The noble gas temperature climate proxy is an established tool that has previously been applied to determine the source of groundwater recharge, however, unanswered questions remain. In fractured media (e.g., volcanic islands) recharge can be so rapid that groundwater is significantly depleted in heavy noble gases, indicating that the water has retained noble gas concentrations from higher elevations. Previous studies of rain samples have confirmed a match to patterns seen in fractured-rock groundwater for heavy noble gases along with a significant helium excess. Snow has been shown to be a credible source for both the helium excess and the observed heavy noble gas pattern. Here, liquid cloud water samples were collected at two mountainous sites and analyzed for noble gas concentrations. A pattern like that of rainwater was found. However, an analysis of diffusive uptake of noble gases into cloud water demonstrates that droplets of 1 mm diameter and smaller should be in constant solubility equilibrium with the atmosphere. To explain this, we present a novel hypothesis that relies on the assumption that liquid water consists of two types of water molecule clusters bounded by hydrogen bonds: a low-density ice-like structure and a high-density condensed structure. In this model, the pressure gradient near the surface of a droplet resulting from surface tension could allow for the formation of a surface layer that is rich in ice-like low density clusters. This can explain both the helium excess and the heavy noble gas depletion seen in the samples.
Indicator species are species which can be monitored as an index to measure the overall health of an ecosystem. Crocodylians have been shown to be good indicators of wetland condition as they respond to changes in hydrology, can be efficiently monitored, and are a key part of ecosystem trophic relationships. Eye shine surveys at night are a standard method used to sample alligators, but because some individuals that are present in a study area may go undetected and the proportion of individuals counted is not constant over time, appropriate modeling is required to convert counts to estimates of abundance. We analyzed 13 years of American alligator (Alligator mississippiensis) survey count data from South Florida using an N-mixture model. Alligator abundance estimates were assigned to quartiles that were then represented as color coded categories of red, yellow, or green to provide a straightforward rating of Everglades restoration based on familiar stoplight coloring. These results were then compared to a previously used method in which unadjusted counts of these same data were assigned to color coded quartile categories. Water depth played a major role in the detection probability of alligators and the stoplight colors between the two methods matched 76% of the time. This suggests that the original stoplight score method provided a good overall snapshot of the trends in alligator abundance in the Everglades; however, the hierarchical models estimate abundance and trends of alligator abundance by incorporating detection probability thus providing unbiased estimates of abundance.
As science becomes increasingly cross-disciplinary and scientific models become increasingly cross-coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like “season” or “variability,” and that simple decompositions can be combined to represent more complex concepts, like “seasonal variability,” creating an expressive language through which to interrogate models and data.
Above average precipitation from October 2018 through July 2019 in the Maumee River (R.) Basin resulted in 29% of cropland left fallow, providing a glimpse of potential effects from decreased nutrient application. Ongoing monitoring at 15 water-quality sites on the Maumee R. upstream from Defiance enabled comparison with 2017, which was hydrologically similar to 2019 in precipitation and streamflow. In 2019, nitrate (as nitrogen; NO3-N) for March-July was significantly less than previous years (2015–2018), but the response for phosphorus (P) was more complicated. Relative to 2017, total P (TP) was lower at 7 of 15 sites, but higher at 7, reflecting higher suspended sediment (SS). Dissolved P (DP) was generally lower, but less different than NO3; DP was higher at 3 sites. DP-P:NO3-N was generally higher in 2019, DP-P:TP was lower, and there was less TP relative to SS. Overall, less P was in the system in 2019. However smaller streams showed a large range of difference between 2019 and 2017 for all constituents, indicating variability in land management and physiography. In contrast, all constituents were lower in 2019 in larger (>5000 km2) streams, including the Maumee R. near Defiance, where the difference in NO3 (−37%) exceeded that for TP (−16%), DP (−10%), and SS (−20%). Differences in these relations among N, P, and SS indicate that P was available from legacy sources that are more difficult to distinguish during typical agricultural production years and that some material from 2019 was stored in the system upstream from the largest sites.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.10.004","usgsCitation":"Williamson, T.N., Shaffer, K., Runkle, D.L., Hardebeck, M.J., Dobrowolski, E.G., Frey, J.W., Baker, N.T., Collier, K.M., Huitger, C.A., Kula, S.P., Haefner, R.J., Hartley, L.M., Crates, H.F., Webber, J.J., Finnegan, D.P., Reithel, N.J., Toussant, C.A., and Weaver, T.L., 2021, Nutrient and suspended-sediment concentrations in the Maumee River and tributaries during 2019 rain-induced fallow conditions: Journal of Great Lakes Research, v. 47, no. 6, p. 1726-1736, https://doi.org/10.1016/j.jglr.2021.10.004.","productDescription":"11 p.","startPage":"1726","endPage":"1736","ipdsId":"IP-122985","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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0000-0002-0656-622X","orcid":"https://orcid.org/0000-0002-0656-622X","contributorId":269783,"corporation":false,"usgs":true,"family":"Crates","given":"Hunter","email":"","middleInitial":"Frederick","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827910,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Webber, J. Jeremy 0000-0002-2512-2448","orcid":"https://orcid.org/0000-0002-2512-2448","contributorId":259209,"corporation":false,"usgs":true,"family":"Webber","given":"J.","email":"","middleInitial":"Jeremy","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827911,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Finnegan, Dennis P. 0000-0003-1934-1262 dpfinneg@usgs.gov","orcid":"https://orcid.org/0000-0003-1934-1262","contributorId":269784,"corporation":false,"usgs":true,"family":"Finnegan","given":"Dennis","email":"dpfinneg@usgs.gov","middleInitial":"P.