This report characterizes changes in peak streamflow in Missouri and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-streamflow frequency analysis in Missouri. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; and 30 years, 1991–2020) for trends, change points (abrupt changes in the streamflow time series), and other statistical properties indicative of changing conditions. Peak streamflow magnitudes generally exhibit upward trends across the State for the 100-, 75-, and 50-year trend periods and only in southern Missouri for the 30-year trend period. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 10-percent increase during the 30-year trend period to a 40-percent increase during the 100-year trend period. Changes in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, are not as substantial or widespread, showing consistent increases mainly in the southern part of the State in the 50- and 30-year trend periods. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. Change points in peak streamflows generally follow that of the peak streamflow trends, with upward change points throughout most of the State at the 100- and 75-year trend periods and in southern Missouri at the 30-year trend period. Temporally, clusters upward of change points are observed in the 1970s through 1980s for the 100-, 75-, and 50-year trend periods and around 2006 and 2007 for the 50- and 30-year trend periods.
A peaks-over-threshold analysis, which evaluates changes in the frequency of peak streamflows over a certain threshold, indicates that high flows have increased in frequency at 50 to 64 percent of streamgages in the 100- and 75-year trend periods. Most streamgages in the 50- and 30-year trend periods exhibit no change. Although the frequency of high flows has increased at some streamgages and trend periods in Missouri, these increases are not as widespread as the increases in the magnitude of peak streamflow.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with no downward trends in precipitation and only two somewhat likely downward trends in temperature for the 100-year trend period. Increases in annual precipitation mostly are limited to southern Missouri for the 30-year trend period. The proportion of precipitation falling as snow has largely decreased in the study basins across the State, which is expected in response to increasing temperature. Upward trends in modeled annual runoff, which in this study incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation. When peak streamflow and climatic trends are considered together, widespread upward trends in peak streamflows for the 100-, 75-, and 50-year trend periods and for the 30-year trend period mainly in southern Missouri (encompassing both trends and abrupt change) appear to be driven largely by increases in precipitation based on spatial patterns and statistical relations.
The prevalence of nonstationarity in peak streamflow in Missouri has important implications for peak-flow frequency analysis. Winter and spring precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.
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Wetland ecosystems hold nearly a third of the global soil carbon pool, but as wetlands rapidly disappear the fate of this stored soil carbon is unclear. The aim of this study was to quantify and then link potential rates of microbial decomposition after vertical drowning of vegetated tidal marshes in coastal Louisiana to known drivers of anaerobic decomposition altered by vegetation loss. Profiles of potential CH4 and CO2 production (surface to 60 cm deep) were measured during anaerobic incubations, organic matter chemistry was assessed with infrared spectroscopy, and soil porewater nutrients and redox potentials were measured in the field along a chronosequence of wetland loss. After vertical drowning, pond soils had lower redox potentials, higher pH values, lower soil carbon and nitrogen concentrations, lower lignin: polysaccharide ratios, more NH4+ and PO43−, and higher rates of potential CO2 release than vegetated marsh soils. Potential CH4 production was similar in vegetated marshes and open water ponds, with depth-dependent decreases in CH4 production as soil carbon concentrations increased. In these anoxic soils, vegetation loss exerts a primary control on decomposition rates because flooding drives sustained increases in porewater nutrient availability (NH4+ and PO43, dissolved organic carbon) and decreases in redox potential (from −150 to −500 mV) that lead to higher potential CO2 fluxes within a few years. Without new carbon inputs following wetland loss, the sustained decomposition in open water ponds may lead to losses of stored soil carbon and could influence global carbon budgets.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007832","usgsCitation":"Creamer, C., Waldrop, M., Stagg, C., Manies, K.L., Baustian, M.M., Laurenzano, C., Aw, T.G., Haw, M., Merino, S., Schoolmaster, D.R., Sevilgen, S.N., Villani, R.K., and Ward, E., 2024, Vegetation loss following vertical drowning of Mississippi River deltaic wetlands leads to faster microbial decomposition and decreases in soil carbon: JGR Biogeosciences, v. 129, no. 4, e2023JG007832, 17 p., https://doi.org/10.1029/2023JG007832.","productDescription":"e2023JG007832, 17 p.","ipdsId":"IP-156115","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":434988,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YJ25DP","text":"USGS data release","linkHelpText":"Plant, soil, and microbial characteristics of marsh collapse in Mississippi River Deltaic wetlands"},{"id":427817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi River Deltaic Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.114692623043,\n 29.608206673708935\n ],\n [\n -90.114692623043,\n 29.52716446952803\n ],\n [\n -90.0419601124058,\n 29.52716446952803\n ],\n [\n -90.0419601124058,\n 29.608206673708935\n ],\n [\n -90.114692623043,\n 29.608206673708935\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Creamer, Courtney 0000-0001-8270-9387","orcid":"https://orcid.org/0000-0001-8270-9387","contributorId":201952,"corporation":false,"usgs":true,"family":"Creamer","given":"Courtney","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":898895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":898896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":220330,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":898898,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898899,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laurenzano, Claudia 0000-0003-1406-8658","orcid":"https://orcid.org/0000-0003-1406-8658","contributorId":218316,"corporation":false,"usgs":false,"family":"Laurenzano","given":"Claudia","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898900,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aw, Tiong Gim","contributorId":335353,"corporation":false,"usgs":false,"family":"Aw","given":"Tiong","email":"","middleInitial":"Gim","affiliations":[{"id":80377,"text":"Department of Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University","active":true,"usgs":false}],"preferred":false,"id":898901,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haw, Monica","contributorId":168603,"corporation":false,"usgs":false,"family":"Haw","given":"Monica","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":898902,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Merino, Sergio 0000-0002-2834-2243 merinos@usgs.gov","orcid":"https://orcid.org/0000-0002-2834-2243","contributorId":3653,"corporation":false,"usgs":true,"family":"Merino","given":"Sergio","email":"merinos@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898903,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Schoolmaster, Donald R. Jr. 0000-0003-0910-4458 schoolmasterd@usgs.gov","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":4746,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","suffix":"Jr.","email":"schoolmasterd@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":898904,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sevilgen, Sabrina N. 0000-0002-1265-1842","orcid":"https://orcid.org/0000-0002-1265-1842","contributorId":298537,"corporation":false,"usgs":true,"family":"Sevilgen","given":"Sabrina","email":"","middleInitial":"N.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":898905,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Villani, Rachel Katherine 0000-0002-8494-8178","orcid":"https://orcid.org/0000-0002-8494-8178","contributorId":290308,"corporation":false,"usgs":true,"family":"Villani","given":"Rachel","email":"","middleInitial":"Katherine","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898906,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":217389,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898907,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70253098,"text":"70253098 - 2024 - Remotely mapping gullying and incision in Maryland Piedmont headwater streams using repeat airborne lidar","interactions":[],"lastModifiedDate":"2024-04-19T12:23:43.348649","indexId":"70253098","displayToPublicDate":"2024-04-15T07:21:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Remotely mapping gullying and incision in Maryland Piedmont headwater streams using repeat airborne lidar","docAbstract":"Headwater streams can contribute significant amounts of fine sediment to downstream waterways, especially when severely eroded and incised. Potential upstream sediment source identification is crucial for effective management of water quality, aquatic habitat, and sediment loads in a watershed. This study explored topographic openness (TO) derived from 1-m lidar for its ability to predict incision in headwater streams and to remotely detect changes in incision over time. Field surveys were conducted in one forested and two recently urbanized headwater watersheds in the Maryland Piedmont physiographic province, USA to characterize the level of stream channel incision (none, moderate, or severe) in the main stem of each watershed. Predictions of the severity of stream channel incision derived from TO were compared against the field surveys. Channel incision was detected with an overall accuracy of 67 %, with best performance in reaches with either severe or no incision (79–86 % accuracy). The method was also applied to repeat lidar collected over the same area to model the extent of channel incision in 2002 before urban development began and in 2008 and 2013 during active construction in the urban watersheds. Results showed increasing incision over time in all three watersheds, with similar patterns in the forested and urban watersheds. This new method of remotely measuring channel incision can be used to identify potential sediment sources across a watershed, enhance water and habitat quality predictions, and detect changes over time where multiple years of overlapping lidar are available.
Groundwater-resource quality is assumed to be less responsive to drought compared to that of surface water due to relatively long transit times of recharge to drinking-supply wells. Here, we evidence dynamic perturbations in aquifer pressure dynamics during drought and subsequent recovery periods cause dramatic shifts in groundwater quality on a basin scale. We used a novel application of time-series clustering on annual nitrate anomalies at >450 public-supply wells (PSWs) across California's San Joaquin Valley during 2000–22 to group sub-populations of wells with similar water-quality responses to drought. Additionally, we statistically evaluated the direction and magnitude of multi-constituent water-quality changes across the San Joaquin Valley using a broader dataset of >3000 PSWs with data during two select hydrologic stress periods representing an extreme drought (2012–16) and subsequent recovery (2016–19). Results of time-series clustering and stress-period change analyses corroborate a predominant regional response to pumping stress characterized by increased concentrations of anthropogenic constituents (nitrate, total dissolved solids) and decreased concentrations of geogenic constituents (arsenic, fluoride), which largely reversed during recovery. Cluster analysis also identified a secondary, less commonly occurring group of PSWs where nitrate decreased during drought, but explanatory factor analysis was not able to discern hydrogeologic drivers for these two divergent response patterns. Long-term tracer data support the hypothesis that the predominant regional signal of nitrate increase during drought is caused by enhanced capture of modern-aged groundwater by PSWs during periods of pumping stress, which can drive rapid changes in water quality on seasonal and multiannual timescales. Pumping-induced migration of modern, oxic groundwater to depth during drought may affect geochemical conditions in deeper portions of regional aquifers controlling the mobility of geogenic contaminants over the long term.
About 80 % of the precipitation at the Colorado River's headwaters is snow, and the resulting snowmelt-driven hydrograph is a crucial water source for about 40 million people. Snowmelt from alpine and subalpine snowpack contributes substantially to groundwater recharge and river flow. However, the dynamics of snowmelt progression are not well understood because observations of the high-elevation snowpack are difficult due to challenging access in complex mountainous terrain as well as the cost and labor intensity of currently available methods. We present a novel approach to infer the processes and dynamics of high-elevation snowmelt contributions predicated upon stable hydrogen and oxygen isotope ratios observed in streamflow. We show that deuterium-excess (d-excess) values of stream water could serve as a comparatively cost-effective proxy for a catchment-integrated signal of high-elevation snowmelt contributions to catchment runoff.
We sampled stable hydrogen and oxygen isotope ratios of the precipitation, snowpack, and stream water in the East River, a headwater catchment of the Colorado River, and the stream water of larger catchments at sites on the Gunnison River and Colorado River.
