A cellular automata model was developed and parameterized to test the effectiveness of application of biological control insects to water hyacinth (Pontederia crassipes), which is an invasive floating plant species in many parts of the world and outcompetes many submersed native aquatic species in southern Florida. In the model, P. crassipes was allowed to compete with Nuttall’s waterweed (Elodea nuttallii). In the absence of biocontrol acting on the P. crassipes, E. nuttallii excluded P. crassipes at low concentrations of the limiting nutrient (nitrogen), and the reverse occurred at high nutrient concentrations. At intermediate values, alternative stable states could occur; either P. crassipes alone or a mixture of the two species. When the biocontrol agent, the weevil Neochetina eichhorniae, was applied in the model, there was initially a rapid reduction of the P. crassipes, however, over time a regular striped pattern of moving spatially alternating stripes of P. crassipes and E. nuttallii emerged. -This pattern of moving stripes emerged and persisted over thousands of days but could quickly transform into an irregular pattern at some apparently random time, when either external stochasticity (added adult weevils) or only the weak internal stochasticity of weevil movements occurred. The cause of the end of the long transient can be traced to a single slightly irregular pixel within the striped pattern. Model parameters were varied to study effects of plant growth rate, nutrient concentration and nutrient diffusion rate on the dynamics of the system.
Understanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low-flow events (5- to 100-year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate-driven trends in extreme low flows (1976–2015 and 1946–2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low-flow regimes using air temperature and monthly low-flow frequency. Trends in the annual occurrence rate of extreme low-flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (p < 0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold-season low-flow regime in North America were found to be significantly decreasing for low return-period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low-flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold- and warm-season low-flow regimes; there were also some non-significant trends. The difference in the results between the shorter (40-year) and longer (70-year) records and between low-flow regimes highlights the complexities of low-flow response to changing climatic conditions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR034326","usgsCitation":"Hodgkins, G.A., Renard, B., Whitfield, P.H., Laaha, G., Stahl, K., Hannaford, J., Burn, D.H., Westra, S., Fleig, A.K., Lopes, W.T., Murphy, C., Mediero, L., and Hanel, M., 2024, Climate driven trends in historical extreme low streamflows on four continents: Water Resources Research, v. 60, no. 6, e2022WR034326, 25 p., https://doi.org/10.1029/2022WR034326.","productDescription":"e2022WR034326, 25 p.","ipdsId":"IP-147345","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":466994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr034326","text":"Publisher Index Page"},{"id":463765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":918098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitfield, Paul H.","contributorId":198041,"corporation":false,"usgs":false,"family":"Whitfield","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":918099,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laaha, Gregor","contributorId":335609,"corporation":false,"usgs":false,"family":"Laaha","given":"Gregor","email":"","affiliations":[{"id":80445,"text":"University of Natural Resources and Life Sciences, Austria","active":true,"usgs":false}],"preferred":false,"id":918100,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stahl, Kerstin","contributorId":198044,"corporation":false,"usgs":false,"family":"Stahl","given":"Kerstin","email":"","affiliations":[],"preferred":false,"id":918101,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hannaford, Jamie","contributorId":198043,"corporation":false,"usgs":false,"family":"Hannaford","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":918102,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burn, Donald H.","contributorId":198042,"corporation":false,"usgs":false,"family":"Burn","given":"Donald","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":918103,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Westra, Seth","contributorId":335610,"corporation":false,"usgs":false,"family":"Westra","given":"Seth","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":918104,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fleig, Anne K.","contributorId":198045,"corporation":false,"usgs":false,"family":"Fleig","given":"Anne","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":918105,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lopes, Walsczon Terllizzie Araujo","contributorId":335611,"corporation":false,"usgs":false,"family":"Lopes","given":"Walsczon","email":"","middleInitial":"Terllizzie Araujo","affiliations":[{"id":80446,"text":"National Water and Sanitation Agency, Brazil","active":true,"usgs":false}],"preferred":false,"id":918106,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Murphy, Conor","contributorId":198049,"corporation":false,"usgs":false,"family":"Murphy","given":"Conor","email":"","affiliations":[],"preferred":false,"id":918108,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mediero, Luis","contributorId":198047,"corporation":false,"usgs":false,"family":"Mediero","given":"Luis","email":"","affiliations":[],"preferred":false,"id":918109,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hanel, Martin","contributorId":346109,"corporation":false,"usgs":false,"family":"Hanel","given":"Martin","email":"","affiliations":[],"preferred":false,"id":918115,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70255606,"text":"70255606 - 2024 - Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling","interactions":[],"lastModifiedDate":"2024-06-26T13:38:24.914144","indexId":"70255606","displayToPublicDate":"2024-06-17T08:36:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling","docAbstract":"Foragers on patchy landscapes must efficiently balance time between searching for and consuming resources to meet their daily energetic requirements. Spatial aggregation foraging behaviors may improve foraging efficiency by sharing information on locations of resource hotspots. Wading birds are an example of patch foragers that form colonial aggregations during the breeding season to obtain sufficient prey energy to sustain themselves and their offspring each day. Here, we describe a spatially-explicit simulation model of wading bird optimal foraging that represents information sharing through visual cues. The overall purpose of the model is to describe how wading bird daily foraging and reproductive success may change with alternative water control management practices that determine spatial availability of prey for wading birds on the landscape, throughout their breeding seasons. Wading birds are simulated as individuals that operate independently, sampling and selecting among patches based on a prey density tolerance threshold, but also use information from other birds to inform their selection decisions. Foraging success is evaluated against the fundamental objectives of (a) fulfilling daily energetic demands and (b) minimizing predation exposure, by tracking individual daily energetic intake and time spent foraging. In this way, the model approximates population level dynamics of wading bird aggregations that emerge through collective decision making of birds simulated at the lower individual level. Key results of this study suggest that aggregation behaviors may improve population-level foraging success rates, and the optimal settling threshold may modulate when resources become more scarce or difficult to find. Thus, the model addresses ecological theory on the advantages of foraging in groups versus independently. This technique is appropriate for evaluating wading bird populations that forage on patchy landscapes, such as seasonally-pulsed wetlands, wherever sufficient information is available to describe (1) foraging behavior (e.g., feeding rate, flight speeds, patch selection decisions), (2) key landscape characteristics, (3) spatial distributions of prey densities among foraging patches, and (4) changes in prey densities through time. The model was designed to predict qualitative, testable spatial patterns of wading bird foraging movements which can be compared with empirical observations and empirically-derived habitat suitability models. These techniques can also be applied to other bird species, such as shorebirds, or more generally to any species that transits between discrete foraging patches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110702","usgsCitation":"Yurek, S., DeAngelis, D.L., Lee, H.W., and Tennenbaum, S., 2024, Visualizing wading bird optimal foraging decisions with aggregation behaviors using individual-based modeling: Ecological Modelling, v. 493, 110702, 15 p., https://doi.org/10.1016/j.ecolmodel.2024.110702.","productDescription":"110702, 15 p.","ipdsId":"IP-153166","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110702","text":"Publisher Index Page"},{"id":430522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"493","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216738,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":904925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":904926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Hyo Won","contributorId":292184,"corporation":false,"usgs":false,"family":"Lee","given":"Hyo","email":"","middleInitial":"Won","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":904927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tennenbaum, Stephen","contributorId":292180,"corporation":false,"usgs":false,"family":"Tennenbaum","given":"Stephen","email":"","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":904928,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064B","publicationYear":"2024","noYear":false,"chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2024-06-17T22:21:15.873668","indexId":"sir20235064B","displayToPublicDate":"2024-06-17T07:11:12","publicationYear":"2024","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":"2023-5064","chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","docAbstract":"This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. 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; 30 years 1991–2020) for trends, change points, and other statistical properties indicative of changing conditions. Median peak streamflows generally exhibit upward trends across the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 23-percent increase during the 30-year trend period to a 41-percent increase during the 100-year trend period. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. More than two-thirds of streamgages at the 100- and 75-year trend periods exhibit a trend and change point in median peak streamflow in the same direction. Temporally, clusters of change points are observed in the late 1960s through early 1980s for the 100- and 75-year trend periods and around 2006 for the 50- and 30-year trend periods. Trends in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, increased about the same amount as the 50-percent quantile peak streamflows, except at the 100-year trend period, where the 50-percent quantile peak flow increased more for almost all streamgages. The frequency of high flows has also increased in Illinois, with increases in peaks-over-threshold observed across much of the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with clusters of likely upward trends observed in northern and southern Illinois at the 50- and 30-year trend periods. As expected in response to increasing temperature, the modeled proportion of precipitation falling as snow has largely decreased in the study basins across the State, and modeled potential evapotranspiration has increased. Upward trends in modeled annual runoff, which in this report incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation.