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827912,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Reithel, Nicholas J. 0000-0002-7205-357X","orcid":"https://orcid.org/0000-0002-7205-357X","contributorId":269785,"corporation":false,"usgs":true,"family":"Reithel","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827913,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Toussant, Chad A. 0000-0002-1324-0914","orcid":"https://orcid.org/0000-0002-1324-0914","contributorId":210079,"corporation":false,"usgs":true,"family":"Toussant","given":"Chad","middleInitial":"A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827914,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Weaver, Thomas L. 0000-0002-6523-2553 tlweaver@usgs.gov","orcid":"https://orcid.org/0000-0002-6523-2553","contributorId":213949,"corporation":false,"usgs":true,"family":"Weaver","given":"Thomas","email":"tlweaver@usgs.gov","middleInitial":"L.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":827915,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70226625,"text":"70226625 - 2021 - Tissue and salinity specific Na+/Cl− cotransporter (NCC) orthologues involved in the adaptive osmoregulation of sea lamprey (Petromyzon marinus)","interactions":[],"lastModifiedDate":"2021-12-02T14:26:33.259972","indexId":"70226625","displayToPublicDate":"2021-11-22T06:44:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Tissue and salinity specific Na+/Cl− cotransporter (NCC) orthologues involved in the adaptive osmoregulation of sea lamprey (Petromyzon marinus)","title":"Tissue and salinity specific Na+/Cl− cotransporter (NCC) orthologues involved in the adaptive osmoregulation of sea lamprey (Petromyzon marinus)","docAbstract":"Two orthologues of the gene encoding the Na+-Cl− cotransporter (NCC), termed ncca and nccb, were found in the sea lamprey genome. No gene encoding the Na+-K+-2Cl− cotransporter 2 (nkcc2) was identified. In a phylogenetic comparison among other vertebrate NCC and NKCC sequences, the sea lamprey NCCs occupied basal positions within the NCC clades. In freshwater, ncca mRNA was found only in the gill and nccb only in the intestine, whereas both were found in the kidney. Intestinal nccb mRNA levels increased during late metamorphosis coincident with salinity tolerance. Acclimation to seawater increased nccb mRNA levels in the intestine and kidney. Electrophysiological analysis of intestinal tissue ex vivo showed this tissue was anion absorptive. After seawater acclimation, the proximal intestine became less anion absorptive, whereas the distal intestine remained unchanged. Luminal application of indapamide (an NCC inhibitor) resulted in 73% and 30% inhibition of short-circuit current (Isc) in the proximal and distal intestine, respectively. Luminal application of bumetanide (an NKCC inhibitor) did not affect intestinal Isc. Indapamide also inhibited intestinal water absorption. Our results indicate that NCCb is likely the key ion cotransport protein for ion uptake by the lamprey intestine that facilitates water absorption in seawater. As such, the preparatory increases in intestinal nccb mRNA levels during metamorphosis of sea lamprey are likely critical to development of whole animal salinity tolerance.
Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021EF002285","usgsCitation":"Anderson, D.L., Ruggiero, P., Mendez, F.J., Barnard, P.L., Erikson, L.H., O'Neill, A., Merrifield, M., Rueda, A., Cagigal, L., and Marra, J.M., 2021, Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model: Earth's Future, v. 9, no. 12, e2021EF002285, 24 p., https://doi.org/10.1029/2021EF002285.","productDescription":"e2021EF002285, 24 p.","ipdsId":"IP-111912","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450163,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021ef002285","text":"External Repository"},{"id":395620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Diego","otherGeospatial":"Naval Base Coronado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.32986450195312,\n 32.54565554741415\n ],\n [\n -117.05795288085936,\n 32.54565554741415\n ],\n [\n -117.05795288085936,\n 32.87555050280593\n ],\n [\n -117.32986450195312,\n 32.87555050280593\n ],\n [\n -117.32986450195312,\n 32.54565554741415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, D. L.","contributorId":274874,"corporation":false,"usgs":false,"family":"Anderson","given":"D.","email":"","middleInitial":"L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":833469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruggiero, P.","contributorId":191579,"corporation":false,"usgs":false,"family":"Ruggiero","given":"P.","email":"","affiliations":[],"preferred":false,"id":833470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mendez, F. J.","contributorId":274876,"corporation":false,"usgs":false,"family":"Mendez","given":"F.","email":"","middleInitial":"J.","affiliations":[{"id":27840,"text":"Universidad de Cantabria","active":true,"usgs":false}],"preferred":false,"id":833471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O'Neill, Andrea C. 0000-0003-1656-4372 aoneill@usgs.gov","orcid":"https://orcid.org/0000-0003-1656-4372","contributorId":5351,"corporation":false,"usgs":true,"family":"O'Neill","given":"Andrea