The d-excess of snowpack increased with elevation; the upper subalpine and alpine snowpack (> 3200 m) had substantially higher d-excess compared to lower elevations (< 3200 m) in the study area. The d-excess values of stream water reflected this because d-excess values increased as the higher-elevation snowpack contributed more to stream water generation later in the snowmelt/runoff season. End-member mixing analyses based on the d-excess data showed that the share of high-elevation snowmelt contributions within the snowmelt hydrograph was on average 44 % and generally increased during melt period progression, up to 70 %. The observed pattern was consistent during 6 years for the East River, and a similar relation was found for the larger catchments on the Gunnison and Colorado rivers. High-elevation snowpack contributions were found to be higher for years with lower snowpack and warmer spring temperatures. Thus, we conclude that the d-excess of stream water is a viable proxy to observe changes in high-elevation snowmelt contributions in catchments at various scales. Inter-catchment comparisons and temporal trends of the d-excess of stream water could therefore serve as a catchment-integrated measure to monitor if mountain systems rely on high-elevation water inputs more during snow drought compared to years of average snowpack depths.
Fluvial strata of the Upper Triassic Chinle Formation and Dockum Group, exposed across the Western Interior of North America, have long been interpreted to record a transcontinental river system that connected the ancestral Ouachita orogen of Texas and Oklahoma, USA, to the Auld Lang Syne basin of northwestern Nevada, USA, its inferred marine terminus. Fluvial strata are well-characterized by existing detrital zircon data, but the provenance of the Auld Lang Syne basin is poorly constrained. We present new detrital zircon U-Pb and Hf isotopic data that characterize the provenance of Norian siliciclastic strata that dominate the Auld Lang Syne basin. Mixture modeling of Auld Lang Syne basin data identifies the Alleghany−Ouachita−Marathon belt of eastern Laurentia as a dominant source of sediment, but the presence of Triassic detrital zircon grains in Auld Lang Syne basin strata indicates that at least one peri-Laurentian arc segment had to have also contributed sediment. A comparison of new Hf isotopic data with those characterizing various peri-Laurentian volcanic arcs demonstrates that although multiple arc segments may have simultaneously contributed zircons to the Auld Lang Syne basin, the west Pangean arc of northern Mexico stands out as a unique source of highly evolved Permian to Triassic detrital zircon grains in samples from the Auld Lang Syne basin. Altogether, our data and analyses demonstrate source-to-sink connectivity between the Late Triassic (Norian) Cordilleran margin and remnant late Paleozoic highlands of southern to eastern Laurentia, which ultimately framed a Mississippi River−scale, transcontinental watershed that traversed the topographically subdued Laurentian continental interior.
We examined Holocene benthic foraminiferal biofacies, % planktonic foraminifera, and lithofacies changes from New England mud patch cores and present a relative sea-level (RSL) record to evaluate evolution of these rapidly deposited (30–79 cm/kyr) muds. Sandy lower Holocene sections are dominated by Bulimina marginata. The mud patch developed from 11–9 ka as RSL rise slowed from 10 to 7 mm/yr; mud deposition began when the cores (69 to 91 m modern) were inundated below storm wave base. An Elphidium-B. marginata fauna developed at ca. 7–6 ka as RSL rise slowed from approximately 7 to 2 mm/yr. A Globobulimina fauna developed at 3 ka as RSL rise slowed to 1 mm/yr, reflecting lower O2 conditions. Single specimen δ18O analyses of Globobulimina show ∼1‰ variations over the past 3 kyr, reflecting a shelf bottom water seasonal cycle of 4–5°C, and a temperature minimum during the Little Ice Age with warming since.
Drought-induced groundwater decline and warming associated with climate change are primary threats to dryland riparian woodlands. We used the extreme 2012–2019 drought in southern California as a natural experiment to assess how differences in water-use strategies and groundwater dependence may influence the drought susceptibility of dryland riparian tree species with overlapping distributions. We analyzed tree-ring stable carbon and oxygen isotopes collected from two cottonwood species (Populus trichocarpa and P. fremontii) along the semi-arid Santa Clara River. We also modeled tree source water δ18O composition to compare with observed source water δ18O within the floodplain to infer patterns of groundwater reliance. Our results suggest that both species functioned as facultative phreatophytes that used shallow soil moisture when available but ultimately relied on groundwater to maintain physiological function during drought. We also observed apparent species differences in water-use strategies and groundwater dependence related to their regional distributions. P. fremontii was constrained to more arid river segments and ostensibly used a greater proportion of groundwater to satisfy higher evaporative demand. P. fremontii maintained ∆13C at pre-drought levels up until the peak of the drought, when trees experienced a precipitous decline in ∆13C. This response pattern suggests that trees prioritized maintaining photosynthetic processes over hydraulic safety, until a critical point. In contrast, P. trichocarpa showed a more gradual and sustained reduction in ∆13C, indicating that drought conditions induced stomatal closure and higher water use efficiency. This strategy may confer drought avoidance for P. trichocarpa while increasing its susceptibility to anticipated climate warming.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035928","usgsCitation":"Williams, J., Stella, J.C., Singer, M.B., Lambert, A.M., Voelker, S.L., Drake, J.E., Friedman, J.M., Pelletier, L., Kui, L., and Roberts, D.A., 2024, Seasonal and species-level water-use strategies and groundwater dependence in dryland riparian woodlands during extreme drought: Water Resources Research, v. 60, no. 4, e2023WR035928, 19 p., https://doi.org/10.1029/2023WR035928.","productDescription":"e2023WR035928, 19 p.","ipdsId":"IP-154460","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439860,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr035928","text":"Publisher Index Page"},{"id":427819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Santa Clara River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.3291956794047,\n 34.69656575071852\n ],\n [\n -119.29876507962365,\n 34.69656575071852\n ],\n [\n -119.29876507962365,\n 34.154097435682715\n ],\n [\n -118.3291956794047,\n 34.154097435682715\n ],\n [\n -118.3291956794047,\n 34.69656575071852\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-04-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Jared","contributorId":298516,"corporation":false,"usgs":false,"family":"Williams","given":"Jared","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":898880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stella, John C.","contributorId":174328,"corporation":false,"usgs":false,"family":"Stella","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":27417,"text":"Department of Forest and Natural Resources Management, SUNY College of Environmental Science and Forestry, Syracuse, NY 13210 USA","active":true,"usgs":false}],"preferred":false,"id":898881,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singer, Michael Bliss 0000-0002-6899-2224","orcid":"https://orcid.org/0000-0002-6899-2224","contributorId":299449,"corporation":false,"usgs":false,"family":"Singer","given":"Michael","email":"","middleInitial":"Bliss","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":898882,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lambert, Adam M.","contributorId":335631,"corporation":false,"usgs":false,"family":"Lambert","given":"Adam","email":"","middleInitial":"M.