The widespread upward trends in the magnitude of median peak streamflows and the frequency with which high flows occur across the State at the 100- and 75-year trend periods and in northern and southern Illinois at the 50- and 30-year trend periods appear to be driven largely by increases in precipitation based on spatial patterns of these changes and statistical relations between streamflow and climate metrics. Other effects not considered in this report, like urbanization, may be important drivers for certain streamgages in the State.
The prevalence of nonstationarity in peak streamflow in Illinois has important implications for peak-flow frequency analysis. Average annual 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.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long-term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro-geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non-dendritic, trellis-like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro-geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long-term landscape evolution.
The eruption of Hunga volcano on 15 January 2022 produced a higher plume and faster-growing umbrella cloud than has ever been previously recorded. The plume height exceeded 58 km, and the umbrella grew to 450 km in diameter within 50 min. Assuming an umbrella thickness of 10 km, this growth rate implied an average volume injection rate into the umbrella of 330–500 km3 s−1. Conventional relationships between plume height, umbrella-growth rate, and mass eruption rate suggest that this period of activity should have injected a few to several cubic kilometers of rock particles (tephra) into the plume. Yet tephra fall deposits on neighboring islands are only a few centimeters thick and can be reproduced using ash transport simulations with only 0.1–0.2 km3 erupted volume (dense-rock equivalent). How could such a powerful eruption contain so little tephra? Here, we propose that seawater mixing at the vent boosted the plume height and umbrella growth rate. Using the one-dimensional (1-D) steady plume model Plumeria, we find that a plume fed by ~90% water vapor at a temperature of 100 °C (referred to here as steam) could have exceeded 50 km height while keeping the injection rate of solids low enough to be consistent with Hunga’s modest tephra-fall deposit volume. Steam is envisaged to rise from intense phreatomagmatic jets or pyroclastic density currents entering the ocean. Overall, the height and expansion rate of Hunga’s giant plume is consistent with the total mass of fall deposits plus underwater density current deposits, even though most of the erupted mass decoupled from the high plume. This example represents a class of high (> 10 km), ash-poor, steam-driven plumes, that also includes Kīlauea (2020) and Fukutoku-oka-no-ba (2021). Their height is driven by heat flux following well-established relations; however, most of the heat is contained in steam rather than particles. As a result, the heights of these water-rich plumes do not follow well-known relations with the mass eruption rate of tephra.
Understanding genetic structure and diversity among remnant populations of rare species can inform conservation and recovery actions. We used a population genetic framework to spatially delineate gene pools and estimate gene flow and effective population sizes for the endangered California Freshwater Shrimp Syncaris pacifica. Tissues of 101 individuals were collected from 11 sites in 5 watersheds, using non-lethal tissue sampling. Single Nucleotide Polymorphism markers were developed de novo using ddRAD-seq methods, resulting in 433 unlinked loci scored with high confidence and low missing data. We found evidence for strong genetic structure across the species range. Two hierarchical levels of significant differentiation were observed: (i) five clusters (regional gene pools, FST = 0.38–0.75) isolated by low gene flow were associated with watershed limits and (ii) modest local structure among tributaries within a watershed that are not connected through direct downstream flow (local gene pools, FST = 0.06–0.10). Sampling sites connected with direct upstream-to-downstream water flow were not differentiated. Our analyses suggest that regional watersheds are isolated from one another, with very limited (possibly no) gene flow over recent generations. This isolation is paired with small effective population sizes across regional gene pools (Ne = 62.4–147.1). Genetic diversity was variable across sites and watersheds (He = 0.09–0.22). Those with the highest diversity may have been refugia and are now potential sources of genetic diversity for other populations. These findings highlight which portions of the species range may be most vulnerable to future habitat fragmentation and provide management consideration for maintaining local effective population sizes and genetic connectivity.
Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.
Preventing the spread of aquatic invasive species is an important management action. Identifying the characteristics of lakes that are susceptible to invasion creates an opportunity for management groups to prioritize limited resources for high-risk areas. In this study, we leveraged big data from a popular fishing app and other publicly available sources of environmental and human-use exposure measurements to develop machine learning models to predict aquatic invasive species presence in 30,375 lakes in the upper Mississippi river basin of the United States. Our results predicted that an additional 665, 771, 544, 703, and 638 lakes in the basin are invaded or at high risk of invasion by Eurasian watermilfoil, curly-leaf pondweed, rusty crayfish, Chinese mystery snail, and dreissenid mussels, respectively. Lake invasions were predicted by a combination of environmental, human-use exposure, and community dynamics variables. Features that made a lake more attractive to recreationists were consistently important across our models including the presence of a boat ramp, larger lake size, and surrounding natural landscape. The importance of co-occurring invasive species in some models could reflect several scenarios including invasional meltdown, facilitation among species, similar pathways for introduction, or similar response to the environment. Our models predicted a higher proportion of invasions in less popular lakes compared to known invasions. The finding underscores the potential importance of less popular lakes in the invasion process and suggests that the detection of invasions may be lower in these lakes. These results serve as a valuable tool for data-driven management decisions and can provide actionable insights for effective aquatic invasive species management.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-024-03367-6","usgsCitation":"Weir, J.L., Daniel, W., Hyder, K., Skov, C., and Venturelli, P.A., 2024, Artificial intelligence applied to big data reveals that lake invasions are predicted by human traffic and co-occurring invasions: Biological Invasions, v. 26, p. 3163-3178, https://doi.org/10.1007/s10530-024-03367-6.","productDescription":"16 p.","startPage":"3163","endPage":"3178","ipdsId":"IP-162115","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":432144,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Weir, Jessica L.","contributorId":330438,"corporation":false,"usgs":false,"family":"Weir","given":"Jessica","email":"","middleInitial":"L.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":908933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daniel, Wesley 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219312,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":908934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hyder, Kieran","contributorId":291284,"corporation":false,"usgs":false,"family":"Hyder","given":"Kieran","email":"","affiliations":[{"id":62658,"text":"The Centre for Environment, Fisheries and Aquaculture Science (Cefas) and Collaborative Centre for Sustainable Use of the Seas (CCSUS), School of Environmental Sciences, University of East Anglia, Norwich Research Park","active":true,"usgs":false}],"preferred":false,"id":908935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skov, Christian","contributorId":268055,"corporation":false,"usgs":false,"family":"Skov","given":"Christian","email":"","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":908936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Venturelli, Paul A.","contributorId":171477,"corporation":false,"usgs":false,"family":"Venturelli","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908937,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256119,"text":"70256119 - 2024 - Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","interactions":[],"lastModifiedDate":"2024-07-23T20:23:11.322287","indexId":"70256119","displayToPublicDate":"2024-06-15T09:11:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model","docAbstract":"Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples. The variability in egg ages in virtual samples compare well to field samples. The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 kilometers upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102376","usgsCitation":"Soong, D., Jackson, P.R., Kocovsky, P.M., Morrison, L., Garcia, T., Santacruz, S., Chen, C., Zhu, Z., and Embke, H.S., 2024, Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model: Journal of Great Lakes Research, v. 50, no. 4, 102376, 14 p., https://doi.org/10.1016/j.jglr.2024.102376.","productDescription":"102376, 14 p.","ipdsId":"IP-157787","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439399,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102376","text":"Publisher Index Page"},{"id":431354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Sandusky River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83,\n 41.5\n ],\n [\n -83.25,\n 41.5\n ],\n [\n -83.25,\n 41.25\n ],\n [\n -83,\n 41.25\n ],\n [\n -83,\n 41.