C.","email":"aoneill@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Merrifield, M.","contributorId":274878,"corporation":false,"usgs":false,"family":"Merrifield","given":"M.","email":"","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":833475,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rueda, A.","contributorId":274880,"corporation":false,"usgs":false,"family":"Rueda","given":"A.","email":"","affiliations":[{"id":27840,"text":"Universidad de Cantabria","active":true,"usgs":false}],"preferred":false,"id":833476,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cagigal, L.","contributorId":274882,"corporation":false,"usgs":false,"family":"Cagigal","given":"L.","affiliations":[{"id":27840,"text":"Universidad de Cantabria","active":true,"usgs":false}],"preferred":false,"id":833477,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marra, J. M.","contributorId":219619,"corporation":false,"usgs":false,"family":"Marra","given":"J.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":833478,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70226550,"text":"70226550 - 2021 - Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud","interactions":[],"lastModifiedDate":"2021-11-24T13:27:41.850893","indexId":"70226550","displayToPublicDate":"2021-11-21T07:23:35","publicationYear":"2021","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":"Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud","docAbstract":"Understanding the age and movement of groundwater is important for predicting the vulnerability of wells to contamination, constraining flow models that inform sustainable groundwater management, and interpreting geochemical signals that reflect past climate. Due to both the ubiquity of groundwater with order ten-thousand-year residence times and its importance for climate reconstruction of the last glacial period, there is a strong need for improving geochemical dating tools on this timescale. Whereas 14C of dissolved inorganic carbon and dissolved 4He are common age tracers for Late Pleistocene groundwater, each is limited by systematic uncertainties related to aquifer composition and lithology, and the extent of water-rock interaction. In principle, radiogenic 40Ar in groundwater acquired from decay of 40K in aquifer minerals should be insensitive to some processes that impact 14C and 4He and thus represent a useful, complementary age tracer. In practice, however, detection of significant radiogenic 40Ar signals in groundwater has been limited to a small number of studies of extremely old groundwater (>100 ka). Here we present the first high-precision (<1‰) measurements of triple Ar isotopes (40Ar, 38Ar, 36Ar) in groundwater. We introduce a model that distinguishes radiogenic 40Ar from atmospheric 40Ar by using the non-radiogenic Ar isotopes (36Ar, 38Ar) to correct for mass-dependent fractionation. Using this model, we investigate variability in radiogenic 40Ar excess (Δ40Ar) across 58 groundwater samples collected from 36 wells throughout California (USA). We find that Δ40Ar ranges from ~0‰ (the expected minimum value) to +4.2‰ across three study areas near Fresno, San Diego, and the western Mojave Desert. Based on measurements from a network of 23 scientific monitoring wells in San Diego, we find evidence for a strong dependence of Δ40Ar on aquifer lithology. We suggest that Δ40Ar is fundamentally controlled by the weathering of old K-bearing minerals and thus reflects both the degree of groundwater-rock interaction, which is related to groundwater age, and the integrated flow through different geological formations. Future studies of Late Pleistocene groundwater may benefit from high-precision triple Ar isotope measurements as a new tool to better interpret 14C- and 4He-based constraints on groundwater age and flow.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120458","usgsCitation":"Seltzer, A., Krantz, J.A., Ng, J., Danskin, W.R., Bekaert, D., Barry, P.H., Kimbrough, D.L., Kulongoski, J.T., and Severinghaus, J.P., 2021, The triple argon isotope composition of groundwater on ten-thousand-year timescales: Chemical Geology, v. 583, 120458, 12 p., https://doi.org/10.1016/j.chemgeo.2021.120458.","productDescription":"120458, 12 p.","ipdsId":"IP-134673","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450168,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120458","text":"Publisher Index Page"},{"id":417210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"583","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Seltzer, Alan 0000-0003-2870-1215","orcid":"https://orcid.org/0000-0003-2870-1215","contributorId":270717,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":873138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krantz, John A.","contributorId":305541,"corporation":false,"usgs":false,"family":"Krantz","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":66250,"text":"Woods Hole Oceanographic Institution, Marine Chemistry & Geochemistry Department, Woods Hole, MA, United States of America","active":true,"usgs":false}],"preferred":false,"id":873139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ng, Jessica","contributorId":268304,"corporation":false,"usgs":false,"family":"Ng","given":"Jessica","email":"","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":873140,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danskin, Wesley R. 0000-0001-8672-5501 wdanskin@usgs.gov","orcid":"https://orcid.org/0000-0001-8672-5501","contributorId":1034,"corporation":false,"usgs":true,"family":"Danskin","given":"Wesley","email":"wdanskin@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873141,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bekaert, David 0000-0002-1062-6221","orcid":"https://orcid.org/0000-0002-1062-6221","contributorId":270718,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":873142,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barry, Peter H. 0000-0002-6960-1555","orcid":"https://orcid.org/0000-0002-6960-1555","contributorId":218244,"corporation":false,"usgs":false,"family":"Barry","given":"Peter","email":"","middleInitial":"H.