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":898883,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voelker, Steven L.","contributorId":176586,"corporation":false,"usgs":false,"family":"Voelker","given":"Steven","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":898884,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drake, John E.","contributorId":335632,"corporation":false,"usgs":false,"family":"Drake","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":898885,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":898886,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pelletier, Lissa","contributorId":298520,"corporation":false,"usgs":false,"family":"Pelletier","given":"Lissa","email":"","affiliations":[{"id":27852,"text":"State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":898887,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kui, Li","contributorId":194515,"corporation":false,"usgs":false,"family":"Kui","given":"Li","email":"","affiliations":[],"preferred":false,"id":898888,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Roberts, Dar A.","contributorId":100503,"corporation":false,"usgs":false,"family":"Roberts","given":"Dar","email":"","middleInitial":"A.","affiliations":[{"id":12804,"text":"Univ. of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":898889,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70255266,"text":"70255266 - 2024 - Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands","interactions":[],"lastModifiedDate":"2024-06-13T14:22:10.265931","indexId":"70255266","displayToPublicDate":"2024-04-12T08:34:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5201,"text":"Nature Plants","onlineIssn":"2055-0278","active":true,"publicationSubtype":{"id":10}},"title":"Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands","docAbstract":"Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. Here we evaluated the relative importance of grazing pressure and herbivore type, climate and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this, we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.
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These actions have the potential to affect downstream resources, including threatened and endangered species, in the Grand Canyon. This report covers modeling support provided on the two sEIS by the USGS Grand Canyon Monitoring and Research Center (GCMRC; U.S. Geological Survey, Southwest Biological Science Center). The first sEIS (Interim Guidelines sEIS) modifies Reclamation’s 2007 Colorado River Interim Guidelines for Lower Basin Shortages and Coordinated Operations for Lake Powell and Lake Mead (USBR, 2007) that determines annual water releases from GCD based on inflow to Lake Powell, the power generating requirements of GCD, and relative lake levels of Lake Powell and Lake Mead. Drought conditions have lowered the level of Lake Powell and may require GCD to release less water than was analyzed in the Interim EIS (7-million-acre feet/year). The effect of less water released, as well as lower lake levels and associated water quality concerns, on downstream resources was not analyzed in the 2007 Interim Guidelines EIS. Reclamation requested GCMRC support to provide models predicting the effects to resources of water releases lower than 7M acre feet/year, including models predicting effects to threatened and endangered species for use in a Biological Assessment.","language":"English","publisher":"Bureau of Reclamation","collaboration":"Bureau of Reclamation, Glen Canyon Dam Adaptive Management Program","usgsCitation":"Yackulic, C., Bair, L., Eppehimer, D.E., Salter, G.L., Deemer, B., Butterfield, B.J., Kasprak, A., Caster, J., Fairley, H.C., Grams, P.E., Mihalevich, B.A., Palmquist, E.C., and Sankey, J., 2024, Modeling the impacts of Glen Canyon Dam operations on Colorado River resources, ii, 133 p.","productDescription":"ii, 133 p.","ipdsId":"IP-163024","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":427772,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/uc/progact/amp/pdfs/LTEMP/20240408-ModelingImpactsGlenCanyonDamOperationsColoradoRiverResources-508-UCRO.pdf"},{"id":427780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Hartwell, Meredith A. 0000-0001-6350-5450 mhartwell@usgs.gov","orcid":"https://orcid.org/0000-0001-6350-5450","contributorId":4842,"corporation":false,"usgs":true,"family":"Hartwell","given":"Meredith","email":"mhartwell@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898821,"contributorType":{"id":2,"text":"Editors"},"rank":14}],"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898808,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bair, Lucas 0000-0002-9911-3624","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":248714,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eppehimer, Drew Elliot 0000-0003-0076-1494","orcid":"https://orcid.org/0000-0003-0076-1494","contributorId":333633,"corporation":false,"usgs":true,"family":"Eppehimer","given":"Drew","email":"","middleInitial":"Elliot","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898810,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Salter, Gerard Lewis 0000-0001-6426-0133","orcid":"https://orcid.org/0000-0001-6426-0133","contributorId":333645,"corporation":false,"usgs":true,"family":"Salter","given":"Gerard","email":"","middleInitial":"Lewis","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898811,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898812,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":898813,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":898814,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898815,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fairley, Helen C. 0000-0001-6151-4804 hfairley@usgs.gov","orcid":"https://orcid.org/0000-0001-6151-4804","contributorId":3040,"corporation":false,"usgs":true,"family":"Fairley","given":"Helen","email":"hfairley@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":898816,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898817,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mihalevich, Bryce Anthony 0000-0001-5492-221X","orcid":"https://orcid.org/0000-0001-5492-221X","contributorId":335493,"corporation":false,"usgs":false,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"Anthony","affiliations":[{"id":80423,"text":"U.S. Bureau of Reclamation, Salt Lake City, UT","active":true,"usgs":false}],"preferred":false,"id":898818,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898819,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898820,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70254074,"text":"70254074 - 2024 - Methane seeps on the U.S. Atlantic margin: An updated inventory and interpretative framework","interactions":[],"lastModifiedDate":"2024-05-06T11:10:25.339469","indexId":"70254074","displayToPublicDate":"2024-04-12T06:08:11","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Methane seeps on the U.S. Atlantic margin: An updated inventory and interpretative framework","docAbstract":"Since the discovery of >570 methane flares on the northern U.S. Atlantic margin between Cape Hatteras and Georges Bank in the last decade, the acquisition of thousands of kilometers of additional water column imaging data has provided greater coverage at water depths between the outer continental shelf and the lower continental slope. The additional high-resolution data reveal >1400 gas flares, but the removal of probable duplicates from the combined database of new flares and those recognized in 2014 yields ∼1139 unique sites. Most of these sites occur in clusters of 5 or more seeps, leaving about 275 unique locations (including 47 clusters) for seepage along the margin. As a function of depth, seep distribution is heavily skewed toward the upper continental slope at water depths shallower than 400 m on the southern New England margin and ∼ 550 m in the Mid-Atlantic Bight, with additional seeps clustered at ∼1100 m and just deeper than ∼1400 m in both sectors. Despite little ongoing tectonic deformation or active faulting on this passive margin, a variety of processes driven from below the seafloor (e.g., migration of fluids along faults or through permeable strata, seepage above diapirs or other pre-existing structures) and from above (e.g., erosion, sapping, unroofing) contribute to the development of seeps in different settings along the margin. In addition, the prevalence of seeps on promontories overlooking shelf-breaking canyons may be directly related to the three-dimensional nature of the hydrate stability zone in these locations. As a function of depth, the parts of the slope at the contemporary landward limit of gas hydrate stability are devoid of seeps, and the upper slope zones with the most concentrated seepage were not within the gas hydrate stability zone even during the Last Glacial Maximum. Thus, if the large number of upper slope seeps is at least partially sourced in gas hydrate degradation, the gas emitted at these seeps must have migrated there from greater depths on the continental slope.
In the U.S. Pacific Northwest, climate change is increasing air temperatures, decreasing warm season (April–September) streamflow, and increasing cool season (October–March) streamflow. Warmer water temperatures may alter conditions for migratory coldwater fishes like the Bull Trout Salvelinus confluentus. Consequently, an understanding of Bull Trout migration and survival is critical for species conservation and restoration. In the Salmon River basin, Idaho, 1992 and 1993 transpired to be two of the most opposing extreme years among the past three decades for warm season water temperature and streamflow. These extremes provided a unique opportunity to retrospectively compare Bull Trout survival and migration under potential climate change scenarios.
We evaluated prespawning and postspawning migrations and survival of fluvial Bull Trout that were radio-tagged and tracked from 1992 to 1994. We used a Cormack–Jolly–Seber linear spatial capture–recapture model to simultaneously model the migration and survival of radio-tagged prespawn (n = 58) and postspawn (n = 23) Bull Trout among weeks and river reaches with streamflow, water temperature, and habitat covariates.
Most individual prespawning migrations were similar among tagged fish, whereas postspawn fish adopted multiple migration and overwintering strategies. Movements of prespawn Bull Trout were larger when (1) weekly average daily maximum streamflow increased and (2) weekly average daily maximum water temperature increased. The model estimated that at least 52% of spawners survived to spawning, and mean weekly prespawning apparent survival was higher in the low-streamflow year (1992) than in the year with higher and more variable streamflow (1993). Survival of 1992–1994 fish during the 38-week postspawning period was intermediate to that in the prespawning period. Detections of prespawn Bull Trout were generally higher at sites with more complex habitats, less large woody debris, and fewer undercut banks.
We found that the prespawn life stage can represent a shorter time frame (14–18 weeks) with increased mortality compared to the longer postspawning period (38 weeks). Bull Trout apparent survival increased with lower streamflow variability, indicating that expected future changes in climate may adversely affect Bull Trout.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10464","usgsCitation":"Wohner, P., Thurow, R.F., and Peterson, J., 2024, Evaluating streamflow and temperature effects on Bull Trout migration and survival with linear spatial capture-recapture models: Transactions of the American Fisheries Society, v. 153, no. 3, p. 326-346, https://doi.org/10.1002/tafs.10464.","productDescription":"21 p.","startPage":"326","endPage":"346","ipdsId":"IP-159745","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Rapid River, Salmon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.24896032398794,\n 46.02582243642945\n ],\n [\n -117.24896032398794,\n 44.29228397972153\n ],\n [\n -115.22929879910345,\n 44.29228397972153\n ],\n [\n -115.22929879910345,\n 46.02582243642945\n ],\n [\n -117.24896032398794,\n 46.02582243642945\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"153","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Wohner, Patti","contributorId":337609,"corporation":false,"usgs":false,"family":"Wohner","given":"Patti","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":902519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thurow, Russell F.","contributorId":21035,"corporation":false,"usgs":true,"family":"Thurow","given":"Russell","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":902520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259306,"text":"70259306 - 2024 - Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022","interactions":[],"lastModifiedDate":"2024-10-03T12:03:47.394201","indexId":"70259306","displayToPublicDate":"2024-04-11T07:01:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022","docAbstract":"Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global-scale spatiotemporal change detection of surface water has significantly improved in recent years due to planetary-scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38 years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas with intermittent seasonal coverage have increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman's rank correlations (rho) above 0.05 and p values less than 0.05. The seasonal river extent is nearly 32 % larger than the previously observed annual mean river extent, suggesting large seasonal variations that impact not only ecosystem health but also estimations of terrestrial biogeochemical cycles of carbon. The outcomes of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, serving as a valuable resource for monitoring and research of hydrological cycles, ecosystem accounting, and water management.
