulses of modern water and the diffuse percolation of old water (greater than 1,000 years). Episodic recharge is a minor recharge component observed as a rise in groundwater levels that occurs 3 months to 1 year following a wet winter. Minor amounts of episodic recharge through an unsaturated zone in excess of 1,000 feet (ft) requires preferential flow through faults and fractures. The dominant recharge component is slow, steady, diffuse percolation of old water through the unsaturated zone. A large component of old water recharging the groundwater system is consistent with observations of isotopically light deuterium and oxygen 18 compositions in water from wells on Pahute Mesa and central Oasis Valley. About half the recharge in the PMOV basin is derived from the eastern Pahute Mesa area. The remaining recharge is derived primarily from other highland areas including Timber Mountain, Belted and Kawich Ranges, and Black Mountain.
The PMOV groundwater system is nearly steady state, where recharge is balanced by the 5,900 acre-feet per year of natural discharge at Oasis Valley. This assumption is reasonable because the basin is dominated by steady-state conditions, where long-term changes in groundwater storage are minimal. Total groundwater withdrawals from 1963 to 2018 have amounted to less than 10 percent of annual groundwater discharge and less than 0.2 percent of the basin’s groundwater storage. Therefore, present-day (2020) conditions are considered representative of predevelopment (pre-1950) conditions in nearly all areas of the basin.
The lower PMOV basin boundary is defined at 4,000 ft below the water table to encompass all underground nuclear tests and tritium plumes. This boundary defines the lower boundary of radionuclide migration. However, nearly all flow and tritium transport occur in the upper 1,600 ft of the saturated zone because a transmissivity-with-depth relation indicates that greater than 90 percent of the transmissivity contributing to groundwater flow occurs within 1,600 ft of the water table. Rocks at deeper depths have low transmissivity because argillic and mineralized alterations plug the fractures.
Volcanic rocks form the primary aquifers and confining units in the PMOV basin. Volcanic hydrogeologic units (HGUs) and hydrostratigraphic units (HSUs) have transmissivity distributions that span up to eight orders of magnitude with considerable overlap between distributions. Despite the large overlap between units, mean transmissivities of aquifers are one-to-two orders of magnitude greater than the confining units. However, all volcanic-rock HGUs and HSUs are composite units, meaning that they can function spatially as either an aquifer or confining unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205134","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management under Interagency Agreement, DE-EM0004969","usgsCitation":"Jackson, T.R., Fenelon, J.M., and Paylor, R.L., 2021, Groundwater flow conceptualization of the Pahute Mesa–Oasis Valley Groundwater Basin, Nevada—A synthesis of geologic, hydrologic, hydraulic-property, and tritium data: U.S. Geological Survey Scientific Investigations Report 2020–5134, 100 p., https://doi.org/10.3133/sir20205134.","productDescription":"Report: viii, 100 p.; 2 Plates: 26.00 x 42.00 inches and 120.01 x 36.00 inches; 7 Appendixes","onlineOnly":"Y","ipdsId":"IP-095406","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":382683,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix2.xlsx","text":"Appendix 2","size":"78 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 2"},{"id":382684,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix3.xlsm","text":"Appendix 3","size":"530 KB xlsm","description":"SIR 2020-5134 Appendix 3"},{"id":382685,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix4.xlsx","text":"Appendix 4","size":"6.1 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 4"},{"id":382681,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate02.pdf","text":"Plate 2","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 2"},{"id":382678,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5134/coverthb.jpg"},{"id":382679,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134.pdf","text":"Report","size":"9.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134"},{"id":382680,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_plate01.pdf","text":"Plate 1","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5134 Plate 1"},{"id":382682,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix1.xlsx","text":"Appendix 1","size":"2.5 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 1"},{"id":382688,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix7.xlsx","text":"Appendix 7","size":"433 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 7"},{"id":382687,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix6.xlsx","text":"Appendix 6","size":"856 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 6"},{"id":382686,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5134/sir20205134_appendix5.xlsx","text":"Appendix 5","size":"799 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020-5134 Appendix 5"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa–Oasis Valley Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 36.65079252503471\n ],\n [\n -116.00,\n 38.00\n ],\n [\n -117.00,\n 38.00\n ],\n [\n -117.00,\n 36.65079252503471\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/trans.13938","usgsCitation":"Moeser, C.D., and Douglas-Mankin, K.R., 2021, Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.: Transactions of the American Society of Agricultural and Biological Engineers, v. 64, no. 1, p. 137-150, https://doi.org/10.13031/trans.13938.","productDescription":"14 p.","startPage":"137","endPage":"150","ipdsId":"IP-101142","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":382749,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809288,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217875,"text":"70217875 - 2021 - Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States","interactions":[],"lastModifiedDate":"2021-02-09T13:17:48.692204","indexId":"70217875","displayToPublicDate":"2021-01-27T07:13:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5836,"text":"Journal of Hydrology X","onlineIssn":"2589-9155","active":true,"publicationSubtype":{"id":10}},"title":"Changing climate drives future streamflow declines and challenges in meeting water demand across the southwestern United States","docAbstract":"Society and the environment in the arid southwestern United States depend on reliable water availability, yet current water use outpaces supply. Water demand is projected to grow in the future and climate change is expected to reduce supply. To adapt, water managers need robust estimates of future regional water supply to support management decisions. To address this need, we estimate future streamflow in seven water resource regions in the southwestern U.S. using a new SPAtially Referenced Regressions On Watershed attributes (SPARROW) streamflow model. We present streamflow projections corresponding to input data from seven climate models and two greenhouse gas Representative Concentration Pathways (RCP4.5 and 8.5) for three, thirty-year intervals centered on the 2030s, 2050s, and 2080s, and for a historical thirty year interval centered on the 1990s. Across water resource regions, about half of the RCP4.5 models (51%) and two thirds of the RCP8.5 models (67%) indicate decreases in streamflow in the 2080s relative to the historical period. Models project maximum decreases in streamflow of 36–80% in all water resource regions for all periods and RCPs relative to historical streamflow, and maximum streamflow decreases of up to 20–45% in the 2080s at sites along the Colorado River used for measuring compliance with interstate and international water agreements. Headwaters are projected to experience the greatest declines, with substantial downstream implications. Among these estimates, the streamflows from models forced with RCP8.5 tend to be lower than those forced with RCP4.5. Not all climate models, times, and RCPs project widespread streamflow declines. The most ubiquitous streamflow increases are projected to occur in the 2030s under RCP4.5. Later time periods and enhanced greenhouse gas forcings indicate smaller regions of streamflow increase and lower accumulated streamflows, suggesting that limiting or reducing greenhouse gas concentrations could support future water availability. Although some possible streamflow increases are promising, the modest and spatially limited increases in streamflow projected for later time periods are still unlikely to be sufficient to meet the projected water demand. These results inform the likelihood of future water agreement compliance, and support developing strategies to balance water supply and demand.