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soong, David 0000-0003-0404-2163","orcid":"https://orcid.org/0000-0003-0404-2163","contributorId":206523,"corporation":false,"usgs":true,"family":"Soong","given":"David","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906761,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":906762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morrison, Lori","contributorId":340259,"corporation":false,"usgs":false,"family":"Morrison","given":"Lori","email":"","affiliations":[{"id":81526,"text":"Alaska Water Resources","active":true,"usgs":false}],"preferred":false,"id":906763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garcia, Tatiana","contributorId":340260,"corporation":false,"usgs":false,"family":"Garcia","given":"Tatiana","affiliations":[{"id":81527,"text":"AquaIntel Inc.","active":true,"usgs":false}],"preferred":false,"id":906764,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santacruz, Santiago","contributorId":340261,"corporation":false,"usgs":false,"family":"Santacruz","given":"Santiago","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":906765,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Cindy","contributorId":340262,"corporation":false,"usgs":false,"family":"Chen","given":"Cindy","email":"","affiliations":[{"id":12537,"text":"USACE","active":true,"usgs":false}],"preferred":false,"id":906766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhu, Zhenduo","contributorId":340263,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhenduo","affiliations":[{"id":81528,"text":"Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":906767,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":906768,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70260439,"text":"70260439 - 2024 - Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","interactions":[],"lastModifiedDate":"2024-11-01T13:35:38.261218","indexId":"70260439","displayToPublicDate":"2024-06-15T08:26:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations","docAbstract":"According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m*. We further identify site-specific m* and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007875","usgsCitation":"Yi, K., Novick, K.A., Zhang, Q., Wang, L., Hwang, T., Yang, X., Mallick, K., Beland, M., Senay, G.B., and Baldocchi, D., 2024, Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations: Journal of Geophysical Research Biogeosciences, v. 129, no. 6, e2023JG007875, 19 p., https://doi.org/10.1029/2023JG007875.","productDescription":"e2023JG007875, 19 p.","ipdsId":"IP-163083","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007875","text":"Publisher Index Page"},{"id":463530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Yi, Koong","contributorId":345841,"corporation":false,"usgs":false,"family":"Yi","given":"Koong","email":"","affiliations":[{"id":82725,"text":"Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, CA, U.S.A","active":true,"usgs":false}],"preferred":false,"id":917685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Novick, Kimberly A.","contributorId":196379,"corporation":false,"usgs":false,"family":"Novick","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":917686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Quan","contributorId":345842,"corporation":false,"usgs":false,"family":"Zhang","given":"Quan","email":"","affiliations":[{"id":82726,"text":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China.","active":true,"usgs":false}],"preferred":false,"id":917687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Lixin","contributorId":300466,"corporation":false,"usgs":false,"family":"Wang","given":"Lixin","affiliations":[{"id":65165,"text":"Department of Earth Sciences, Indiana University–Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.","active":true,"usgs":false}],"preferred":false,"id":917688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Taehee","contributorId":345843,"corporation":false,"usgs":false,"family":"Hwang","given":"Taehee","email":"","affiliations":[{"id":82727,"text":"Department of Geography, Indiana University Bloomington, Bloomington, IN, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":917690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallick, Kanishka","contributorId":345844,"corporation":false,"usgs":false,"family":"Mallick","given":"Kanishka","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beland, Martin","contributorId":345845,"corporation":false,"usgs":false,"family":"Beland","given":"Martin","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":917693,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":917694,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70254922,"text":"sir20245027 - 2024 - Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York","interactions":[],"lastModifiedDate":"2026-02-03T18:16:49.214434","indexId":"sir20245027","displayToPublicDate":"2024-06-14T15:39:00","publicationYear":"2024","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":"2024-5027","displayTitle":"Accuracy Assessment of Three-Dimensional Point Cloud Data Collected With a Scanning Total Station on Shinnecock Nation Tribal Lands in Suffolk County, New York","title":"Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York","docAbstract":"A combined point cloud of about 85.6 million points was collected during 27 scans of a section of the western shoreline along the Shinnecock Peninsula of Suffolk County, New York, to document baseline geospatial conditions during July and October 2022 using a scanning total station. The three-dimensional accuracy of the combined point cloud is assessed to identify potential systematic error sources associated with the surveying equipment and the novel methodology used to collect and field-register (data are oriented and aligned in real time) point cloud data. The accuracy of the combined point cloud was assessed in terms of relative and absolute reference frames. Relative accuracy provides a measure of error within the local coordinate system and is determined by combining the uncertainty associated with the position of the scan station (the point being occupied by the scanning total station during the scan), the uncertainty associated with the position of the network control points, and the uncertainty associated with the laser of the scanning total station. Assessment of the absolute accuracy includes these three potential error sources combined with the uncertainty associated with the geodetic coordinates to which the local control network is referenced. The combined overall relative horizontal and vertical accuracy of the point cloud is 0.0156 and 0.0241 meter, respectively, at the 95 percent confidence level. The combined overall absolute horizontal and vertical accuracy of the point cloud is 0.0598 and 0.0733 meter, respectively, at the 95 percent confidence level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245027","collaboration":"Prepared in cooperation with the Shinnecock Nation and the Federal Emergency Management Agency","usgsCitation":"Noll, M.L., Capurso, W.D., and Chu, A., 2024, Accuracy assessment of three-dimensional point cloud data collected with a scanning total station on Shinnecock Nation Tribal lands in Suffolk County, New York: U.S. Geological Survey Scientific Investigations Report 2024–5027, 23 p., https://doi.org/10.3133/sir20245027.","productDescription":"Report: vii, 23 p.; Data Release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-153251","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":429787,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5027/sir20245027.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5027 XML"},{"id":429784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5027/coverthb.jpg"},{"id":429785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5027/sir20245027.pdf","text":"Report","size":"14.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5027 PDF"},{"id":429786,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245027/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5027 HTML"},{"id":429788,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/"},{"id":429789,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OG0AAO","text":"USGS data release","linkHelpText":"Three-dimensional point cloud data collected with a scanning total station on the western shoreline of the Shinnecock Nation Tribal lands, Suffolk County, New York, 2022"},{"id":429861,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig02.png","text":"Figure 2","size":"3.20 MB","linkHelpText":"- Map showing the study area where three-dimensional point cloud data were collected with a scanning total station along the western shoreline of the Shinnecock Peninsula in Suffolk County, New York, for a point cloud accuracy assessment"},{"id":429862,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig03.png","text":"Figure 3","size":"3.25 MB","linkHelpText":"- Map showing estimated position of the shoreline after sea-level rise of about 0.46 meter (m) within the study area on the Shinnecock Nation Tribal lands in Suffolk County, New York, using a conservative model projection for 2050"},{"id":429863,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2024/5027/images/sir20245027_fig04.png","text":"Figure 4","size":"1.76 MB","linkHelpText":"- Graphical representation of the point cloud of A, the study area in plan view, B, the coastal spit in plan view, and C, the dune adjacent to the Tribal cemetery on the Shinnecock Nation Tribal lands in Suffolk County, New York, in section view in July 2022"},{"id":499455,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117076.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Suffolk County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.67660903513142,\n 41.00419828031659\n ],\n [\n -72.67660903513142,\n 40.789306473711775\n ],\n [\n -72.23164172630058,\n 40.789306473711775\n ],\n [\n -72.23164172630058,\n 41.00419828031659\n ],\n [\n -72.67660903513142,\n 41.00419828031659\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Water availability is an important issue in the Black Mesa area because of the arid climate, past industrial water use, and continued water requirements for municipal use by a growing population. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year, depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents the results of data collected as part of the monitoring program in the Black Mesa area from calendar years 2020–2021 and, additionally, uses streamflow statistics from November and December 2019. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In calendar year 2020, total groundwater withdrawals were estimated to be 2,680 acre-feet (acre-ft), and, in 2021, total withdrawals were estimated to be 2,570 acre-ft. Total withdrawals during 2021 were about 65 percent less than total withdrawals in 2005 because the Peabody Western Coal Company discontinued its use of water to transport coal in a coal slurry pipeline after 2005 and ceased mining operations in 2019.