Magnetotelluric (MT) and audiomagnetotelluric (AMT) data are used to better understand the subsurface geology and mineral resources in the San Juan–Silverton caldera complex located near Silverton, Colorado, western United States, as part of the extensive southern Rocky Mountains volcanic field that covers much of southwestern Colorado and northern New Mexico. Seven MT and AMT profiles of varying lengths image resistivity structure to depths of ~5 km. The AMT inversion models characterize geophysical responses of near-surface lithologies, structures, and mineralized systems and also help corroborate airborne electromagnetic data at shallow levels. The MT inversion models extend our depth of investigation from near the surface to great depths (~5 km) and help to form hypotheses about roots of the hydrothermal plumbing that fed shallower mineralized systems. Subsurface high resistivities occur beneath intermediate-composition lava flows and Proterozoic units. Subsurface moderate- to low-resistivity values may reflect hydrothermal plumbing that served as flow paths for mineralizing fluids and metallic ore formation. The model interpretations presented in this study could be utilized in remediation planning or mineral resource applications. The methods used could be applied to other watersheds with similar volcanic environments containing acid-generating historical mines or hydrothermally altered and mineralized source rocks.
Genetic variation in Arctic species is often influenced by vicariance during the Pleistocene, as ice sheets fragmented the landscape and displaced populations to low- and high-latitude refugia. The formation of secondary contact or suture zones during periods of ice sheet retraction has important consequences on genetic diversity by facilitating genetic connectivity between formerly isolated populations. Brant geese (Branta bernicla) are a maritime migratory waterfowl (Anseriformes) species that almost exclusively uses coastal habitats. Within North America, brant geese are characterized by two phenotypically distinct subspecies that utilize disjunct breeding and wintering areas in the northern Pacific and Atlantic. In the Western High Arctic of Canada, brant geese consist of individuals with an intermediate phenotype that are rarely observed nesting outside this region. We examined the genetic structure of brant geese populations from each subspecies and areas consisting of intermediate phenotypes using mitochondrial DNA (mtDNA) control region sequence data and microsatellite loci. We found a strong east–west partition in both marker types consistent with refugial populations. Within subspecies, structure was also observed at mtDNA while microsatellite data suggested the presence of only two distinct genetic clusters. The Western High Arctic (WHA) appears to be a secondary contact zone for both Atlantic and Pacific lineages as mtDNA and nuclear genotypes were assigned to both subspecies, and admixed individuals were observed in this region. The mtDNA sequence data outside WHA suggests no or very restricted intermixing between Atlantic and Pacific wintering populations which is consistent with published banding and telemetry data. Our study indicates that, although brant geese in the WHA are not a genetically distinct lineage, this region may act as a reservoir of genetic diversity and may be an area of high conservation value given the potential of low reproductive output in this species.
Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.
Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate-based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling-Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non-optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions.
The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post-wildfire hydrologic hazards including debris flows, flash floods, water-quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data-sparse, headwater catchments in complex terrain, and post-wildfire hydrologic effects are particularly sensitive to high-intensity, short-duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire-induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade-offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post-wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire-prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1728","usgsCitation":"Partridge, T.F., Johnson, Z., Sleeter, R., Qi, S.L., Walvoord, M.A., Murphy, S.F., Peterman-Phipps, C.L., and Ebel, B., 2024, Opportunities and challenges for precipitation forcing data in post-wildfire hydrologic modeling applications: WIREs Water, v. 11, no. 5, e1728, 27 p., https://doi.org/10.1002/wat2.1728.","productDescription":"e1728, 27 p.","ipdsId":"IP-155206","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":427613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439910,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1728","text":"Publisher Index Page"}],"volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":898395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterman-Phipps, Cara L. 0000-0003-1822-2552","orcid":"https://orcid.org/0000-0003-1822-2552","contributorId":259166,"corporation":false,"usgs":true,"family":"Peterman-Phipps","given":"Cara","email":"","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":898400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70252824,"text":"70252824 - 2024 - Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits","interactions":[],"lastModifiedDate":"2024-04-09T00:04:06.406654","indexId":"70252824","displayToPublicDate":"2024-04-06T11:45:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits","docAbstract":"Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.