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":873143,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kimbrough, David L.","contributorId":211569,"corporation":false,"usgs":false,"family":"Kimbrough","given":"David","email":"","middleInitial":"L.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":873144,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873145,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Severinghaus, Jeffrey P.","contributorId":140715,"corporation":false,"usgs":false,"family":"Severinghaus","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":873146,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225702,"text":"sir20205137 - 2021 - Numerical modeling of groundwater flow in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire","interactions":[],"lastModifiedDate":"2022-04-14T16:02:52.30844","indexId":"sir20205137","displayToPublicDate":"2021-11-19T13:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5137","displayTitle":"Numerical Modeling of Groundwater Flow in the Crystalline-Rock Aquifer in the Vicinity of the Savage Municipal Water-Supply Well Superfund Site, Milford, New Hampshire","title":"Numerical modeling of groundwater flow in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire","docAbstract":"In 2010, tetrachloroethylene (PCE), a chlorinated volatile organic compound, was detected in groundwater from deep (more than 300 feet below land surface) fractures in monitoring wells tapping a crystalline-rock aquifer. The aquifer underlies the Milford-Souhegan glacial-drift aquifer, a high water-producing aquifer, and the Savage Municipal Water-Supply Well Superfund site in Milford, New Hampshire. Between 30 and 40 residential water-supply wells are near (0.25 mile north of) the PCE-contaminated monitoring wells. Some of the residential water-supply wells are likely installed in similar rock types and formations as those of the monitoring wells installed as part of the Superfund site. As of 2020, periodic sampling by the U.S. Environmental Protection Agency and New Hampshire Department of Environmental Services (cooperative partners for this study) since 1996 had not detected PCE in groundwater from the residential water-supply wells. Nevertheless, understanding the vulnerability of the residential water wells to capture PCE contaminated groundwater was of concern.
A numerical groundwater flow model was developed by the U.S. Geological Survey to assess groundwater flow and advective transport of PCE-contaminated groundwater in the crystalline-rock aquifer of the Milford area. The model (called the area-wide model) encompasses a 26.5-square mile area to allow for more accurate computation of water fluxes near the PCE-contaminated monitoring wells and the residential water wells. Simulations with the area-wide model show that, with the 2016 configuration of residential wells, capture of PCE by the residential water wells appears unlikely for hydrologic conditions typical of 2010 based on steady-state, advective transport modeling. However, simulations also show that adding residential water wells to the north of the PCE-contaminated monitoring wells could affect the transport of PCE. Groundwater withdrawals at other adjacent wells in the overlying Milford-Souhegan glacial-drift aquifer affect advective transport in the crystalline-rock aquifer. Therefore, the potential for future changes in withdrawals in the area, as well as changes in hydrologic conditions, including groundwater recharge and streamflow amounts, should be considered in the remedial assessment process. The development of the area-wide model and linkages established by this study with previously developed Milford-Souhegan glacial-drift aquifer transport models will help facilitate the development of remedial strategies for this Superfund site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205137","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the New Hampshire Department of Environmental Services","usgsCitation":"Harte, P.T., 2021, Numerical modeling of groundwater flow in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire: U.S. Geological Survey Scientific Investigations Report 2020–5137, 47 p., https://doi.org/10.3133/sir20205137.","productDescription":"Report: ix, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-036649","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391937,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205137/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":391330,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5137/sir20205137.XML"},{"id":391326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5137/coverthb.jpg"},{"id":391329,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5137/images/"},{"id":391328,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J102FK","text":"USGS data release","linkHelpText":"MODFLOW -2005, MODPATH, and MOC3D used for groundwater flow simulation, pathlines analysis, and solute transport in the crystalline-rock aquifer in the vicinity of the Savage Municipal Water-Supply Well Superfund site, Milford, New Hampshire"},{"id":391327,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5137/sir20205137.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5137"}],"country":"United States","state":"New Hampshire","city":"Milford","otherGeospatial":"Savage Municipal Water-Supply Well Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.78741455078125,\n 42.798675589844414\n ],\n [\n -71.57524108886719,\n 42.798675589844414\n ],\n [\n -71.57524108886719,\n 42.938328528472546\n ],\n [\n -71.78741455078125,\n 42.938328528472546\n ],\n [\n -71.78741455078125,\n 42.798675589844414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Upper Esopus Creek is the primary tributary to the Ashokan Reservoir, part of the New York City water-supply system. Elevated concentrations of suspended sediment and turbidity in the watershed of the creek are of concern for the system.