Owing to Navajo Nation and Hopi Reservation access restrictions during the Coronavirus pandemic, water levels were not collected from municipal wells in 2020 or 2021. Water levels measured in 2021 from wells completed in the unconfined areas of the N aquifer within the Black Mesa area showed a decline in 7 of 13 wells when compared with water levels from the prestress period (prior to 1965). The changes in water levels across all 13 wells ranged from +8.4 feet (ft) to −42.4 ft, and the median change was −0.4 ft. Water levels also showed decline in 11 of 12 wells measured in the confined area of the aquifer when compared to the prestress period. The median change for the confined area of the aquifer was −25.9 ft, with changes across all 12 wells ranging from +17.3 ft to −133.7 ft.
Spring flow was measured at four springs between 2020 and 2021. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso, Arizona. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s, and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976–2021), Dinnebito Wash near Sand Springs 09401110 (1993–2020), Polacca Wash near Second Mesa 09400568 (1994–2020), and Pasture Canyon Springs 09401265 (2004–2021). Median winter flows (November through February) of each winter were used as an estimate of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Polacca Wash and Pasture Canyon Springs, whereas a decreasing trend was observed at Moenkopi Wash and Dinnebito Wash.
In 2020 and 2021, water samples were collected from a total of four springs in the Black Mesa area and analyzed for selected chemical constituents. Results from the four springs were compared with previous analyses from the same springs. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. However, concentrations of chloride from Pasture Canyon Spring show a diminishing trend. Concentrations of dissolved solids, chloride, and sulfate at Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Concentrations of dissolved solids at Burro Spring have varied for the period of record, but there is no statistical trend in the data. However, concentrations of chloride and sulfate from Burro Spring show a trend towards lower concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241019","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2024, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2019–2021: U.S. Geological Survey Open-File Report 2024–1019, 47 p., https://doi.org/10.3133/ofr20241019.","productDescription":"vii, 48 p.","onlineOnly":"Y","ipdsId":"IP-148316","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":430241,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1019/images"},{"id":430240,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1019/ofr20241019.xml"},{"id":430239,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1019/ofr20241019.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":430238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1019/covrthb.jpg"},{"id":430242,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241019/full"},{"id":499245,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117071.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.50769351138865,\n 36.993810314532595\n ],\n [\n -111.50769351138865,\n 35.29946810356502\n ],\n [\n -109.33240054263857,\n 35.29946810356502\n ],\n [\n -109.33240054263857,\n 36.993810314532595\n ],\n [\n -111.50769351138865,\n 36.993810314532595\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Coal mine drainage (CMD) and associated metal-rich precipitates have recently been proposed as unconventional sources of rare earth elements (REEs). However, the potential occurrence of radium (Ra), a known carcinogen, with the REE-bearing phases has not been investigated. We hypothesized that Ra may occur in solids that are precipitated from CMD as a “radiobarite” solid solution ((Ba,Sr,Ra)SO4) and/or adsorbed with hydrous metal oxides. REEs have been documented to sorb or co-precipitate with iron (Fe), manganese (Mn), and aluminum (Al) oxyhydroxide in CMD solids. Likewise, Ra has been documented to sorb to hydrous Fe and Mn oxides especially where sulfate (SO4) and/or barium (Ba) concentrations are insufficient to precipitate radiobarite. Thus, we conducted the first-ever survey of Ra concentrations in corresponding CMD water and solid samples in the United States. Samples were analyzed from 4 untreated and 9 treated CMD sites in both the bituminous and anthracite coal regions of Pennsylvania across a range of pH and SO4 concentrations. The dissolved Ra in CMD was relatively low (<0.5 Bq/L), consistent with radiobarite solubility; however, CMD solids were largely composed of amorphous Fe, Al, and Mn oxyhydroxide and silicate minerals. Ra was associated with Mn-enriched CMD solids, upwards of 875 Bq/kg. Total REE + yttrium (Y) content in the CMD solids was enriched upwards of 3600 mg/kg and was significantly correlated with Al content. These preliminary results suggest that REE extraction may target Al-rich solids to avoid Ra in Mn-rich solids.
The U.S. Geological Survey Ohio Water Microbiology Laboratory is a part of the Ohio-Kentucky-Indiana Water Science Center. The mission of the laboratory is to provide microbiological data of public health significance from surface waters, groundwaters, and sediments for a variety of study objectives. The laboratory conducts internal projects, works with external cooperators, and assists U.S. Geological Survey offices and National programs. The laboratory offers guidance, study design, and data interpretation expertise to collaborators, all following rigorous quality control and quality assurance procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243004","usgsCitation":"Lanier, B.M., Brady, A.M.G., Cicale, J.R., Kephart, C.M., Lynch, L.D., Schroeder, M.W., and Stelzer, E.A., 2024, The U.S. Geological Survey Ohio Water Microbiology Laboratory: U.S. Geological Survey Fact Sheet 2024–3004, 4 p., https://doi.org/10.3133/fs20243004.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158056","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":429504,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3004/images/"},{"id":429503,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3004/fs20243004.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3004 XML"},{"id":429502,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243004/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3004 HTML"},{"id":429501,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3004/fs20243004.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3004 PDF"},{"id":429500,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3004/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center,
Ohio Water Microbiology Laboratory
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Laboratory email: gs-w-ohclb_owml@usgs.gov
","tableOfContents":"Conservation translocations may be a useful tool for the restoration of declining freshwater invertebrates, but they are poorly represented in the literature. We conducted a before-after/control-impact (BACI) experiment to test the efficacy of conservation translocation for re-establishing abundant populations of the amphipod Gammarus lacustris, a declining species and wildlife food resource in depressional wetlands in the upper Midwest of the United States of America. Each study site (n = 19) contained at least one treatment wetland receiving translocated G. lacustris from a local donor and one control wetland. We selected study wetlands based on a suite of wetland characteristics and randomly assigned recipient versus control treatment. Gammarus lacustris was detected post-translocation at only 2 of 22 recipient wetlands (1 of 19 sites). Overall, there was a statistical increase in G. lacustris density in recipient wetlands compared to controls; however, the results were of minimal biological significance due to being driven by a single site with low G. lacustris densities. Accordingly, our results suggest that future conservation translocations of amphipods might be successful if limited to recently restored wetlands or informed by a more complex habitat suitability model to differentiate dispersal limitations from habitat limitations. To develop such a model would involve identifying the fewest, most influential physical and biological factors (e.g. wetland size/structure, fish, aquatic vegetation, and water chemistry) from the numerous inter-related factors that correlate with the abundance of naturally occurring G. lacustris; candidate wetlands to receive amphipods would be those for which the model predicts abundant G. lacustris but in which they do not presently occur.