","language":"English","publisher":"MDPI","doi":"10.3390/s24072339","usgsCitation":"Thieme, A., Prabhakara, K., Jennewein, J., Lamb, B.T., McCarty, G.T., and Hively, W.D., 2024, Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits: Sensors, v. 24, no. 7, 2339, 25 p., https://doi.org/10.3390/s24072339.","productDescription":"2339, 25 p.","ipdsId":"IP-079899","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":439911,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s24072339","text":"Publisher Index Page"},{"id":427561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","city":"Beltsville","otherGeospatial":"Beltsville Agricultural Research Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.93347699075227,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.01461497846458\n ],\n [\n -76.90338189099843,\n 39.029631686898625\n ],\n [\n -76.93347699075227,\n 39.029631686898625\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prabhakara, Kusuma","contributorId":335445,"corporation":false,"usgs":false,"family":"Prabhakara","given":"Kusuma","affiliations":[{"id":80408,"text":"University of Maryland, Department of Geographic Sciences","active":true,"usgs":false}],"preferred":false,"id":898361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennewein, Jyoti","contributorId":335446,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","email":"","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":898362,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898363,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCarty, Gregory T.","contributorId":335447,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"T.","affiliations":[{"id":65190,"text":"USDA-ARS Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":898364,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898365,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256409,"text":"70256409 - 2024 - Food, water quality, and the growth of a freshwater mussel: Implications for population restoration","interactions":[],"lastModifiedDate":"2024-08-01T15:44:04.975177","indexId":"70256409","displayToPublicDate":"2024-04-05T10:35:32","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Food, water quality, and the growth of a freshwater mussel: Implications for population restoration","docAbstract":"Unknown causes behind the loss of freshwater mussel populations have prompted population restoration as a tool to recover these imperiled species. However, water quality conditions that support mussel species within natural environments and potential causes of water quality impairment in systems with declining populations are typically unknown and may be critical knowledge needed before reintroducing mussels. Our objective was to relate the growth and survival of declining freshwater mussel populations of the Brook Floater Alasmidonta varicosa (Lamarck, 1819) to water quality parameters within 4 Massachusetts, USA, rivers containing extant populations. We deployed propagated age-1 and age-2 Brook Floater in contained systems (silos) for 1 growing season (June–October). Through biweekly sampling, we tracked the growth and survival of mussels then modeled their relationships with water quality variables. Mussels had a higher growth rate at a higher chlorophyll a (Chl a) value (2.82 µg/L) over the temperature range measured (biweekly mean = 16–26°C) when compared with lower Chl a values (0.61 and 1.17 µg/L). Age-1 mussel growth rate was negatively affected by low Chl a concentration (0.61 µg/L) across the temperature range, but age-2 mussel growth rate was not negatively affected until temperatures were above ~22°C. Na+ limited the growth rate of mussels, with the rate of change in growth rate for age-1 mussels greater than for age-2 mussels. Other cations (Mg2+, K+, and Ca2+)—potentially linked to road deicers—also negatively affected growth rate in all 4 rivers but may have had a greater impact on mussels in rivers with reduced growth rates from lower temperatures and Chl a. However, survival was uniformly high across all rivers, indicating water quality parameters may have sublethal but not lethal effects. Additional assessments for chronic water quality stressors along with changing land cover, land management, and climates are important considerations for restoration potential and the long-term persistence of populations.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/730247","usgsCitation":"Skorupa, A., Roy, A.H., Hazelton, P., Perkins, D., Timothy Warren, T., and Cheng, B.S., 2024, Food, water quality, and the growth of a freshwater mussel: Implications for population restoration: Freshwater Science, v. 43, no. 2, p. 107-123, https://doi.org/10.1086/730247.","productDescription":"7 p.","startPage":"107","endPage":"123","ipdsId":"IP-154695","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.92801492613863,\n 42.333950606928596\n ],\n [\n -71.9376914557071,\n 42.33232039224919\n ],\n [\n -73.077586638902,\n 42.32546816564172\n ],\n [\n -73.17435193458931,\n 42.06443189078277\n ],\n [\n -71.94349737344804,\n 42.081670744671186\n ],\n [\n -71.92801492613863,\n 42.333950606928596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.62300789778979,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.70452095814687\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":340492,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelton, Peter D.","contributorId":340493,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, David","contributorId":340494,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Timothy Warren, Timothy","contributorId":340495,"corporation":false,"usgs":false,"family":"Timothy Warren","given":"Timothy","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907297,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Brian S.","contributorId":340496,"corporation":false,"usgs":false,"family":"Cheng","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907298,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70253103,"text":"70253103 - 2024 - Spatial, seasonal, and diel controls of nitrogen-carbon-oxygen cycling during lake-water infiltration to an aquifer","interactions":[],"lastModifiedDate":"2024-12-20T17:35:03.69538","indexId":"70253103","displayToPublicDate":"2024-04-05T07:09:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial, seasonal, and diel controls of nitrogen-carbon-oxygen cycling during lake-water infiltration to an aquifer","docAbstract":"Many freshwater lakes are groundwater flow-through systems. Although lakes commonly are considered to be sinks for nitrogen inputs, relatively little is known about carbon and nitrogen export from lakes to groundwater. The current study focused on lake-bottom biogeochemical processes accompanying the transport of nitrogen, dissolved oxygen (O2), and dissolved organic carbon (DOC) during lake-water recharge from a groundwater flow-through lake. Lake-water and porewater (15–100 cm below lakebed) samples were collected along transects within the lake downwelling zone. Infiltrating porewater O2 and DOC concentrations decreased with depth while nitrate (NO3−) concentrations increased, indicating nitrification of organic matter within the profiles. The depth of NO3− production and transport was seasonally dependent. In winter, NO3− and O2 were exported beyond 100-cm depth; whereas in summer, shallow nitrification zones were underlain by deeper NO3− reduction zones, and diel patterns of O2 and NO3− penetration depths were observed. Microbial community compositions and stable isotope profiles (δ15N[NO3−], δ18O[NO3−], δ18O[O2]) were consistent with apparent C–N–O reaction stoichiometries indicating O2 reduction and nitrification in shallower porewater, followed by varying NO3− reduction at depth. Maximum porewater NO3− concentrations (∼10–20 μM) were limited by infiltrating O2 concentrations and C/N ratios of reacting organic matter. Lake-water level variations caused changes in shoreline position and porewater velocities, while variations in lake-water temperature, DOC, and O2 contributed to changes in reaction rates and depth of O2 and NO3− penetration into the lakebed. The quality of groundwater recharged by lake water reflected temporally and spatially varying physical and biogeochemical processes in the sediment porewater.