Water samples were collected through a range of streamflow and turbidity at 14 monitoring sites in the upper Esopus Creek watershed for analyses of suspended-sediment concentration (SSC) and measurements of turbidity. Analyses of the samples provided data that were used to develop cross-section coefficients and turbidity-SSC regression equations for the monitoring sites for the period October 2016 through September 2019. The equations can be used to estimate SSC at a 15-minute timestep for the monitored sites. The equations can be validated for future use by the collection and analysis of additional data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211065","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Siemion, J., Bonville, D.B., McHale, M.R., and Antidormi, M.R., 2021, Turbidity–suspended-sediment concentration regression equations for monitoring stations in the upper Esopus Creek watershed, Ulster County, New York, 2016–19: U.S. Geological Survey Open-File Report 2021–1065, 27 p., https://doi.org/10.3133/ofr20211065.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120199","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":391805,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MV3NZ8","text":"USGS data release","linkHelpText":"Suspended-sediment concentration and turbidity data for sites in the upper Esopus Creek watershed New York, 2016–19"},{"id":391807,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1065/images"},{"id":391804,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.pdf","text":"Report","size":"2.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1065"},{"id":391806,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1065/ofr20211065.XML"},{"id":391803,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1065/coverthb.jpg"}],"country":"United States","state":"New York","county":"Ulster County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-73.9109,42.1271],[-73.916,42.1199],[-73.9245,42.1019],[-73.9311,42.082],[-73.93,42.0765],[-73.9302,42.0679],[-73.9341,42.0575],[-73.937,42.0398],[-73.9347,42.0293],[-73.9331,42.0216],[-73.9436,41.9913],[-73.9504,41.9664],[-73.9556,41.9528],[-73.9551,41.9464],[-73.954,41.9401],[-73.9567,41.9301],[-73.9625,41.9179],[-73.9639,41.9138],[-73.9609,41.9088],[-73.9423,41.8827],[-73.9389,41.8704],[-73.939,41.8654],[-73.9423,41.8596],[-73.9448,41.8559],[-73.9461,41.851],[-73.9477,41.8346],[-73.9463,41.8142],[-73.9504,41.7979],[-73.9488,41.7847],[-73.946,41.7719],[-73.9414,41.7592],[-73.9408,41.7592],[-73.938,41.7469],[-73.9389,41.7337],[-73.9424,41.7142],[-73.9439,41.6993],[-73.9411,41.6884],[-73.9513,41.6149],[-73.9525,41.59],[-73.9999,41.5855],[-74.0521,41.5816],[-74.0575,41.5926],[-74.0677,41.604],[-74.0886,41.5988],[-74.0983,41.6089],[-74.1246,41.6133],[-74.1325,41.6152],[-74.1282,41.5833],[-74.1858,41.5944],[-74.187,41.5908],[-74.1907,41.5913],[-74.2458,41.6036],[-74.25,41.6059],[-74.2502,41.6291],[-74.2606,41.6337],[-74.2667,41.6324],[-74.2754,41.6284],[-74.281,41.6257],[-74.2989,41.6182],[-74.3156,41.6115],[-74.3187,41.6084],[-74.3404,41.5954],[-74.3521,41.5982],[-74.3583,41.5938],[-74.3675,41.5916],[-74.3681,41.5961],[-74.3705,41.597],[-74.3736,41.5975],[-74.376,41.5994],[-74.3772,41.6044],[-74.3807,41.6117],[-74.3843,41.6167],[-74.3873,41.6217],[-74.3884,41.6299],[-74.392,41.6345],[-74.3926,41.6399],[-74.3943,41.6458],[-74.4004,41.6486],[-74.4449,41.6726],[-74.4833,41.6942],[-74.5755,41.7453],[-74.4892,41.8377],[-74.4573,41.8747],[-74.5124,41.8992],[-74.6363,41.9542],[-74.7235,41.9915],[-74.78,42.0182],[-74.667,42.0697],[-74.5538,42.1212],[-74.5312,42.1464],[-74.504,42.1449],[-74.4516,42.1694],[-74.3077,42.1142],[-74.2496,42.1095],[-74.0767,42.0968],[-74.0424,42.1682],[-74.0259,42.1621],[-74.0054,42.1642],[-74.0038,42.18],[-73.9189,42.1286],[-73.9109,42.1271]]]},\"properties\":{\"name\":\"Ulster\",\"state\":\"NY\"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product.
Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing.
The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics.
The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing.
Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both.
More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland.
China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent.
This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues.
The GCEP30 product, which can be browsed at full resolution at www.croplands.org, has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1868","usgsCitation":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D., 2021, Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud: U.S. Geological Survey Professional Paper 1868, 63 p., https://doi.org/10.3133/pp1868.","productDescription":"Report: ix, 63 p.; Dataset","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119164","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":391888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1868/pp1868.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1868/covrthb.jpg"},{"id":391890,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/","text":"Associated data","linkHelpText":"- Release of GFSAD 30 meter Cropland Extent Products"}],"contact":"Director,
Western Geographic Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Inland recreational fishing, defined as primarily leisure-driven fishing in freshwaters, is a popular pastime in the USA. State natural resource agencies endeavor to provide high-quality and sustainable fishing opportunities for anglers. Managers often use creel and other angler survey data to inform state- and waterbody-level management efforts. Despite the broad implementation of angler surveys and their importance to fisheries management at state scales, regional and national coordination among these activities is minimal, limiting data applicability for larger-scale management practices and research. Here, we introduce the U.S. Inland Creel and Angler Survey Catalog (CreelCat), a first-of-its-kind, publicly available national database of angler survey data that establishes a baseline of national inland recreational fishing metrics. We highlight research and management applications to help support sustainable inland recreational fishing practices, consider cautions, and make recommendations for implementation.