Drought is one of the key drivers of food insecurity in Afghanistan, which is among the most food insecure countries in the world. In this study, we build on previous research and seek to answer the central question: “What is the influence of El Niño-Southern Oscillation (ENSO) on drought outlooks and agricultural yield outcome in Afghanistan, and how do these influences vary spatially?” We do so by utilizing multiple indicators of droughts and available wheat yield reports. We find a clear distinction in the probability of drought (defined as being in the lower tercile) in Afghanistan during La Niña compared to El Niño events since 1981. The probability of drought in Afghanistan increased during La Niña, particularly in the North, Northeast, and West regions. La Niña events are related to an increase in the probability of snow drought, particularly in parts of the Amu Darya basin. It is found that relative to El Niño events, snow water equivalent [total runoff] during La Niña events January–March (March–July total runoff) decreases between 9% and 30% (28%–42%) for the five major basins in the country. The probability of agricultural drought during La Niña events is found to be higher than 70% in the rainfed and irrigated areas of the Northeast, North, and West regions. This result is at least partly supported by reported wheat yield composites related to La Niña events that tend to be lower than for El Niño events across all regions in the case of rainfed wheat (statistically significant in Northeast, West, and South regions) and in some cases for irrigated wheat. The results of this study have direct implications for improving early warning of worsening food insecurity in Afghanistan during La Niña events, given that we now have long-lead and skillful forecasts of ENSO up to 18–24 months in advance, which could potentially be used to provide earlier warning of worsening food insecurity in Afghanistan
","language":"English","publisher":"Elsevier","doi":"10.1016/j.wace.2024.100697","usgsCitation":"Shukla, S., Zaheer, F., Hoell, A., Anderson, W., Jayanthi, H., Husak, G., Lee, D., Barker, B., Pervez, S., Slinski, K., Justice, C., Rowland, J., McNally, A., Budde, M., and Verdin, J., 2024, ENSO-based outlook of droughts and agricultural outcomes in Afghanistan: Weather and Climate Extremes, v. 45, 100697, 16 p., https://doi.org/10.1016/j.wace.2024.100697.","productDescription":"100697, 16 p.","ipdsId":"IP-155371","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493783,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.wace.2024.100697","text":"Publisher Index Page"},{"id":493562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Afghanistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[61.21082,35.65007],[62.23065,35.27066],[62.98466,35.40404],[63.19354,35.85717],[63.9829,36.00796],[64.54648,36.31207],[64.74611,37.11182],[65.58895,37.30522],[65.74563,37.66116],[66.21738,37.39379],[66.51861,37.36278],[67.07578,37.35614],[67.83,37.14499],[68.13556,37.02312],[68.85945,37.34434],[69.19627,37.15114],[69.51879,37.609],[70.11658,37.58822],[70.27057,37.73516],[70.3763,38.1384],[70.80682,38.48628],[71.34813,38.25891],[71.2394,37.95327],[71.54192,37.90577],[71.44869,37.06564],[71.84464,36.73817],[72.19304,36.94829],[72.63689,37.04756],[73.26006,37.49526],[73.9487,37.42157],[74.98,37.41999],[75.15803,37.13303],[74.57589,37.02084],[74.06755,36.83618],[72.92002,36.72001],[71.84629,36.50994],[71.26235,36.07439],[71.49877,35.65056],[71.61308,35.1532],[71.11502,34.73313],[71.15677,34.34891],[70.8818,33.98886],[69.93054,34.02012],[70.32359,33.35853],[69.68715,33.1055],[69.26252,32.50194],[69.31776,31.90141],[68.92668,31.62019],[68.55693,31.71331],[67.79269,31.58293],[67.68339,31.30315],[66.93889,31.30491],[66.38146,30.7389],[66.34647,29.88794],[65.04686,29.47218],[64.35042,29.56003],[64.148,29.34082],[63.55026,29.46833],[62.54986,29.31857],[60.87425,29.82924],[61.78122,30.73585],[61.69931,31.37951],[60.94194,31.54807],[60.86365,32.18292],[60.53608,32.98127],[60.9637,33.52883],[60.52843,33.67645],[60.80319,34.4041],[61.21082,35.65007]]]},\"properties\":{\"name\":\"Afghanistan\"}}]}","volume":"45","noUsgsAuthors":false,"publicationDate":"2024-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Shukla, Shraddhanand","contributorId":140735,"corporation":false,"usgs":false,"family":"Shukla","given":"Shraddhanand","email":"","affiliations":[{"id":13549,"text":"UC Santa Barbara Climate Hazards Group","active":true,"usgs":false}],"preferred":false,"id":944802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zaheer, Fahim","contributorId":359036,"corporation":false,"usgs":false,"family":"Zaheer","given":"Fahim","affiliations":[{"id":81109,"text":"University of California-Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoell, Andrew","contributorId":337032,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":944804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Weston","contributorId":353902,"corporation":false,"usgs":false,"family":"Anderson","given":"Weston","affiliations":[{"id":84523,"text":"NASA Goddard Space Flight Center, Greenbelt, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":944805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jayanthi, Harikishan","contributorId":331304,"corporation":false,"usgs":false,"family":"Jayanthi","given":"Harikishan","email":"","affiliations":[{"id":79183,"text":"ASRC Federal Contractor to the USGS EROS","active":true,"usgs":false}],"preferred":false,"id":944806,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Husak, Greg","contributorId":359038,"corporation":false,"usgs":false,"family":"Husak","given":"Greg","affiliations":[{"id":81109,"text":"University of California-Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944807,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lee, Donghoon 0000-0001-5438-903X","orcid":"https://orcid.org/0000-0001-5438-903X","contributorId":292417,"corporation":false,"usgs":false,"family":"Lee","given":"Donghoon","email":"","affiliations":[{"id":62899,"text":"Climate Hazards Center, University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":944808,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barker, Brian","contributorId":359048,"corporation":false,"usgs":false,"family":"Barker","given":"Brian","affiliations":[],"preferred":false,"id":944809,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pervez, Shahriar 0000-0003-3417-1871 shahriar.pervez.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-3417-1871","contributorId":174568,"corporation":false,"usgs":true,"family":"Pervez","given":"Shahriar","email":"shahriar.pervez.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944810,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Slinski, Kimberly","contributorId":337030,"corporation":false,"usgs":false,"family":"Slinski","given":"Kimberly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":944811,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Justice, Christina","contributorId":347086,"corporation":false,"usgs":false,"family":"Justice","given":"Christina","email":"","affiliations":[{"id":37106,"text":"Cherokee Nation","active":true,"usgs":false}],"preferred":false,"id":944812,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"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":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944813,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McNally, Amy","contributorId":337027,"corporation":false,"usgs":false,"family":"McNally","given":"Amy","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":944814,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"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":944815,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Verdin, James","contributorId":337042,"corporation":false,"usgs":false,"family":"Verdin","given":"James","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":944816,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70256219,"text":"70256219 - 2024 - An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape","interactions":[],"lastModifiedDate":"2024-07-29T13:58:25.97437","indexId":"70256219","displayToPublicDate":"2024-06-12T08:41:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape","docAbstract":"Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16122122","usgsCitation":"Petrakis, R., Norman, L., Villarreal, M.L., Senay, G.B., Friedrichs, M., Cassassuce, F., Gomis, F., and Nagler, P.L., 2024, An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape: Remote Sensing, v. 16, no. 12, 2122, 35 p.; Data Release, https://doi.org/10.3390/rs16122122.","productDescription":"2122, 35 p.; Data Release","ipdsId":"IP-160120","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":439410,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16122122","text":"Publisher Index Page"},{"id":434943,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBXG2R","text":"USGS data release","linkHelpText":"Monthly Ensemble Mean Evapotranspiration (EMET) Product for the Los Planes basin in Baja California Sur, Mexico from January 2006 through December 2021: U.S. Geological Survey Data Release"},{"id":431560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Baja California Sur","otherGeospatial":"Los Planes Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.15,\n 24.185882621902465\n ],\n [\n -110.15,\n 23.666\n ],\n [\n -109.796162654228,\n 23.666\n ],\n [\n -109.796162654228,\n 24.185882621902465\n ],\n [\n -110.15,\n 24.185882621902465\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Petrakis, Roy E. 0000-0001-8932-077X rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":907134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":907135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":907136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":907137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@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":907138,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cassassuce, Florance","contributorId":337023,"corporation":false,"usgs":false,"family":"Cassassuce","given":"Florance","email":"","affiliations":[{"id":80952,"text":"Rancho Ancon","active":true,"usgs":false}],"preferred":false,"id":907139,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gomis, Florent","contributorId":337024,"corporation":false,"usgs":false,"family":"Gomis","given":"Florent","email":"","affiliations":[{"id":80952,"text":"Rancho Ancon","active":true,"usgs":false}],"preferred":false,"id":907140,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":907141,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70255332,"text":"70255332 - 2024 - Chlorophyll a in lakes and streams of the United States (2005–2022)","interactions":[],"lastModifiedDate":"2024-06-17T12:02:05.380493","indexId":"70255332","displayToPublicDate":"2024-06-12T06:59:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17083,"text":"Nature Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Chlorophyll a in lakes and streams of the United States (2005–2022)","docAbstract":"The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.
Letterkenny Army Depot in Chambersburg, Pennsylvania, built an Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility in 2016 to centralize rocket motor destruction and contain all waste during the destruction process. The U.S. Geological Survey has collected environmental samples from groundwater, surface water, and soils at ARMD since 2016.
During 2021, samples were collected from four groundwater wells in September, one surface-water site in October, and five soil sites in November near the facility. Samples were analyzed for nutrients, trace metals, major ions, total volatile organic compounds, and perchlorate. Perchlorate was not detected in any 2021 samples.
Groundwater results showed no constituents exceeded any U.S. Environmental Protection Agency (EPA) maximum contaminant level (MCL). Dissolved arsenic (As) was detected in one well above the reporting detection level (RDL) of 3 micrograms per liter (μg/L) at 5.4 μg/L but below its MCL of 10 μg/L. Dissolved iron (Fe) was the only inorganic constituent measured above an EPA secondary maximum contaminant level (SMCL). All groundwater samples collected in 2021 exceeded the Fe SMCL of 300 μg/L, with concentrations ranging from 390 μg/L to 3,500 μg/L.
Surface-water data collected during 2021 showed no measured constituents in the surface-water sample that exceeded any EPA MCL or SMCL.
Soil samples collected from 2016 through 2021 showed all concentrations of As exceeded the EPA soil screening levels of 3 milligrams per kilogram (mg/kg) but did not exceed the Pennsylvania medium-specific concentrations for As of 61 mg/kg. Arsenic concentrations in 2021 ranged from 9.1 mg/kg to 12.9 mg/kg.
The 2021 results for the ARMD Facility indicate no increases in concentrations of reported compounds compared to data from 2016 to 2020. The contained burn treatment facility for demilitarization of rocket motors during 2021 appears to have operated without elevating concentrations of target compounds compared to previous years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241031","collaboration":"Prepared in Cooperation with the Letterkenny Army Depot","usgsCitation":"Galeone, D.G., and Donmoyer, S.J., 2024, Environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2021: U.S. Geological Survey Open-File Report 2024–1031, 31 p., https://doi.org/10.3133/ofr20241031","productDescription":"Report: vii, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-148346","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":499252,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117074.htm","linkFileType":{"id":5,"text":"html"}},{"id":429681,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1031/ofr20241031.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1031 XML"},{"id":429679,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YIATZ","text":"USGS data release","linkHelpText":"Groundwater, surface water, and soil data collected near and at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania"},{"id":429680,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1031/images/"},{"id":429677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1031/ofr20241031.pdf","text":"Report","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1031 PDF"},{"id":429678,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241031/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1031 HTML"},{"id":429676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1031/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Letterkenny Army Depot","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.7937803364734,\n 40.07953712912567\n ],\n [\n -77.7937803364734,\n 39.95565132046923\n ],\n [\n -77.61334530644311,\n 39.95565132046923\n ],\n [\n -77.61334530644311,\n 40.07953712912567\n ],\n [\n -77.7937803364734,\n 40.07953712912567\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The groundwater below the Red Hill Bulk Fuel Storage Facility (the facility) in Oʻahu, Hawaiʻi, contains fuel compounds from past spills. This study used carbon-14 analyses to distinguish fuel-derived carbon from background carbon, along with other biodegradation indicators, to address two goals: (1) determine the extent and migration direction of groundwater affected by residual fuel below the facility and (2) determine if residual fuel locations in the subsurface could be identified by analyzing soil gas at the surface above the facility.
Groundwater from 19 wells was sampled between September 2022 and April 2023. Nonvolatile dissolved organic carbon (NVDOC) from a well presumed to be unaffected by past spills contained 38 percent ancient carbon indicating a natural source of ancient carbon in the subsurface. The NVDOC concentrations and ancient carbon percentages indicate fuel biodegradation products are likely present on the north and south of Red Hill with the greatest effects at well RHMW02 near the 2014 spill site. The NVDOC concentrations are almost three times higher than diesel range organic (DRO) concentrations in groundwater from the same sites. Major ion data indicate that iron reduction is an important biodegradation process.