Carbon dioxide (CO2) is gaining interest as a tool to combat aquatic invasive species, including zebra mussels (Dreissena polymorpha). However, the effects of water chemistry on CO2 efficacy are not well described. We conducted five trials in which we exposed adult zebra mussels to a range of CO2 in water with adjusted total hardness and specific conductance. We compared dose–responses and found differences in lethal concentration to 50% of organisms (LC50) estimates ranging from 108.3 to 179.3 mg/L CO2 and lethal concentration to 90% of organisms (LC90) estimates ranging from 163.7 to 216.6 mg/L CO2. We modeled LC50 and LC90 estimates with measured water chemistry variables from the trials. We found sodium (Na+) concentration to have the strongest correlation to changes in the LC50 and specific conductance to have the strongest correlation to changes in the LC90. Our results identify water chemistry as an important factor in considering efficacious CO2 concentrations for zebra mussel control. Additional research into the physiological responses of zebra mussels exposed to CO2 may be warranted to further explain mode of action and reported selectivity. Further study could likely develop a robust and relevant model to refine CO2 applications for a wider range of water chemistries. Environ Toxicol Chem 2024;00:1–8. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
An understanding of oceanographic conditions and processes important to marine animal ecology is fundamental to the development of effective management and conservation actions. Longfin Smelt (Spirinchus thaleichthys) is a pelagic forage fish found in coastal and estuarine waters along the Pacific coast of North America from Alaska to central California. Substantial population declines in California’s San Francisco Estuary, where Longfin Smelt are protected under California’s Endangered Species Act, have prompted extensive study of estuarine factors associated with the decline. However, coastal factors that affect up to two-thirds of the Longfin Smelt life cycle are poorly understood and may be important drivers of population dynamics. We compiled coastal observations from numerous sources to estimate the range-wide coastal marine distribution of Longfin Smelt and assess habitat factors affecting distribution in the northeast Pacific Ocean. Based on maximum entropy species distribution models, Longfin Smelt distribution was correlated with depth, distance from the nearest estuary, sea surface temperature, and sea surface chlorophyll. Longfin Smelt were found in shallow, higher productivity coastal waters closer to estuaries, with depth and temperature the most consistent factors influencing distribution. Habitat suitability was highly variable at the southern extent of the range, particularly off the California coast, and was largely driven by habitat contractions associated with warm-water conditions. Study results provide insights into the habitat and range-wide distribution of an at-risk estuarine-reliant forage fish and are the first step toward identifying processes that affect the marine portion of the Longfin Smelt life cycle.
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.
He‘eia and ‘Ioleka‘a Streams in the He‘eia watershed on O‘ahu, Hawai‘i, receive substantial discharge from dike-impounded groundwater. Previous studies indicated that groundwater withdrawals from the watershed affect streamflow. Resource managers and users seek information that can be used to balance the needs of competing uses of groundwater and streamflow in the watershed.
In this study, analyses of historical streamflow and withdrawal data indicate that when groundwater withdrawals from Haiku Tunnel (a groundwater development tunnel built in the 1940s in the watershed) of 1.73–1.87 million gallons per day (Mgal/d) were introduced in the first few decades of the tunnel’s operation, base flow at a gage on He‘eia Stream decreased by 1.37–1.40 Mgal/d. Changes in rainfall during this period were not sufficient to account for the changes in base flow. The tunnel withdrawal also affected ‘Ioleka‘a Stream, but the effect was less. In the 1980s, average withdrawal from the tunnel decreased by 0.73–1.00 Mgal/d and base flow at the He‘eia streamgage increased by 0.15–0.21 Mgal/d; a concurrent rainfall increase may partly account for the base-flow increase. Withdrawal from another well (Haiku well) starting in the late 1980s had a much smaller effect than the tunnel did on flow at the He‘eia streamgage.
Numerical groundwater-model simulations indicate that shutting down withdrawals from Haiku Tunnel and Haiku well would increase base flows in streams inside and outside of the He‘eia watershed. Simulated shutdown of 0.35 Mgal/d withdrawal from Haiku well caused base flow of streams in the He‘eia watershed to increase by 0.09 Mgal/d or 26 percent of the withdrawal reduction, and shutdown of 0.60 Mgal/d withdrawal from Haiku Tunnel caused base flow of streams within the watershed to increase by 0.12 Mgal/d or 20 percent of withdrawal reduction. Shutdown of a combined 0.95 Mgal/d withdrawal from the tunnel and well caused base flow of streams within the watershed to increase by 0.22 Mgal/d or 23 percent of the withdrawal reduction.
The model simulations and analyses of streamflow data demonstrate that, climate changes notwithstanding, reducing or shutting down withdrawal from Haiku Tunnel has not in the past, and will not in the future, restore base flow to predevelopment rates. The nearly pristine condition that existed prior to the construction of the Haiku Tunnel no longer exists because other large-producing tunnels and wells near the He‘eia watershed have since begun withdrawing water from the same dike-impounded aquifer. Reduction or shutdown of withdrawals from the wells and tunnel in the He‘eia watershed cannot restore streamflow to predevelopment rates if withdrawals from all other wells and tunnels continue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245020","collaboration":"Prepared in cooperation with the Honolulu Board of Water Supply","usgsCitation":"Izuka, S.K., Kāne, H.L., and Rotzoll, K., 2024, Groundwater and surface-water interactions in the He‘eia watershed, O‘ahu, Hawai‘i—Insights from analysis of historical data and numerical groundwater-model simulations: U.S. Geological Survey Scientific Investigations Report 2024–5020, 22 p., https://doi.org/10.3133/sir20245020.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-149791","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499449,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116223.htm","linkFileType":{"id":5,"text":"html"}},{"id":427359,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5020/sir20245020.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":427358,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5020/covrthb.jpg"},{"id":427357,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91JM5FZ","text":"USGS Data Release","description":"Rotzoll, K., 2024, MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P91JM5FZ.","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii"}],"country":"United States","state":"Hawaii","otherGeospatial":"He‘eia Watershed, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.841667,\n 21.441667\n ],\n [\n -157.841667,\n 21.391667\n ],\n [\n -157.791667,\n 21.391667\n ],\n [\n -157.791667,\n 21.441667\n ],\n [\n -157.841667,\n 21.441667\n ]\n ]\n ],\n \"type\": \"Polygon\"\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