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In areas where it has been introduced by humans, American beaver (Castor canadensis) is an iconic example of such species due to its capacity to alter trophic dynamics of entire ecosystems and create new invasional pathways for other non-native species. The species is problematic in several watersheds within the Southern California-Northern Baja California Coast Ecoregion, a recognized hotspot of biodiversity, due to its ability to modify habitat in ways that favor invasive predators and competitors over the region's native species and habitat. Beaver was deliberately introduced across California in the mid-1900s and generally accepted as non-native to the region up to the early 2000s; however, articles promoting the idea that beaver may be a natural resident have gained traction in recent years, due in large part to the species' charismatic nature rather than by presentation of sound evidence. Here, we discuss the problems associated with beaver disturbance and its effects on conserving the region's native fauna and flora. We refute arguments underlying the claim that beaver is native to the region, and review paleontological, zooarchaeological, and historical survey data from renowned field biologists and naturalists over the past ~160 years to show that no evidence exists that beaver arrived by any means other than deliberate human introduction. Managing this ecosystem engineer has potential to reduce the richness and abundance of other non-native species because the novel, engineered habitat now supporting these species would diminish in beaver-occupied watersheds. At the same time, hydrologic functionality would shift toward more natural, ephemeral conditions that favor the regions' native species while suppressing the dominance of the most insidious invaders.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2021.752400","usgsCitation":"Richmond, J.Q., Swift, C.C., Wake, T.A., Brehme, C.S., Preston, K.L., Kus, B., Ervin, E., Tremor, S., Matsuda, T., and Fisher, R.N., 2021, Impacts of a non-indigenous ecosystem engineer, the American beaver (Castor canadensis), in a biodiversity hotspot: Frontiers in Conservation Science, v. 2, p. 1-14, https://doi.org/10.3389/fcosc.2021.752400.","productDescription":"752400, 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-134539","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":450174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2021.752400","text":"Publisher Index Page"},{"id":395531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": 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California","active":true,"usgs":false}],"preferred":false,"id":833408,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matsuda, Tritia 0000-0001-9271-7671","orcid":"https://orcid.org/0000-0001-9271-7671","contributorId":213956,"corporation":false,"usgs":true,"family":"Matsuda","given":"Tritia","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833409,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833410,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227105,"text":"70227105 - 2021 - Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts","interactions":[],"lastModifiedDate":"2021-12-29T13:56:35.368279","indexId":"70227105","displayToPublicDate":"2021-11-18T07:55:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts","docAbstract":"• There are three general stages of a well's life on US public land: 1) the permitting process to drill, 2) active extraction of fossil fuel resource, and 3) plugging and abandonment of well.
• There is no national standard for oil and gas reclamation in the United States similar to mining and therefore current reclamation practices and standards fail to achieve long-term effectiveness across the western United States.
• A reclaimed well pad's land potential is determined by 3 properties: static (e.g., climate), dynamic (e.g., soil stability), and process (e.g., water retention).
• Understanding a reclaimed well pad's land potential enables federal land agencies to outline surface reclamation goals and requirements consistently and clearly.
• Monitoring for land potential increases the capacity of the private industry to practice adaptive management by enabling companies to respond to plant community changes while maintaining long-term progress toward recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.10.004","usgsCitation":"Di Stefano, S., Karl, J.W., Duniway, M.C., Heinse, R., Hulet, A., and Wulfhorst, J., 2021, Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts: Rangelands, v. 43, no. 6, p. 211-221, https://doi.org/10.1016/j.rala.2021.10.004.","productDescription":"11 p.","startPage":"211","endPage":"221","ipdsId":"IP-126162","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498665,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/671287","text":"External Repository"},{"id":393569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Di Stefano, Sean","contributorId":270642,"corporation":false,"usgs":false,"family":"Di Stefano","given":"Sean","email":"","affiliations":[{"id":56192,"text":"Department of Forest, Rangeland, and Fire Sciences, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":829645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heinse, Robert","contributorId":270646,"corporation":false,"usgs":false,"family":"Heinse","given":"Robert","email":"","affiliations":[{"id":56192,"text":"Department of Forest, Rangeland, and Fire Sciences, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hulet, April","contributorId":270647,"corporation":false,"usgs":false,"family":"Hulet","given":"April","affiliations":[{"id":56192,"text":"Department of Forest, Rangeland, and Fire Sciences, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wulfhorst, J.D.","contributorId":270648,"corporation":false,"usgs":false,"family":"Wulfhorst","given":"J.D.","email":"","affiliations":[{"id":56193,"text":"Department of Natural Resources and Society, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829649,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70225535,"text":"sir20215069 - 2021 - Depth of groundwater used for drinking-water supplies in the United States","interactions":[],"lastModifiedDate":"2021-11-18T23:19:01.5908","indexId":"sir20215069","displayToPublicDate":"2021-11-18T06:53:01","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5069","displayTitle":"Depth of Groundwater Used for Drinking-Water Supplies in the United States","title":"Depth of groundwater used for drinking-water supplies in the United States","docAbstract":"Groundwater supplies 35 percent of drinking water in the United States. Mapping the quantity and quality of groundwater at the depths used for potable supplies requires an understanding of locational variation in the characteristics of drinking-water wells (depth and open interval). Typical depths of domestic- and public-drinking-water supply wells vary by and within aquifer across the United States. The depths to the top and bottom of the zones from which drinking water is withdrawn are important predictor variables in regional- and national-scale statistical water models, but spatially extensive maps of the depths to drinking-water-supply sources are not consistently available in modeled regions. Therefore, it was necessary to generate a set of grids representing surfaces of the approximate common depth and length of open intervals in the wells from which water is withdrawn for domestic- and public-drinking-water supply (withdrawal zones) within the conterminous United States.