Soil probe samples and soil carbon traps were used to determine the carbon-14 content of soil carbon dioxide. Ancient carbon from fuel biodegradation was not detected at any soil probe or carbon trap site in contrast to a 2017 study which reported ancient carbon detections. A reanalysis of the 2017 results using a range of local values for background carbon-14 indicates that ancient carbon from fuel biodegradation was probably only detected in lower tunnel exhaust system samples and not in any soil carbon trap samples. Measurements of carbon dioxide efflux with a dynamic closed chamber were highly variable. The soil gas results indicate that soil gas measurements at land surface were not useful for detecting residual fuel at the facility.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245034","collaboration":"Prepared in cooperation with the U.S. Navy and the Defense Logistics Agency","programNote":"Environmental Health Program","usgsCitation":"Trost, J.J., Bekins, B.A., Jaeschke, J.B., Delin, G.N., Sinclair, D.A., Stack, J.K., Nakama, R.K., Miyajima, U.M., Pagaduan, L.D., and Cozzarelli, I.M., 2024, Distribution of ancient carbon in groundwater and soil gas from degradation of petroleum near the Red Hill Bulk Fuel Storage Facility, Oʻahu, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2024–5034, 54 p., https://doi.org/10.3133/sir20245034.","productDescription":"Report: xi, 54 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":429760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5034/sir20245034.pdf","text":"Report","size":"42.6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429761,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5034/sir20245034.XML"},{"id":429767,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245034/full"},{"id":429766,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":429762,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5034/images/"},{"id":429759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5034/coverthb.jpg"},{"id":429765,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TIDAA4","text":"USGS data release","linkHelpText":"Groundwater and soil gas data, methods, and quality assurance information for samples collected to determine ancient carbon distributions at Red Hill Bulk Fuel Storage Facility, Oʻahu, Hawaiʻi, 2022–2023"},{"id":497942,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117072.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Red Hill Bulk Fuel Storage Facility","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.91707912662315,\n 21.38710545210772\n ],\n [\n -157.91707912662315,\n 21.358702773157617\n ],\n [\n -157.87618345098332,\n 21.358702773157617\n ],\n [\n -157.87618345098332,\n 21.38710545210772\n ],\n [\n -157.91707912662315,\n 21.38710545210772\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
or
Director, Pacific Islands Water Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
The potential for polycyclic aromatic hydrocarbon (PAH)-related effects in benthic organisms is commonly estimated from organic carbon-normalized sediment concentrations based on equilibrium partitioning (EqP). Although this approach is useful for screening purposes, it may overestimate PAH bioavailability by orders of magnitude in some sediments, leading to inflated exposure estimates and potentially unnecessary remediation costs. Recently, passive samplers have been shown to provide an accurate assessment of the freely dissolved concentrations of PAHs, and thus their bioavailability and possible biological effects, in sediment porewater and overlying surface water. We used polyethylene passive sampling devices (PEDs) to measure freely dissolved porewater and water column PAH concentrations at 55 Great Lakes (USA/Canada) tributary locations. The potential for PAH-related biological effects using PED concentrations were estimated with multiple approaches by applying EqP, water quality guidelines, and pathway-based biological activity based on in vitro bioassay results from ToxCast. Results based on the PED-based exposure estimates were compared with EqP-derived exposure estimates for concurrently collected sediment samples. The results indicate a potential overestimation of bioavailable PAH concentrations by up to 960-fold using the EqP-based method compared with measurements using PEDs. Even so, PED-based exposure estimates indicate a high potential for PAH-related biological effects at 14 locations. Our findings provide an updated, weight-of-evidence–based site prioritization to help guide possible future monitoring and mitigation efforts.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5896","usgsCitation":"Baldwin, A.K., Corsi, S., Alvarez, D.A., Villeneuve, D.L., Ankley, G., Blackwell, B., Mills, M.A., Lenaker, P.L., and Nott, M.A., 2024, Potential hazards of polycyclic aromatic hydrocarbons in Great Lakes tributaries using water column and porewater passive samplers and sediment wquilibrium partitioning: Environmental Toxicology and Chemistry, v. 43, no. 7, p. 1509-1523, https://doi.org/10.1002/etc.5896.","productDescription":"15 p.","startPage":"1509","endPage":"1523","ipdsId":"IP-150118","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439414,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5896","text":"Publisher Index Page"},{"id":430014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, Wisconsin","otherGeospatial":"Great lakes tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.50038664600407,\n 47.734293732737\n ],\n [\n -93.32668214062738,\n 47.781822909562834\n ],\n [\n -93.66522319683997,\n 46.6205914248724\n ],\n [\n -90.10144072177354,\n 45.72245658797377\n ],\n [\n -89.24336353619054,\n 43.31706446043586\n ],\n [\n -87.74990312848416,\n 41.14799638657436\n ],\n [\n -84.68233074166602,\n 40.27325147141107\n ],\n [\n -80.98474656912649,\n 40.9132970607065\n ],\n [\n -75.35756182467634,\n 42.91509952407404\n ],\n [\n -75.72359765908611,\n 44.31876428021542\n ],\n [\n -76.89109191305005,\n 43.68270068891388\n ],\n [\n -79.27814795358721,\n 43.46605571456897\n ],\n [\n -78.96930286423293,\n 42.9910648317088\n ],\n [\n -82.81924582493077,\n 41.844014344440694\n ],\n [\n -83.20482143032358,\n 42.31054790382913\n ],\n [\n -82.31114877039539,\n 42.8842174758241\n ],\n [\n -81.9076889466279,\n 44.500553732181515\n ],\n [\n -84.24318006713943,\n 46.72260994496574\n ],\n [\n -87.10560312557051,\n 47.83801940362676\n ],\n [\n -90.50038664600407,\n 47.734293732737\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":903310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":903311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, David A. 0000-0002-6918-2709","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":220763,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":903312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Villeneuve, David L.","contributorId":338508,"corporation":false,"usgs":false,"family":"Villeneuve","given":"David","email":"","middleInitial":"L.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":903313,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ankley, Gerald T.","contributorId":332307,"corporation":false,"usgs":false,"family":"Ankley","given":"Gerald T.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":903314,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blackwell, Brett R.","contributorId":173601,"corporation":false,"usgs":false,"family":"Blackwell","given":"Brett R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":903315,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":903316,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lenaker, Peter L. 0000-0002-9469-6285 plenaker@usgs.gov","orcid":"https://orcid.org/0000-0002-9469-6285","contributorId":5572,"corporation":false,"usgs":true,"family":"Lenaker","given":"Peter","email":"plenaker@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":903317,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nott, Michelle A. 0000-0003-3968-7586","orcid":"https://orcid.org/0000-0003-3968-7586","contributorId":221766,"corporation":false,"usgs":true,"family":"Nott","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":903318,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255313,"text":"70255313 - 2024 - Temporal habitat use of mule deer in the Pueblo of Santa Ana, New Mexico","interactions":[],"lastModifiedDate":"2024-07-15T15:38:36.010069","indexId":"70255313","displayToPublicDate":"2024-06-11T06:39:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Temporal habitat use of mule deer in the Pueblo of Santa Ana, New Mexico","docAbstract":"Mule deer (Odocoileus hemionus) are important economically, culturally, and recreationally to the Pueblo of Santa Ana in central New Mexico, USA. Studies of habitat selection improve our understanding of mule deer ecology in central New Mexico and provide the Tribe with valuable information for management of mule deer. We used global positioning system telemetry-collar data collected on mule deer around the Pueblo of Santa Ana to create resource selection functions from proximity-based habitat predictors using a generalized linear mixed model. We created separate resource selection functions for females and males during summer and winter at different times of the day. Season generally had a greater effect on mule deer habitat use than the time of day. Female and male mule deer selected for similar habitats but were sexually segregated in their summer distributions. These findings are consistent with results from other locations where mule deer partitioned habitat similarly between seasons and sexes. Supported models reaffirm accepted patterns of habitat selection for mule deer to the Pueblo of Santa Ana where local results were lacking. Our results can help managers identify locations in and around the Pueblo of Santa Ana where future development such as highway expansion are likely to conflict with mule deer activity and locations where habitat enhancement projects such as adding water sources can have the greatest effect for the deer population.