Mechanisms of runoff generation in the humid tropics are poorly understood, particularly in the context of land-use/land cover change. This study analyzed the results of 124 storm hydrographs from three humid tropical catchments of markedly different vegetation cover and land-use history in central Panama during the 2017 wet season: actively grazed pasture, young secondary succession, and near-mature forest. We used electrical conductivity to separate baseflow (old water) from storm-event water (new-water). In all three land covers, new-water dominated storm runoff generation in 44% of the sampled storm events, indicating the dominance of fast shallow flow paths in the landscape. Activation of these flow paths was found to depend on a combination of maximum rainfall intensity and total storm rainfall, which, in turn, relates to markedly contrasting hydrograph separation results among land covers. Relationships between these rainfall characteristics and storm runoff generation were nonlinear, producing a threshold response with the exceedance of specific rainfall volumes and/or intensities. The pastoral catchment delivered order of magnitude more new-water during storm events than the two forested catchments. Although new-water contributed minimally (<10%) to total wet season runoff in the forested catchments, 43% of runoff generation in the pasture came from five large rainfall events where a threshold response produced substantial increases in total runoff and new-runoff efficiency. Based on our results, we propose a conceptual model of hydrologic flow paths in humid tropical systems that can explain previously observed disparities in seasonal storage and runoff with respect to land use/land cover.
The U.S. Geological Survey (USGS), in collaboration with university, Federal, Tribal, and independent partners, conducts fundamental research on the distribution, vulnerability, and importance of permafrost in arctic and boreal ecosystems. Scientists, land managers, and policy makers use USGS data to help make decisions for development, wildlife habitat, and other needs. Native villages and cities can forecast landscape change and where soils are vulnerable to thaw with more certainty. The scientific community can use USGS data to develop scenarios of future permafrost change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203058","usgsCitation":"Waldrop, M.P., Anderson, L., Dornblaser, M., Erikson, L.H., Gibbs, A.E., Herman-Mercer, N.M., James, S.R., Jones, M.C., Koch, J.C., Leewis, M.-C., Manies, K.L., Minsley, B.J., Pastick, N.J., Patil, V., Urban, F., Walvoord, M.A., Wickland, K.P., and Zimmerman, C., 2021, USGS permafrost research determines the risks of permafrost thaw to biologic and hydrologic resources: U.S. Geological Survey Fact Sheet 2020–3058, 6 p., https://doi.org/10.3133/fs20203058.","productDescription":"6 p.","ipdsId":"IP-116939","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":382041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3058/coverthb.jpg"},{"id":382042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3058/fs20203058.pdf","text":"Report","size":"8.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3058"}],"country":"United 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Regional Director
4210 University Drive
Anchorage, AK 99508
907–786–7091
No abstract available.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2021.125982","usgsCitation":"Mahler, B., Yongjun, J., Pu, J., and Martin, J., 2021, Editorial: Advances in hydrology and the water environment in the karst critical zone under the impacts of climate change and anthropogenic activities: Journal of Hydrology, v. 595, 125982, 6 p., https://doi.org/10.1016/j.jhydrol.2021.125982.","productDescription":"125982, 6 p.","ipdsId":"IP-125436","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":384271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"595","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":811576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yongjun, Jiang 0000-0002-9936-698X","orcid":"https://orcid.org/0000-0002-9936-698X","contributorId":254975,"corporation":false,"usgs":false,"family":"Yongjun","given":"Jiang","email":"","affiliations":[{"id":51378,"text":"Southwest University","active":true,"usgs":false}],"preferred":false,"id":811577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pu, Junbing 0000-0003-0418-4719","orcid":"https://orcid.org/0000-0003-0418-4719","contributorId":254976,"corporation":false,"usgs":false,"family":"Pu","given":"Junbing","email":"","affiliations":[{"id":51380,"text":"Chinese Academy of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":811578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Jonathan 0000-0001-7047-0321","orcid":"https://orcid.org/0000-0001-7047-0321","contributorId":254977,"corporation":false,"usgs":false,"family":"Martin","given":"Jonathan","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":811579,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217368,"text":"70217368 - 2021 - Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models","interactions":[],"lastModifiedDate":"2024-09-16T22:32:11.340035","indexId":"70217368","displayToPublicDate":"2021-01-12T07:59:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models","docAbstract":"Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as < 65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated U.S. is about 6,000 km3, or about 0.5% of globally stored young groundwater.