Well data (about 7.6 million records) were compiled from several sources, including the U.S. Geological Survey’s National Water Information System (600,922 records), the U.S. Environmental Protection Agency’s Safe Drinking Water Information System dataset (66,540 records, primarily public-supply wells), a groundwater ambient monitoring dataset (31,448 records, primarily domestic-supply wells), individual State data (6,096,503 records), a national brackish aquifer study (96,885 records), and a glacial aquifer study (729,564 records).
Fifty-seven principal aquifers and 65 secondary hydrogeologic regions have been designated in the conterminous United States. The principal aquifers and secondary hydrogeologic regions vary in depth, thickness, lithology, and transmissivity characteristics. Some principal aquifers underlie secondary hydrogeologic regions, and may in turn be overlain by glacial sediment or basin and valley fill aquifers, which may also be used as drinking-water sources. The principal aquifer and secondary hydrogeologic region polygons were merged with overlying sediment polygons, where present, including glacial sediment, coarse glacial sediment, and stream valley alluvium (alluvium) polygons, to generate unique hydrogeologic settings across the conterminous United States. A total of 288 distinct hydrogeologic settings resulted from the merging of principal aquifer, secondary hydrogeologic region, glacial sediment, coarse glacial sediment, and alluvium polygons.
Each well was assigned to a hydrogeologic setting on the basis of location. Hydrogeologic setting well groupings were used to guide calculations of the median value for well depth and depth to and length of open intervals across the hydrogeologic setting. Where well data were sparse or missing, wells from hydrogeologic settings with similar well construction properties, geology, physiography, and topography were grouped and used to calculate the moving median depth (if less than five wells in a 100-kilometer [62.1-mile] radius) and to estimate open interval length (if not available within hydrogeologic setting). Grids were generated to represent what might be considered as the “typical” or “median” domestic- and public-supply well in an area. The well properties are defined with moving median grids of top depth, bottom depth, and length of open interval at a 1-square-kilometer (0.38-square-mile) grid cell scale.
Median depths and open intervals of domestic- and public-supply wells varied by lithology of the hydrogeologic setting and overlying sediment. Overall, the median depths were 142 feet (43.3 meters) for all domestic-supply wells and 202 feet (61.6 meters) for all public-supply wells. The median open intervals were 21 feet (6.4 meters) for domestic-supply wells and 49 feet (14.9 meters) for public-supply wells. The shallowest median bottom open interval depths for domestic-supply wells were in the secondary hydrogeologic regions with coarse glacial sediment, which suggests that the wells are most commonly completed in the permeable coarse glacial sediment and not in the underlying secondary hydrogeologic region. Public-supply wells were completed at relatively shallow median depths when drilled in permeable sediment that overlie secondary hydrogeologic regions. When public-supply wells were completed in principal aquifers, the median depths were typically greater than wells completed in secondary hydrogeologic regions.
Well data used in this study were limited to those available from national or State digital databases. Several quality-assurance checks were performed during data compilation, but a comprehensive quality assurance inspection for each of the data sources was outside the scope of this study. Grids defining typical open intervals in domestic- and public-supply wells are presented. Although there are many places where multiple aquifers are stacked, these results correspond primarily to the aquifer with the highest documented number of wells for each use.
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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The Kirkwood–Cohansey aquifer in southern New Jersey is an important source of drinking-water supplies, but the availability of the resource is limited in some areas by high concentrations of radium, a potential carcinogen at elevated concentrations. Radium (226Ra plus 228Ra) concentrations from a network of 25 drinking-water wells showed a statistically significant increase over a decadal time scale (p < 0.05), with a median increase of 0.35 picocuries per liter. Increases in Ra are correlated with road-salt application rates, and we hypothesize that the correlation is causal. Geochemical processes associated with road-salt applications that can mobilize Ra into solution include competition by excess sodium for sorption sites and formation of chloride complexes (RaCl+ and RaCl2). The largest increases in Ra were in groundwater with low pH (≤5), which is an indirect surrogate for low cation-sorption capacity. Correlations with other potential anthropogenic causes for the increase in Ra were not observed, further suggesting a road-salt effect. Given the significant increase in Ra concentrations in this drinking-water source, the known carcinogenic risks from Ra, the direct link to road-salt application, and the likelihood for continued increases, additional monitoring is necessary in areas with similar hydrogeologic and geochemical settings.