As part of a long-term cooperative program to monitor water quality within the Scituate Reservoir drainage area, the U.S. Geological Survey in cooperation with Providence Water (sometimes known as Providence Water Supply Board) collected streamflow and water-quality data in tributaries to the Scituate Reservoir, Rhode Island. Streamflow and concentrations of chloride and sodium estimated from records of specific conductance for 14 tributaries were used to calculate loads of chloride and sodium during water year 2020 (October 1, 2019, through September 30, 2020). Water-quality samples were collected by Providence Water at 37 sampling stations on tributaries to the Scituate Reservoir during water year 2020. These water-quality data are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields of selected water-quality constituents for water year 2020 in this report.
Annual mean streamflows for monitoring stations in this study ranged from about 0.32 to 26.7 cubic feet per second during water year 2020. At the 14 continuous-record streamgages, tributaries transported about 2,200 metric tons of chloride and 1,400 metric tons of sodium to the Scituate Reservoir; annual chloride yields for the tributaries ranged from 13 to 110 metric tons per square mile, and annual sodium yields ranged from 8.8 to 6 metric tons per square mile. At the stations where water-quality samples were collected by Providence Water, the medians of the median daily loads were 220 kilograms chloride per day, 10 grams nitrite as nitrogen per day, 500 grams nitrate as nitrogen per day, 290 grams orthophosphate as phosphate per day, 55,000 million colony forming units of coliform bacteria per day, and less than 900 million colony forming units of Escherichia coli per day. The medians of the median yields were 76 kilograms chloride per day per square mile, 4.1 grams nitrite as nitrogen per day per square mile, 240 grams nitrate as nitrogen per day per square mile, 100 grams orthophosphate as phosphate per day per square mile, 31,000 million colony forming units of coliform bacteria per day per square mile, and less than 260 million colony forming units of Escherichia coli per day per square mile.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1192","collaboration":"Prepared in cooperation with Providence Water","usgsCitation":"Smith, K.P., 2024, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2020: U.S. Geological Survey Data Report 1192, 31 p., https://doi.org/10.3133/dr1192.","productDescription":"Report: v, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-139757","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499106,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117055.htm","linkFileType":{"id":5,"text":"html"}},{"id":428114,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1192/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1192"},{"id":428113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1192/dr1192.pdf","text":"Report","size":"5.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1192"},{"id":428112,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1192/coverthb2.jpg"},{"id":428116,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1192/dr1192.XML","linkFileType":{"id":8,"text":"xml"}},{"id":428115,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1192/images/"},{"id":428117,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WK8N0F","text":"USGS data release","linkHelpText":"Water-quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir (ver. 2.0, July 2022)"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir Drainage Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.77640344375646,\n 41.94712091202689\n ],\n [\n -71.77640344375646,\n 41.72813307145168\n ],\n [\n -71.53464552766611,\n 41.72813307145168\n ],\n [\n -71.53464552766611,\n 41.94712091202689\n ],\n [\n -71.77640344375646,\n 41.94712091202689\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Middle and Late Holocene sediments have not been extensively sampled in Lake Tanganyika, and much remains unknown about the response of the Rift Valley’s largest lake to major environmental shifts during the Holocene, including the termination of the African Humid Period (AHP). Here, we present an integrated study (sedimentology, mineralogy, and geochemistry) of a radiocarbon-dated sediment core from the Kavala Island Ridge (KIR) that reveals paleoenvironmental variability in Lake Tanganyika since the Middle Holocene with decadal to centennial resolution. Massive blue-gray sandy silts represent sediments deposited during the terminal AHP (~5880–4640 cal yr BP), with detrital particle size, carbon concentrations, light stable isotopes, and mineralogy suggesting an influx of river-borne soil organic matter and weathered clay minerals to the lake at that time. Enhanced by the AHP’s warm and wet conditions, chemical weathering and erosion of Lake Tanganyika’s watershed appears to have promoted considerable nutrient recharge to the lake system. Following a relatively gradual termination of the AHP over the period from ~4640 cal yr BP to ~3680 cal yr BP, laminated and organic carbon-rich sediments began accumulating on the KIR. δ15Nbulk, C/N, and hydrogen index data suggest high relative primary production from a mix of algae and cyanobacteria, most likely in response to nutrient availability in the water column under a cooler and seasonally dry climate from ~3680 to 1100 cal yr BP. Sediments deposited during the Common Era show considerable variability in magnetic susceptibility, total organic carbon content, carbon isotopes, and C/N, consistent with dynamic hydroclimate conditions that affected the depositional patterns, including substantial changes around the Medieval Climate Anomaly and Little Ice Age. Data from this study highlight the importance of sedimentary records to constrain boundary conditions in hydroclimate and nutrient flux that can inform long-term ecosystem response in Lake Tanganyika.
","language":"English","publisher":"Sage","doi":"10.1177/09596836241254475","usgsCitation":"Domingos-Luz, L., Soreghan, M.J., Rasbold, G., Ellis, G.S., Birdwell, J.E., Kimirei, I.A., Scholz, C., and McGlue, M., 2024, Middle-late Holocene paleolimnological changes in central Lake Tanganyika: Integrated evidence from the Kavala Island Ridge (Tanzania): The Holocene, v. 34, no. 9, p. 1167-1180, https://doi.org/10.1177/09596836241254475.","productDescription":"14 p.","startPage":"1167","endPage":"1180","ipdsId":"IP-158298","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":465011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tanzania","otherGeospatial":"Kavala Island Ridge, Lake Tanganyika","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 29.043817847394536,\n -5.6155120583288465\n ],\n [\n 29.043817847394536,\n -6.203461467324189\n ],\n [\n 30.023780732233263,\n -6.203461467324189\n ],\n [\n 30.023780732233263,\n -5.6155120583288465\n ],\n [\n 29.043817847394536,\n -5.6155120583288465\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Domingos-Luz, Leandro","contributorId":347061,"corporation":false,"usgs":false,"family":"Domingos-Luz","given":"Leandro","email":"","affiliations":[{"id":83051,"text":"Department of Earth and Environmental Sciences, University of Kentucky, Lexington KY, 40506, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":920731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soreghan, Michael J.","contributorId":347062,"corporation":false,"usgs":false,"family":"Soreghan","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":83052,"text":"School of Geosciences, University of Oklahoma, Norman, OK, 73019, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":920732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rasbold, Giliane G.","contributorId":347063,"corporation":false,"usgs":false,"family":"Rasbold","given":"Giliane G.","affiliations":[{"id":83051,"text":"Department of Earth and Environmental Sciences, University of Kentucky, Lexington KY, 40506, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":920733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":920734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":920735,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kimirei, Ishmael A.","contributorId":347064,"corporation":false,"usgs":false,"family":"Kimirei","given":"Ishmael","email":"","middleInitial":"A.","affiliations":[{"id":83053,"text":"Tanzania Fisheries Research Institute, Dar-es-Salaam, Tanzania","active":true,"usgs":false}],"preferred":false,"id":920736,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scholz, Christopher A.","contributorId":149267,"corporation":false,"usgs":false,"family":"Scholz","given":"Christopher A.","affiliations":[{"id":17692,"text":"Syracuse University, Syracuse NY","active":true,"usgs":false}],"preferred":false,"id":920737,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McGlue, Michael M.","contributorId":225229,"corporation":false,"usgs":false,"family":"McGlue","given":"Michael M.","affiliations":[{"id":41081,"text":"Department of Geosciences, The University of Arizona, Tucson AZ","active":true,"usgs":false}],"preferred":false,"id":920738,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}