Arid and semiarid ecosystems drive year-to-year variability in the strength of the terrestrial carbon (C) sink, yet there is uncertainty about how soil C gains and losses contribute to this variation. To address this knowledge gap, we embedded C-depleted soil mesocosms, containing litter or biocrust C inputs, within an in situ dryland ecosystem warming experiment. Over the course of one year, changes in microbial biomass and total soil organic C pools were monitored alongside hourly measurements of soil CO2 flux. We also developed a biogeochemical model to explore the mechanisms that gave rise to observed soil C dynamics. Field data and model simulations demonstrated that water exerted much stronger control on soil biogeochemistry than temperature, with precipitation events triggering large CO2 pulses and transport of litter- and biocrust-derived C into the soil profile. We expected leaching of organic matter would result in steady accumulation of C within the mineral soil over time. Instead, the size of the total organic C pool fluctuated throughout the year, largely in response to microbial growth: increases in the size of microbial biomass were negatively correlated with the quantity of C residing in the top 2 cm, where most biogeochemical changes were observed. Our data and models suggest that microbial responses to precipitation events trigger rapid metabolism of dissolved organic C inputs, which strongly limit accumulation of autotroph-derived C belowground. Accordingly, changes in the magnitude and/or frequency of precipitation events in this dryland ecosystem could have profound impacts on the strength of the soil C sink.
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2021 - Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","interactions":[],"lastModifiedDate":"2021-11-23T13:06:28.021637","indexId":"sir20215109","displayToPublicDate":"2021-11-17T07:15:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5109","displayTitle":"Documentation and Mapping of Flooding From the January and March 2018 Nor’easters in Coastal New England","title":"Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","docAbstract":"In January and March 2018, coastal Massachusetts experienced flooding from two separate nor’easters. To put the January and March floods into historical context, the USGS computed statistical stillwater elevations. Stillwater elevations recorded in January 2018 in Boston (9.66 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 2 and 1 percent (between a 50- and 100-year recurrence interval). Stillwater elevations recorded in March 2018 in Boston (9.17 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 4 and 2 percent (between a 25- and 50-year recurrence interval). Flood maps show that the area inundated by the January storm is slightly more extensive than that of the March storm, reflecting the respective profiles of the two storms. On the basis of a limited dataset, the attenuation of peak water levels was estimated as a function of the hydraulic distance inland and the starting stillwater elevation computed for the flood within 0.6 foot of what was measured in the field. A simple one-dimensional model was calibrated using flood elevation data collected after the January flood, and the results of the model were validated using flood elevation data collected after the March flood to model the attenuation of the flood elevations as the storms move inland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215109","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P.J., Olson, S.A., Sturtevant, L.P., and Kalmon, R.D., 2021, Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England: U.S. Geological Survey Scientific Investigations Report 2021–5109, 13 p., https://doi.org/10.3133/sir20215109.","productDescription":"Report: iv, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125348","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390667,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RINQ4B","text":"USGS data release","linkHelpText":"Data and shapefiles used to document the floods associated with the January and March 2018 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m2) and coarse (100 m2) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under-canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100-m and 1-m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine-scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance.
The U.S. Geological Survey measured cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (anaC, cyrA, taxa specific mcyE, and sxtA), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears (Ursus maritimus). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progressive decline in access to prey or an increase in the energetic cost of accessing prey has the potential to adversely affect polar bear population dynamics. We examined long-term (1983–2015) patterns of spring body condition (indexed using residual body mass) and maternal investment (i.e., litter mass of cubs-of-the-year and yearlings; COY and YRL) of polar bears from Alaska’s southern Beaufort Sea to evaluate potential relationships with regional- and circumpolar-scale sea ice conditions and atmospheric patterns. The length of the summer open-water (OW) season (i.e., the period of time the sea ice is mostly absent from the continental shelf) increased at a rate of 18 days decade-1 over the study period. However, the OW season duration was not a strong determinant of spring residual body mass or litter mass. Residual body mass of independent (i.e., subadults and adults) female bears varied relative to age class, reproductive status, and the strength of the prior winter’s Arctic Oscillation (i.e., a circumpolar-scale mode of climate variability driven by long-term atmospheric patterns). Spring residual mass of independent males varied with age class and variation in wind speed (i.e., regional-scale short-term atmospheric patterns) during the winter of the year preceding capture. Over the study period, mean annual body mass of adult females unaccompanied by COY declined by 4 kg/ decade-1, while no temporal trends were evident in the mean annual body mass of adult females with COY, adult males, and subadults. Litter mass of COY varied relative to capture date, maternal age class and mass, litter size, and year of capture. Litter mass of YRL varied with capture date, maternal age class and mass, litter size, variation in winter wind speed (the year of and year preceding capture), and the strength of the prior winter’s Arctic Oscillation. Mean annual litter mass of COY decreased at a rate of 2.6 kg decade-1 and declined 0.68 kg for every 10 kg reduction in maternal mass. No trend was evident in the mean annual litter mass of yearlings. These findings suggest a nuanced response of the southern Beaufort Sea polar bears to environmental change, where some demographic groups (e.g., adult males and subadults) are presently more resilient than others to changes in the Arctic marine ecosystem.
Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology