Aim
Climate change is affecting the distribution of species and subsequent biotic interactions, including hybridization potential. The imperiled Golden-winged Warbler (GWWA) competes and hybridizes with the Blue-winged Warbler (BWWA), which may threaten the persistence of GWWA due to introgression. We examined how climate change is likely to alter the breeding distributions and potential for hybridization between GWWA and BWWA.
Location
North America.
Methods
We used GWWA and BWWA occurrence data to model climatically suitable conditions under historical and future climate scenarios. Models were parameterized with 13 bioclimatic variables and 3 topographic variables. Using ensemble modeling, we estimated historical and modern distributions, as well as a projected distribution under six future climate scenarios. We quantified breeding distribution area, the position of and amount of overlap between GWWA and BWWA distributions under each climate scenario. We summarized the top explanatory variables in our model to predict environmental parameters of the distributions under future climate scenarios relative to historical climate.
Results
GWWA and BWWA distributions are projected to substantially change under future climate scenarios. GWWA are projected to undergo the greatest change; the area of climatically suitable breeding season conditions is expected to shift north to northwest; and range contraction is predicted in five out of six future climate scenarios. Climatically suitable conditions for BWWA decreased in four of the six future climate scenarios, while the distribution is projected to shift east. A reduction in overlapping distributions for GWWA and BWWA is projected under all six future climate scenarios.
Main Conclusions
Climate change is expected to substantially alter the area of climatically suitable conditions for GWWA and BWWA, with the southern portion of the current breeding ranges likely to become climatically unsuitable. However, interactions between BWWA and GWWA are expected to decline with the decrease in overlapping habitat, which may reduce the risk of genetic introgression.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13659","usgsCitation":"Hightower, J.N., Crawford, D.L., Thogmartin, W.E., Aldinger, K.R., Barker Swarthout, S., Buehler, D.A., Confer, J., Friis, C., Larkin, J., Lowe, J.D., Piorkowski, M., Rohrbaugh, R., Rosenberg, K.V., Smalling, C.G., Wood, P.B., Vallender, R., and Roth, A.M., 2023, Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers: Diversity and Distributions, v. 29, no. 2, p. 254-271, https://doi.org/10.1111/ddi.13659.","productDescription":"18 p.","startPage":"254","endPage":"271","ipdsId":"IP-138007","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":444642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13659","text":"Publisher Index 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observations","interactions":[],"lastModifiedDate":"2024-05-20T13:49:30.008054","indexId":"70239954","displayToPublicDate":"2023-02-01T07:04:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Toward consistent change detection across irregular remote sensing time series observations","docAbstract":"The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.
Droughts that are hotter, more frequent, and last longer; pest outbreaks that are more extensive and more common; and fires that are more frequent, more extensive, and perhaps more severe have raised concern that forests in the western United States may not return once disturbed by one or more of these agents. Numerous field-based studies have been undertaken to better understand forest response to these changing disturbance regimes. Meta-analyses of these studies provide broad guidelines on the biotic and abiotic factors that hinder forest recovery, but study-to-study differences in methods and objectives do not support estimation of the total extent of potentially impaired forest succession. In this research, we provide an estimate of the area of potentially impaired forest succession. The estimate was derived from modeling of an 18-year land cover and Normalized Difference Vegetation Index (NDVI) time series supported by an extensive ancillary dataset. We estimate an upper bound of approximately 3470 km2 of disturbed forest that may not return or reattain prior composition and structure. Based on the data used, fire appears to be the main disturbance agent of impaired forest succession, although climatic factors cannot be discounted. The numerous field studies routinely cite distal seed sources as a factor that hinders forest recovery, and we estimate that 20 % of the upper bound estimate has no forest cover within a 4.4-ha neighborhood. Our upper bound estimate is about 0.5 % of the 2001 mapped extent of western United States forests. The estimate is cognizant of measurement and modeling uncertainties (i.e., upper bound) and uncertainties related to successional rates and trajectories (i.e., potential).
Stocking of hatchery-reared fishes has been used with variable success as a management action to promote the recovery of populations and species. The practice has been controversial for several reasons, including uncertainty about whether the hatchery rearing experience may affect reproduction after release. Fine-scale acoustic telemetry was used during three spawning seasons to test whether hatchery rearing affects the reproductive behavior of lake trout using a spawning shoal complex in northern Lake Huron. Within sex, wild- and hatchery-reared fish behaved similarly, but significant behavioral differences occurred between sexes. Lake trout of both sexes moved synchronously onto the spawning shoals at the completion of autumn thermal turnover and occupied the same spawning sites (confirmed visually by presence of fertilized eggs) on the shoals. Male lake trout tended to congregate directly on spawning sites, with duration of occupancy varying greatly among years. Female lake trout spent less time on spawning shoals than males and congregated less at spawning sites on shoals. Most fish visited multiple spawning sites among shoals per season, with many making multiple transits among individual spawning sites. We found no evidence to support the hypothesis that hatchery rearing impairs spawning behavior of lake trout and, therefore, conclude that behavior deficiencies on the spawning ground are likely not an impediment to rehabilitation of lake trout in northern Lake Huron. Our study narrows the field of possible impediments to lake trout rehabilitation in the Great Lakes and provides insights that expand the conceptual model of lake trout spawning behavior.
Carlin-type gold deposits (CTDs) of Nevada are the largest producers of gold in the United States, a leader in world gold production. Although much has been resolved about the characteristics and origin of CTDs in Nevada, major questions remain, especially about (1) the role of magmatism, whether only a source of heat or also metals, (2) whether CTDs only formed in the Eocene, and (3) whether pre-Eocene metal concentrations contributed to Eocene deposits. These issues are exemplified by the CTDs of the Cortez region, the second largest concentration of these deposits after the Carlin trend.
Carlin-type deposits are notoriously difficult to date because they rarely generate dateable minerals. An age can be inferred from crosscutting relationships with dated dikes and other intrusions, which we have done for the giant Cortez Hills CTD. What we term “Cortez rhyolites” consist of two petrographic-geochemical groups of siliceous dikes: (1) quartz-sanidine-plagioclase-biotite-phyric, high-SiO2 rhyolites emplaced at 35.7 Ma based on numerous 40Ar/39Ar dates and (2) plagioclase-biotite-quartz ± hornblende-phyric, low-SiO2 rhyolites, which probably were emplaced at the same time but possibly as early as ~36.2 Ma. The dikes form a NNW-trending belt that is ~6 to 10 km wide × 40 km long and centered on the Cortez Hills deposit, and they require an underlying felsic pluton that fed the dikes. Whether these dikes pre- or postdated mineralization has been long debated. We show that dike emplacement spanned the time of mineralization. Many of both high- and low-SiO2 dikes are altered and mineralized, although none constitute ore. In altered-mineralized dikes, plagioclase has been replaced by kaolinite and calcite, and biotite by smectite, calcite, and marcasite. Sanidine is unaltered except in a few samples that are completely altered to quartz and kaolinite. Sulfides present in mineralized dikes are marcasite, pyrite, arsenopyrite, and As-Sb–bearing pyrite. Mineralized dikes are moderately enriched in characteristic Carlin-type elements (Au, Hg, Sb, Tl, As, and S), as well as elements found in some CTDs (Ag, Bi, Cu, Mo), and variably depleted in MgO, CaO, Na2O, K2O, MnO, Rb, Sr, and Ba. In contrast, some high-SiO2 rhyolites are unaltered and cut high-grade ore, which shows that they are post-ore. Both mineralized and post-ore dikes have indistinguishable sanidine 40Ar/39Ar dates. These characteristics, along with published interpretations that other giant CTDs formed in a few tens of thousands of years, indicate the Cortez Hills CTD formed at 35.7 Ma. All Cortez-area CTDs are in or adjacent to the Cortez rhyolite dike swarm, which suggests that the felsic pluton that fed the dikes was the hydrothermal heat source. Minor differences in alteration and geochemistry between dikes and typical Paleozoic sedimentary rock-hosted ore probably reflect low permeability and low reactivity of the predominantly quartzofeldspathic dikes.
Despite widespread pre-35.7 Ma mineralization in the Cortez region, including deposits near several CTDs, we find no evidence that older deposits or Paleozoic basinal rocks contributed metals to Cortez-area CTDs. Combining our new information about the age of Cortez Hills with published and our dates on other CTDs demonstrates that CTD formation coincided with the southwestern migration of magmatism across Nevada, supporting a genetic relationship to Eocene magmatism. CTDs are best developed where deep-seated (~6–8 km), probably granitic plutons, expressed in deposits only as dikes, established large, convective hydrothermal systems.
Harmful algal blooms (HABs) are a recurring problem in many temperate large lake and coastal marine ecosystems, caused mainly by anthropogenic eutrophication. Implementation of agricultural conservation practices (ACPs) offers a means to reduce non-point source nutrient runoff and mitigate HABs. However, the effectiveness of ACPs in a changing climate remains uncertain. We used an integrated biophysical modeling approach to predict how Lake Erie cyanobacterial HAB severity (bloom biomass) may change under several climate and ACP implementation scenarios, using western Lake Erie and its largely agricultural watershed as our study system. An ensemble of general circulation model projections was used to drive spatially explicit land use and hydrology models of the Maumee River watershed, the output of which informed a predictive model of Lake Erie HAB severity. Results show that, in the absence of changes in ACPs, the frequency of severe HABs is projected to increase during coming decades, owing to increased inputs of nutrients from the watershed. These anticipated increases are due to increased total precipitation and more frequent higher-magnitude rainfall events. While further implementation of ACPs appears capable of reducing severe HAB events, widespread implementation would be necessary to reduce HAB severity below current management targets. This study highlights how continued climate change will only exacerbate the need for land management practices that can reduce nutrient runoff in agriculturally dominated ecosystems, such as Lake Erie. It also shows how interdisciplinary, biophysical modeling approaches can help identify strategies to mitigate HABs in the face of anthropogenic stressors.
During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality.
The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R2), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit.
In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.
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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Introduction: Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type.
Methods: Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions.
Results: The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters).
Discussion: Our results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable energy.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2023.1078042","usgsCitation":"Arimitsu, M.L., Piatt, J., Thorson, J., Kuletz, K., Drew, G., Schoen, S.K., Cushing, D., Kroeger, C., and Sydeman, W., 2023, Joint spatiotemporal models to predict seabird densities at sea: Frontiers in Marine Science, v. 10, 1078042, 11 p., https://doi.org/10.3389/fmars.2023.1078042.","productDescription":"1078042, 11 p.","ipdsId":"IP-145204","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":444664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2023.1078042","text":"Publisher Index Page"},{"id":412531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.65747250220048,\n 58.811862749051784\n ],\n [\n -148.62920726788076,\n 58.811862749051784\n ],\n [\n -148.62920726788076,\n 61.49890808989605\n ],\n [\n -155.65747250220048,\n 61.49890808989605\n ],\n [\n -155.65747250220048,\n 58.811862749051784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatt, John F. 0000-0002-4417-5748","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":244053,"corporation":false,"usgs":true,"family":"Piatt","given":"John F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorson, James","contributorId":301893,"corporation":false,"usgs":false,"family":"Thorson","given":"James","affiliations":[{"id":65201,"text":"NOAA AFSC","active":true,"usgs":false}],"preferred":false,"id":862947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kuletz, Kathy","contributorId":301894,"corporation":false,"usgs":false,"family":"Kuletz","given":"Kathy","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":862948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drew, Gary 0000-0002-6789-0891","orcid":"https://orcid.org/0000-0002-6789-0891","contributorId":300561,"corporation":false,"usgs":false,"family":"Drew","given":"Gary","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":862949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":862950,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cushing, Dan","contributorId":301895,"corporation":false,"usgs":false,"family":"Cushing","given":"Dan","email":"","affiliations":[{"id":65202,"text":"Pole Star Ecological LLC","active":true,"usgs":false}],"preferred":false,"id":862951,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kroeger, Caitlin","contributorId":301897,"corporation":false,"usgs":false,"family":"Kroeger","given":"Caitlin","email":"","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862952,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sydeman, William","contributorId":301898,"corporation":false,"usgs":false,"family":"Sydeman","given":"William","affiliations":[{"id":35859,"text":"Farallon Institute","active":true,"usgs":false}],"preferred":false,"id":862953,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241025,"text":"70241025 - 2023 - Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","interactions":[],"lastModifiedDate":"2023-03-07T12:47:56.075162","indexId":"70241025","displayToPublicDate":"2023-01-31T06:40:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Can hydrological models benefit from using global soil moisture, evapotranspiration, and runoff products as calibration targets?","docAbstract":"Hydrological models are usually calibrated to in-situ streamflow observations with reasonably long and uninterrupted records. This is challenging for poorly gage or ungaged basins where such information is not available. Even for gaged basins, the single-objective calibration to gaged streamflow cannot guarantee reliable forecasts because, as has been documented elsewhere, the inverse problem is mathematically ill-posed. Therefore, the inclusion of other observations, and the reproduction of other hydrological variables beyond streamflow, become critical components of accurate hydrological forecasting. In this study, six single- and multi-objective model calibration schemes based on different combinations of gaged streamflow, global-scale gridded soil moisture, actual evapotranspiration (ET), and runoff products are used for the calibration of a process-based hydrological model for 20 catchments located within the Lake Michigan watershed, of the Laurentian Great Lakes. Results show that the addition of gridded soil moisture to gaged streamflow in model calibration improves the ET simulation performance for most of the catchments, leading to the overall best-performing models. The monthly streamflow simulation performance for the experiments using gridded runoff products to inform the model is outperformed by those using the gaged streamflow, but the discrepancy is mitigated with increasing catchment scale. A new visualization method that effectively synthesizes model performance for the simulations of streamflow, soil moisture, and ET was also proposed. Based on the method, it is revealed that the streamflow simulation performance is relatively weak for baseflow-dominated catchments; overall, the 20 catchment models simulate streamflow and ET better than soil moisture.
The Kansas River and its associated alluvial aquifer provide drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical processes to treat and remove contaminants before public distribution. An early-notification system of changing water-quality conditions allows water suppliers to proactively make decisions that affect water treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, began collecting water-quality data at the Kansas River above Topeka Weir at Topeka, Kansas (USGS site 06888990, hereafter referred to as the “Topeka site”), during November 2018 to develop linear regression models that relate continuous in situ water-quality sensor measurements to discretely sampled water-quality constituent concentrations or densities. The addition of the Topeka site expanded an existing water-quality monitoring network, which included the upstream Kansas River at Wamego, Kans., and downstream Kansas River at De Soto, Kans., sites. Linear regression analysis was used to develop models that compute real-time concentrations or densities for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a, total suspended solids, suspended sediment, and Escherichia coli at the Topeka site using data collected during November 2018 through June 2021. Water-quality constituent concentrations or densities computed from the models documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov), are useful to the public for cultural and recreational purposes, and can be used to guide water-treatment processes, compare conditions with Federal and State water-quality criteria, and characterize changes in Kansas River water-quality conditions through time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225130","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., 2023, Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021: U.S. Geological Survey Scientific Investigations Report 2022–5130, 14 p., https://doi.org/10.3133/sir20225130.","productDescription":"Report: vii, 14 p.; 14 Appendixes; Dataset","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141414","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":412468,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225130/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412446,"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":412445,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5130/downloads","text":"Appendixes 1–14","linkFileType":{"id":1,"text":"pdf"}},{"id":412443,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500484,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114304.htm","linkFileType":{"id":5,"text":"html"}},{"id":412442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5130/sir20225130.pdf","text":"Report","size":"5.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5130"},{"id":412441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5130/coverthb.jpg"},{"id":412444,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5130/images"}],"country":"United States","state":"Kansas","city":"Topeka","otherGeospatial":"Kansas River, Topeka Weir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.66443090940162,\n 39.06286764035286\n ],\n [\n -95.67191642810837,\n 39.06117240426602\n ],\n [\n -95.64218228435746,\n 39.06262546626132\n ],\n [\n -95.64239021543237,\n 39.064239944513304\n ],\n [\n -95.63864745607941,\n 39.07925282377511\n ],\n [\n -95.63812762839112,\n 39.08401430643988\n ],\n [\n -95.63251348936144,\n 39.0836915042114\n ],\n [\n -95.63199366167355,\n 39.08054410506932\n ],\n [\n -95.61452745135817,\n 39.07780010407046\n ],\n [\n -95.61463141689583,\n 39.07465244209993\n ],\n [\n -95.6078736569523,\n 39.057620349038984\n ],\n [\n -95.60225951792262,\n 39.05794327053502\n ],\n [\n -95.59716520658046,\n 39.05826619055418\n ],\n [\n -95.58572899744581,\n 39.0606880436211\n ],\n [\n -95.58884796357322,\n 39.063432710002644\n ],\n [\n -95.59300658507682,\n 39.070778203846714\n ],\n [\n -95.59092727432522,\n 39.08958241237616\n ],\n [\n -95.60589831173833,\n 39.09127696606694\n ],\n [\n -95.60849745017785,\n 39.08732294411979\n ],\n [\n -95.61702262426043,\n 39.08691946002881\n ],\n [\n -95.62222090113984,\n 39.08611248492275\n ],\n [\n -95.63532055887627,\n 39.090712119361\n ],\n [\n -95.64166245666956,\n 39.08917894121052\n ],\n [\n -95.65101935505261,\n 39.08361080342297\n ],\n [\n -95.65663349408229,\n 39.076266645265974\n ],\n [\n -95.66058418451098,\n 39.067145912321024\n ],\n [\n -95.66443090940162,\n 39.06286764035286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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1217 Biltmore Drive
Lawrence, KS 66049
The U.S. Geological Survey (USGS) is initiating a study approach focused on building cross-disciplinary connections to weave together the scientific knowledge related to drought conditions and effects in the Colorado River Basin. The basin is experiencing the worst drought in recorded history, posing unprecedented new challenges in the basin and in areas relying on water from the basin. Science is continually advancing, and there is an increasing need to interpret the connections between studies to predict the effects of drought and other changes affecting the Earth system. The USGS primarily works in independent disciplines and science centers to provide cutting-edge science to advance research and science applications worldwide. The complexity and volume of research that has been conducted related to drought in the Colorado River Basin is difficult to quantify. To complicate matters, studies, models, and datasets are cataloged and may be available in multiple, unrelated locations, across various internal systems, data repositories, and local offices. Furthermore, there are limited interactions and interfaces between scientists and partners working in different science disciplines; in many cases, individual science products require stakeholders to integrate complex interdisciplinary data across geographical and topical extents. The diverse array of interdisciplinary science and science products produced by the USGS highlights the need for a wide ranging collaborative support structure.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20223016","usgsCitation":"Dahm, K.G., Jones, D.K., Anderson, P.J., Dick, M.C., Hawbaker, T.J., and Horton, R.J., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST): U.S. Geological Survey Fact Sheet 2022–3016, 4 p., https://doi.org/10.3133/fs20223016.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-133121","costCenters":[{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":416352,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223016/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3016"},{"id":416351,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.xml"},{"id":411878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3016/coverthb.jpg"},{"id":411879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3016/fs20223016.pdf","text":"Report","size":"5.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3016"},{"id":416350,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3016/images"}],"country":"Mexico, United Statetes","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.7518896501698,\n 31.333179533551544\n ],\n [\n -108.1826779826668,\n 31.292530391528643\n ],\n [\n -108.22636216977008,\n 31.755580668004285\n ],\n [\n -107.83144654017931,\n 32.82766987491033\n ],\n [\n -107.89423052621623,\n 33.91908118889275\n ],\n [\n -107.60054235862646,\n 34.474546468527535\n ],\n [\n -107.70156965961073,\n 35.43530369556444\n ],\n [\n -106.96299416688146,\n 36.27632977347221\n ],\n [\n -106.49519707134272,\n 36.59537776353493\n ],\n [\n -106.5088572279181,\n 37.05777374754949\n ],\n [\n -106.96751070599771,\n 37.67745762309879\n ],\n [\n -106.2464517524759,\n 38.42849019741902\n ],\n [\n -106.3226667088301,\n 38.8688745253198\n ],\n [\n -105.84832248310391,\n 39.483565795806356\n ],\n [\n -105.6793295391808,\n 39.934832700838456\n ],\n [\n -105.7748145642044,\n 40.35785503337192\n ],\n [\n -106.21930010733132,\n 41.056311809879645\n ],\n [\n -106.42819382446118,\n 41.58215844907255\n ],\n [\n -107.34149070853772,\n 42.33476436232209\n ],\n [\n -108.89445962234083,\n 42.53851660809195\n ],\n [\n -109.80745236050603,\n 43.16088610307435\n ],\n [\n -110.49271378548221,\n 43.40850924462376\n ],\n [\n -111.6838924799572,\n 42.292277027250265\n ],\n [\n -111.99413836336885,\n 41.06269127724076\n ],\n [\n -111.87604170489686,\n 39.799065745689944\n ],\n [\n -112.57737199499377,\n 38.89296778795085\n ],\n [\n -112.78097622152214,\n 38.22122917305467\n ],\n [\n -114.0297938396215,\n 38.10468231708492\n ],\n [\n -114.62100982843458,\n 39.07842450911707\n ],\n [\n -115.69932199571724,\n 39.592082456027185\n ],\n [\n -115.77535041245778,\n 38.53884582465383\n ],\n [\n -115.96430598148137,\n 37.10476019901908\n ],\n [\n -116.19919117971875,\n 36.116449292573606\n ],\n [\n -116.47633007028378,\n 34.83686630451358\n ],\n [\n -116.51856809358253,\n 33.98410239909924\n ],\n [\n -116.31237481460064,\n 33.22592368319175\n ],\n [\n -115.86428179683787,\n 32.56152601033786\n ],\n [\n -115.88456371112946,\n 32.00683378175074\n ],\n [\n -115.57272525296867,\n 31.643556450442958\n ],\n [\n -114.66642714444782,\n 31.597816902553873\n ],\n [\n -113.1842586769861,\n 31.118083077039856\n ],\n [\n -113.25492854485302,\n 30.73760035553252\n ],\n [\n -112.93985982527391,\n 30.184363710503405\n ],\n [\n -110.51663294286107,\n 29.810620687264418\n ],\n [\n -109.58861740954063,\n 30.071266430362215\n ],\n [\n -109.61492936020454,\n 30.42734058413086\n ],\n [\n -109.35938589058884,\n 30.879775471921178\n ],\n [\n -108.4649251498791,\n 30.530080303802123\n ],\n [\n -108.7269534775038,\n 30.809783171064424\n ],\n [\n -108.57530723606152,\n 30.978771390834126\n ],\n [\n -108.7518896501698,\n 31.333179533551544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225
The U.S. Geological Survey (USGS) conducts a wide variety of science that improves understanding of droughts and their effects on ecosystems and society. This work includes data collection and monitoring of aquatic and terrestrial systems; assessment and analysis of patterns, trends, drivers, and impacts of drought; development and application of predictive models; and delivery of information and decision-making tools to stakeholders. Stakeholders, which include Federal, Tribal, State, and local agencies, nongovernmental organizations, and others, use this information to anticipate, assess, react to, and mitigate drought conditions and impacts. There is no obvious near-term solution to reduce the frequency and severity of droughts or to mitigate drought impacts. Multidecadal drought is a “grand challenge” that benefits from integration of existing technologies, data, knowledge, and models across related and disparate disciplines, facilitated by new science and technology. In response, the USGS initiated a new integrated-science approach in the Colorado River Basin in 2020. The Colorado River Basin was specifically selected because of concerns about future drought and its consequences for the region. This document explains how the Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST) project extends and enhances the science supported by USGS Mission Areas and Programs and articulates scientific gaps and stakeholder needs to identify and reduce drought risks. The approach seeks to answer complex Earth science questions developed in partnership with stakeholders about severe long-term drought. An integrated approach is required to tackle these complex questions, which any single science discipline cannot answer on its own.
In addition to increased understanding of drought and drought effects in complex systems, the Colorado River Basin ASIST project was designed to improve efficiencies through rapid location of a broad array of data sources, assembly of model-ready multidisciplinary data, and delivery of actionable science to stakeholders at the speeds and scales needed for decision making. The project team identified the following actions needed for USGS to implement and advance an integrated science approach in the Colorado River Basin: (1) engage with stakeholders to document their needs and iteratively co-produce science and science delivery tools to address these needs, (2) integrate monitoring and observation systems developed by USGS and other agencies that track droughts and their effects, (3) collect and provide analysis-ready data to support integrated applications, (4) integrate data and model connections to predict multiple drought impacts, (5) conduct multidisciplinary coordination to improve interpretations, (6) leverage the knowledge base across USGS to enhance decision making, and (7) support the development of new integrated science approaches and technologies that provide analysis and management tools that can be used to adapt to the effects of drought in the Colorado River Basin. Proposals for initial short-term use-case projects were solicited, a subset of which was selected for funding to test implementation of these actions. Additionally, the project organized and convened a series of science and technology collaboration workshops in the USGS focused on challenges that were identified and prioritized by the short-term use-case projects and during the initial stakeholder analysis. These workshops were designed to bring together diverse perspectives to discuss science and technology challenges, stakeholder needs, capabilities, and knowledge gaps, with the goal of determining how the USGS can address challenges, identify future opportunities for continued engagement between participants, and inform the next steps for the Colorado River Basin ASIST project. Continuing to collaboratively engage with a wide range of stakeholders using an integrated approach will provide a suitable foundation of data and tools to formulate actionable intelligence for predicting droughts and informing adaptation to the effects of long-term drought in a holistic, timely, and effective manner.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1502","usgsCitation":"Dahm, K., Hawbaker, T., Frus, R., Monroe, A., Bradford, J., Andrews, W., Torregrosa, A., Anderson, E., Dean, D., and Qi, S., 2023, Colorado River Basin Actionable and Strategic Integrated Science and Technology Project—Science strategy: U.S. Geological Survey Circular 1502, 57 p., https://doi.org/10.3133/cir1502.","productDescription":"vi, 53 p.","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-130999","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":416723,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1502/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1502"},{"id":411865,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1502/circ1502.xml"},{"id":411861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1502/coverthb.jpg"},{"id":411862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1502/circ1502.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1502"},{"id":411864,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1502/images"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.7518896501698,\n 31.333179533551544\n ],\n [\n -108.1826779826668,\n 31.292530391528643\n ],\n [\n -108.22636216977008,\n 31.755580668004285\n ],\n [\n -107.83144654017931,\n 32.82766987491033\n ],\n [\n -107.89423052621623,\n 33.91908118889275\n ],\n [\n -107.60054235862646,\n 34.474546468527535\n ],\n [\n -107.70156965961073,\n 35.43530369556444\n ],\n [\n -106.96299416688146,\n 36.27632977347221\n ],\n [\n -106.49519707134272,\n 36.59537776353493\n ],\n [\n -106.5088572279181,\n 37.05777374754949\n ],\n [\n -106.96751070599771,\n 37.67745762309879\n ],\n [\n -106.2464517524759,\n 38.42849019741902\n ],\n [\n -106.3226667088301,\n 38.8688745253198\n ],\n [\n -105.84832248310391,\n 39.483565795806356\n ],\n [\n -105.6793295391808,\n 39.934832700838456\n ],\n [\n -105.7748145642044,\n 40.35785503337192\n ],\n [\n -106.21930010733132,\n 41.056311809879645\n ],\n [\n -106.42819382446118,\n 41.58215844907255\n ],\n [\n -107.34149070853772,\n 42.33476436232209\n ],\n [\n -108.89445962234083,\n 42.53851660809195\n ],\n [\n -109.80745236050603,\n 43.16088610307435\n ],\n [\n -110.49271378548221,\n 43.40850924462376\n ],\n [\n -111.6838924799572,\n 42.292277027250265\n ],\n [\n -111.99413836336885,\n 41.06269127724076\n ],\n [\n -111.87604170489686,\n 39.799065745689944\n ],\n [\n -112.57737199499377,\n 38.89296778795085\n ],\n [\n -112.78097622152214,\n 38.22122917305467\n ],\n [\n -114.0297938396215,\n 38.10468231708492\n ],\n [\n -114.62100982843458,\n 39.07842450911707\n ],\n [\n -115.69932199571724,\n 39.592082456027185\n ],\n [\n -115.77535041245778,\n 38.53884582465383\n ],\n [\n -115.96430598148137,\n 37.10476019901908\n ],\n [\n -116.19919117971875,\n 36.116449292573606\n ],\n [\n -116.47633007028378,\n 34.83686630451358\n ],\n [\n -116.51856809358253,\n 33.98410239909924\n ],\n [\n -116.31237481460064,\n 33.22592368319175\n ],\n [\n -115.86428179683787,\n 32.56152601033786\n ],\n [\n -115.88456371112946,\n 32.00683378175074\n ],\n [\n -115.57272525296867,\n 31.643556450442958\n ],\n [\n -114.66642714444782,\n 31.597816902553873\n ],\n [\n -113.1842586769861,\n 31.118083077039856\n ],\n [\n -113.25492854485302,\n 30.73760035553252\n ],\n [\n -112.93985982527391,\n 30.184363710503405\n ],\n [\n -110.51663294286107,\n 29.810620687264418\n ],\n [\n -109.58861740954063,\n 30.071266430362215\n ],\n [\n -109.61492936020454,\n 30.42734058413086\n ],\n [\n -109.35938589058884,\n 30.879775471921178\n ],\n [\n -108.4649251498791,\n 30.530080303802123\n ],\n [\n -108.7269534775038,\n 30.809783171064424\n ],\n [\n -108.57530723606152,\n 30.978771390834126\n ],\n [\n -108.7518896501698,\n 31.333179533551544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS 911
Denver, CO 80225
This study compares the results of two regional steady-state U.S. Geological Survey Modular Three-Dimensional Finite-Difference Ground-Water Flow (MODFLOW) models constructed to quantify the hydrologic changes in the St. Louis River Basin from iron mining on the Mesabi Iron Range in northeastern Minnesota. The U.S. Geological Survey collaborated in this study with bands of the Minnesota Chippewa Tribe, and the Minnesota Pollution Control Agency to inform management decisions about aquatic resources in the St. Louis River Basin. A model constructed and calibrated to represent average 1995–2015 mining conditions produced regional groundwater heads and flows. A pre-mining scenario model was constructed from this mining model but had the land and bedrock surfaces restored to pre-mining topographies and had modeled mining features (mine pits, tailings basins, waste-rock piles, and mining-disturbed areas) eliminated to represent general pre-mining stratigraphy and hydrogeology. Many of the features important to the hydrology of this mining area (like individual mine pits) are difficult to represent in groundwater models and required the use of modeling tools to indirectly account for their effects. The difference between the results of these two models represents mining’s effects on the hydrology in the Mesabi Iron Range area of the St Louis River Basin. The mining and pre-mining regional models also can provide boundary conditions and initial properties for future local or site-specific groundwater-flow models in the area.
Total groundwater flow through the mining model is 171 million cubic feet per day. Areal recharge is the largest source of groundwater (78 and 81 percent of total groundwater flow in the mining and pre-mining scenario models, respectively). Seepage from streams and lakes provides another 17 percent of the total groundwater flow through both models. Water leaves aquifers through seepage to streams (discharge as base flow, 43 percent in both models) and areal seepage to the land surface (surface seepage), for example to wetlands (45 and 49 percent, mining and pre-mining scenario models respectively).
Comparison of the results from the mining and pre-mining scenario models shows that iron mining has produced measurable hydrologic changes in the St. Louis River Basin, but that most of those changes and the highest magnitude changes occur near the mining features. Flow changes to and from surface-water bodies like streams and wetlands were analyzed in detail because of their importance in sustaining surface waters and aquatic life. Overall, groundwater flow in the mining model was 3.62 million cubic feet per day (2.2 percent) greater than total pre-mining model groundwater flow. This was caused by an increase in recharge from tailings basins and a decrease in discharge from surface seepage. Groundwater discharge to mine pits was the largest change in groundwater flows between the models (a change representing 2.8 percent of total pre-mining model groundwater flow). Net recharge to groundwater from tailings basins (2.4 percent), net decrease in surface seepage from groundwater (2.7 percent), and net increase in seepage to streams (1.0 percent) were all in this same range of total pre-mining model groundwater flow. Groundwater lost through mine-pit withdrawals was nearly offset by groundwater gained through recharge from tailings basins. However, because losses and gains occurred in different areas, the effect of mining can have more substantial effects on local areas than the model-wide averages represent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225124","collaboration":"Prepared in cooperation with bands of the Minnesota Chippewa Tribe, the Great Lakes Indian Fish & Wildlife Commission, and the Minnesota Pollution Control Agency","usgsCitation":"Cowdery, T.K., Baker, A.C., Haserodt, M.J., Feinstein, D.T., and Hunt, R.J., 2023, Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota: U.S. Geological Survey Scientific Investigations Report 2022–5124, 59 p., https://doi.org/10.3133/sir20225124.","productDescription":"Report: viii, 59 p.; 2 Data Releases","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-122102","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":412380,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412376,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5124/images"},{"id":412373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5124/coverthb.jpg"},{"id":412374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5124"},{"id":500466,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114303.htm","linkFileType":{"id":5,"text":"html"}},{"id":412504,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225124/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412378,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","linkHelpText":"Soil-water-balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995–2010"},{"id":412377,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6KSBJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT simulations of regional groundwater flow under mining and pre-mining scenarios near the Mesabi Iron Range within the St. Louis River Basin, northeastern Minnesota"}],"country":"United States","state":"Minnesota","otherGeospatial":"Mesabi Iron Range, St Louis River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.333,\n 48\n ],\n [\n -93.3333,\n 47\n ],\n [\n -91.666,\n 47\n ],\n [\n -91.666,\n 48\n ],\n [\n -93.333,\n 48\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Improvements in timber harvest practices and reductions in harvest volumes over the past half century are commonly presumed to have reduced sediment loads in many western US rivers. However, direct assessments in larger watersheds are relatively sparse. Here, we compare 2019–21 sediment concentrations against those of the late 1970s in the Bogachiel and Calawah River watersheds, adjacent and similarly sized (~300 km2) basins in the western Olympic Mountains of Washington State. The Calawah River watershed has experienced significant land-cover disturbance, including a large 1951 fire, extensive post-fire salvage logging, and relatively high rates of timber harvest through the 1990s. In contrast, the Bogachiel River watershed did not burn, and experienced only modest timber harvest that largely post-dated 1970s sediment monitoring. Channel-width trends suggest the Calawah River was still recovering from 1950s disturbances in the late 1970s. We found that 2019–21 suspended-sediment loads in the Calawah River were 2.3–2.6 times lower than would have been expected based on 1970s sediment rating curves, while recent loads in the Bogachiel River were a factor of 1.4 ± 1.0 lower. We consider the plausibility and possible explanations of declining concentrations in the less-disturbed Bogachiel River. Suspended-sediment yields in the Bogachiel River were two times higher than yields in the Calawah River, which is attributed to a combination of modestly higher precipitation, more efficient runoff generation, and more extensive and erodible Quaternary valley fills in the Bogachiel River. Regional shifts in flood hydrology have also influenced suspended-sediment loads in both watersheds. Our results then document a significant decline in suspended-sediment concentrations in the Calawah River over the past half century. Reduced land-cover disturbance provides the simplest and most likely explanation for this decline, though the wide range of possible concentration changes in the Bogachiel River leaves open possibilities that other processes (human, natural, or methodologic) could be a factor.
Salt marsh ponds expand and deepen over time, potentially reducing ecosystem carbon storage and resilience. The water filled volumes of ponds represent missing carbon due to prevented soil accumulation and removal by erosion and decomposition. Removal mechanisms have different implications as eroded carbon can be redistributed while decomposition results in loss. We constrained ponding effects on carbon dynamics in a New England marsh and determined whether expansion and deepening impact nearby soils by conducting geochemical characterizations of cores from three ponds and surrounding high marshes and models of wind-driven erosion. Radioisotope profiles demonstrate that ponds are not depositional environments and that contemporaneous marsh accretion represents prevented accumulation accounting for 32%–42% of the missing carbon. Erosion accounted for 0%–38% and was bracketed using radioisotope inventories and wind-driven resuspension models. Decomposition, calculated by difference, removes 22%–68%, and when normalized over pond lifespans, produces rates that agree with previous metabolism measurements. Pond surface soils contain new contributions from submerged primary producers and evidence of microbial alteration of underlying peat, as higher levels of detrital biomarkers and thermal stability indices, compared to the marsh. Below pond surface horizons, soil properties and organic matter composition were similar to the marsh, indicating that ponding effects are shallow. Soil bulk density, elemental content, and accretion rates were similar between marsh sites but different from ponds, suggesting that lateral effects are spatially confined. Consequently, ponds negatively impact ecosystem carbon storage but at current densities are not causing pervasive degradation of marshes in this system.
The R package iBluff is designed for coastal bluffs/bluffs morphological analysis and offers an automatic and reproducible alternative to identify bluff edges using a bare earth digital elevation model (DEM) instead of hand digitizing. This package extracts elevation profiles along automatically identified transects on the bluff-face, bluff top, toe, secondary inflections, relative concavity/convexity of bluff-face, and beach dunes (crests and troughs). The package requires at a minimum a bare earth DEM as a raster and a generalized line shapefile (shoreline) approximately parallel with the bluff-face. Both files should be in the same projected coordinate system. The iBluff package was developed to expand and generalize studies of high-relief coastal areas, investigate erosion and seasonality, and could be extended to use three-dimensional (3D) point-cloud data instead of a DEM.
1. Integrating ecological theory with empirical methods is ubiquitous in ecology using hierarchical Bayesian models. However, there has been little development focused on integration of ecological theory into models for survival analysis. Survival is a fundamental process, linking individual fitness with population dynamics, but incorporating life history strategies to inform survival estimation can be challenging because mortality processes occur at multiple scales.
2. We develop an approach to survival analysis, incorporating model constraints based on a species' life history strategy using functional analytical tools. Specifically, we structurally separate intrinsic patterns of mortality that arise from age-specific processes (e.g. increasing survival during early life stages due to growth or maturation, versus senescence) from extrinsic mortality patterns that arise over different periods of time (e.g. seasonal temporal shifts). We use shape constrained generalized additive models (CGAMs) to obtain age-specific hazard functions that incorporate theoretical information based on classical survivorship curves into the age component of the model and capture extrinsic factors in the time component.
3. We compare the performance of our modelling approach to standard survival modelling tools that do not explicitly incorporate species life history strategy in the model structure, using metrics of predictive power, accuracy, efficiency and computation time. We applied these models to two case studies that reflect different functional shapes for the underlying survivorship curves, examining age-period survival for white-tailed deer Odocoileus virginianus in Wisconsin, USA and Columbian sharp-tailed grouse Tympanuchus phasianellus columbianus in Colorado, USA.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210x.14057","usgsCitation":"Ketz, A., Storm, D., Barker, R., Apa, A.D., Oliva-Aviles, C., and Walsh, D.P., 2023, Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses: Methods in Ecology and Evolution, v. 14, no. 3, p. 952-967, https://doi.org/10.1111/2041-210x.14057.","productDescription":"16 p.","startPage":"952","endPage":"967","ipdsId":"IP-141205","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487398,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14057","text":"Publisher Index Page"},{"id":482898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, 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\"}}]}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Ketz, Alison","contributorId":347969,"corporation":false,"usgs":false,"family":"Ketz","given":"Alison","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":929532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storm, Daniel J.","contributorId":341059,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":929533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barker, Rachel","contributorId":301098,"corporation":false,"usgs":false,"family":"Barker","given":"Rachel","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":929534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Apa, Anthony D.","contributorId":272966,"corporation":false,"usgs":false,"family":"Apa","given":"Anthony","email":"","middleInitial":"D.","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":929535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oliva-Aviles, Cristian","contributorId":301099,"corporation":false,"usgs":false,"family":"Oliva-Aviles","given":"Cristian","affiliations":[{"id":65304,"text":"Genentech","active":true,"usgs":false}],"preferred":false,"id":929536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":929537,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70247102,"text":"70247102 - 2023 - Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs","interactions":[],"lastModifiedDate":"2023-07-25T15:05:03.23572","indexId":"70247102","displayToPublicDate":"2023-01-27T09:54:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs","docAbstract":"Parasite infrapopulation size - the population of parasites affecting a single host - is a central metric in parasitology. However, parasites are small and elusive such that imperfect detection is expected. Repeated sampling of parasites during primary sampling occasions (e.g., each host capture) informs the detection process. Here, we estimate flea (Siphonaptera) infrapopulation size on black-tailed prairie dogs (Cynomys ludovicianus, BTPDs) as a proof-of-concept for estimating parasite infrapopulations given imperfect detection. From Jun–Aug 2011, we live-trapped 299 BTPDs for a total of 573 captures on 20 plots distributed among 13 colonies at the Vermejo Park Ranch, New Mexico, USA. During each capture, an anesthetized BTPD was combed 3 times consecutively, 15 s each, to remove and count fleas. Each flea (n = 4846) was linked to the BTPD from which it was collected and assigned an encounter history (’100’, ‘010’, ‘001’). We analyzed the encounter histories using Huggins closed captures models, setting recapture probabilities to 0, thereby accounting for flea removal from hosts. The probability of detecting an individual flea (p) increased with Julian date; field personnel may have become more efficient at combing fleas as the field season progressed. Combined p across 3 combings equaled 0.99. Estimates of flea infrapopulation size were reasonable and followed the negative binomial distribution. Our general approach may be broadly applicable to estimating infrapopulation sizes for parasites. The utility of this approach increases as p declines but, if p is very low, inference is likely limited.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijppaw.2023.01.002","usgsCitation":"Eads, D.A., Huyvaert, K.P., and Biggins, D.E., 2023, Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs: International Journal for Parasitology: Parasites and Wildlife, v. 20, p. 117-121, https://doi.org/10.1016/j.ijppaw.2023.01.002.","productDescription":"5 p.","startPage":"117","endPage":"121","ipdsId":"IP-146545","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444692,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijppaw.2023.01.002","text":"Publisher Index Page"},{"id":419306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Vermejo Park Ranch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.8,\n 36.6\n ],\n [\n -104.8,\n 36.5\n ],\n [\n -104.7,\n 36.5\n ],\n [\n -104.7,\n 36.6\n ],\n [\n -104.8,\n 36.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":878902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huyvaert, Kathryn P.","contributorId":202514,"corporation":false,"usgs":false,"family":"Huyvaert","given":"Kathryn","email":"","middleInitial":"P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":878903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":878904,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256545,"text":"70256545 - 2023 - Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs","interactions":[],"lastModifiedDate":"2024-08-22T14:59:53.775057","indexId":"70256545","displayToPublicDate":"2023-01-27T09:42:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs","docAbstract":"Recreational-grade side-scan sonar (SSS) has become an invaluable tool for inland fisheries, particularly when characterizing underwater habitat, but it is being increasingly used for enumerating large-bodied (> 1 m total length [TL]) aquatic fauna. We used SSS in river and reservoir environments to evaluate methods for identifying and counting Paddlefish Polyodon spathula, a large pelagic planktivore of recreational and economic importance that can exceed 2 m in length and weigh over 70 kg. We assessed accuracy and precision among readers to identify Paddlefish by assigning confidence scores (1–3; with 3 being more confident) to sonar images of a ballistics-gel filled fiberglass replica Paddlefish. Readers varied in their confidence scores for the replica Paddlefish and no reader could identify the target beyond 25 m from the transducer. Afterwards, we used SSS to survey several kilometers of a reservoir during summer residency and a large river during springtime spawning migrations. Two readers counted Paddlefish images in the SSS recordings and we estimated population size in the surveyed area with distance sampling. In the reservoir, the number of Paddlefish counted ranged from 172 to 184. In the river, the number of Paddlefish counted ranged from 165 to 617. The exponential model of distance was most-supported for detection in both environments, except there was support for a half-norm distribution for one reader in the river. In the reservoir, abundance estimates were statistically similar between readers at approximately 1500 (7/ha) in the total scanned area. In the river, similar abundance estimates were obtained with the half-norm model from one reader compared to the exponential model of the other reader, resulting in approximately 1500 individuals (30/ha) in the surveyed area. The application of SSS to count Paddlefish has some clear advantages to traditional methods, such as gill netting, and can be done at multiple times of the year. Distance sampling methods compensated for differences in counts among readers, indicating distance sampling can produce similar abundance estimates even when variation in counts exists among readers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2023.106639","usgsCitation":"Wolfenkoehler, W., Long, J.M., Gary, R., Snow, R., Schooley, J.D., Bruckerhoff, L.A., and Lonsinger, R.C., 2023, Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs: Fisheries Research, v. 261, 106639, 9 p., https://doi.org/10.1016/j.fishres.2023.106639.","productDescription":"106639, 9 p.","ipdsId":"IP-142414","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Keystone Lake, Lake Carl Blackwell, Verdigris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.32079029272471,\n 36.18635094806682\n ],\n [\n -97.32079029272471,\n 36.089827328506544\n ],\n [\n -97.1696333849386,\n 36.089827328506544\n ],\n [\n -97.1696333849386,\n 36.18635094806682\n ],\n [\n -97.32079029272471,\n 36.18635094806682\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.2644501788143,\n 36.125328208556084\n ],\n [\n -96.2240244941739,\n 36.16506963468771\n ],\n [\n -96.21875157878593,\n 36.22747975513728\n ],\n [\n -96.45427513277778,\n 36.332329984806975\n ],\n [\n -96.47536679432932,\n 36.306838838970805\n ],\n [\n -96.420880001988,\n 36.254414197425305\n ],\n [\n -96.3945154250485,\n 36.210463747118126\n ],\n [\n -96.29081475575383,\n 36.213300005475446\n ],\n [\n -96.29081475575383,\n 36.13952388632518\n ],\n [\n -96.2644501788143,\n 36.125328208556084\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.52086370103724,\n 36.84221290357827\n ],\n [\n -95.63789552781877,\n 36.84221290357827\n ],\n [\n -95.63789552781877,\n 36.6881372389295\n ],\n [\n -95.52086370103724,\n 36.6881372389295\n ],\n [\n -95.52086370103724,\n 36.84221290357827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"261","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfenkoehler, Wyatt","contributorId":341077,"corporation":false,"usgs":false,"family":"Wolfenkoehler","given":"Wyatt","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gary, Ryan","contributorId":341078,"corporation":false,"usgs":false,"family":"Gary","given":"Ryan","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snow, Richard A.","contributorId":341079,"corporation":false,"usgs":false,"family":"Snow","given":"Richard A.","affiliations":[{"id":81697,"text":"Oklahoma Fishery Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":907911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schooley, Jason D.","contributorId":341080,"corporation":false,"usgs":false,"family":"Schooley","given":"Jason","email":"","middleInitial":"D.","affiliations":[{"id":81698,"text":"Paddlefish Research Center","active":true,"usgs":false}],"preferred":false,"id":907912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bruckerhoff, Lindsey Ann 0000-0002-9523-4808","orcid":"https://orcid.org/0000-0002-9523-4808","contributorId":292594,"corporation":false,"usgs":true,"family":"Bruckerhoff","given":"Lindsey","email":"","middleInitial":"Ann","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247865,"text":"70247865 - 2023 - Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001","interactions":[],"lastModifiedDate":"2023-08-22T13:39:04.295519","indexId":"70247865","displayToPublicDate":"2023-01-27T08:32:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001","docAbstract":"In 1999–2001, Guagua Pichincha volcano, Ecuador, produced a series of cyclic explosive and effusive eruptions. Rock samples, including dense blocks and pumiceous clasts collected during the eruption sequence, and ballistic bombs later collected from the crater floor, provide information about magma storage, ascent, decompression, degassing, repressurization, and regassing prior to eruption. Pairs of Fe-Ti oxides indicate equilibrium within 1.2–1.5 log units above the NNO oxidation buffer and equilibrium temperatures from 805 to 905 °C. Melt inclusions record H2O contents of 2.7–4.6 wt% and CO2 contents (uncorrected for CO2 segregation into bubbles) from 19 to 310 ppm. Minimum melt inclusion saturation pressures fall between 69 and 168 MPa, or equilibration depths of 2.8 and 6.8 km, the lower end of which is coincident with the maximum inferred equilibration depths for the most vesicular breadcrust bombs sampled. Amphibole phenocrysts lack breakdown rims (except for one sample) and plagioclase phenocrysts have abundant oscillatory compositional zones. Plagioclase areal microlite number densities (Na) range over less than one order of magnitude (8.9×103–8.7×104 mm-2) among all samples, with the exception of a dense, low crystallinity sample (Na = 3.0×103 mm−2) and a pumiceous sample erupted on 17 December 1999 (Na = 1.7×103 mm−2). Plagioclase microlite shapes include tabular, hopper, and swallowtail forms. Taken together, the relatively high plagioclase microlite number densities, the high number of oscillatory zones in plagioclase phenocrysts, the presence of CO2 in groundmass glass, seismicity, and time-varying tilt cycles provide a picture of sudden evacuation of magma residing at different levels in the shallow conduit. Explosive eruptions punctuate inter-eruptive repose periods marked by time-varying rates of degassing (volatile fluxing) and re-pressurization. Shallow residence time in the conduit was sufficient to allow precipitation of silica-phase in the groundmass, but insufficient to allow breakdown of hornblende phenocrysts, with the one exception of the final dome sample from 2000, which has the longest preceding repose time. These results support a model of cyclic pressure cycling, volatile exsolution and regassing, and magma decompression decoupled from ascent.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-023-01626-3","usgsCitation":"Wright, H.M., Cioni, R., Cashman, K.V., Mothes, P., and Rosi, M., 2023, Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001: Bulletin of Volcanology, v. 85, 12, 24 p., https://doi.org/10.1007/s00445-023-01626-3.","productDescription":"12, 24 p.","ipdsId":"IP-143035","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444695,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-023-01626-3","text":"Publisher Index Page"},{"id":420012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Guagua Pichincha Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.64295621873246,\n -0.14110488224878281\n ],\n [\n -78.64295621873246,\n -0.201181738857926\n ],\n [\n -78.58224335456136,\n -0.201181738857926\n ],\n [\n -78.58224335456136,\n -0.14110488224878281\n ],\n [\n -78.64295621873246,\n -0.14110488224878281\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","noUsgsAuthors":false,"publicationDate":"2023-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Heather M. 0000-0001-9013-507X hwright@usgs.gov","orcid":"https://orcid.org/0000-0001-9013-507X","contributorId":3949,"corporation":false,"usgs":true,"family":"Wright","given":"Heather","email":"hwright@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":880787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cioni, Raffaello 0000-0002-2526-9095","orcid":"https://orcid.org/0000-0002-2526-9095","contributorId":328622,"corporation":false,"usgs":false,"family":"Cioni","given":"Raffaello","email":"","affiliations":[{"id":78424,"text":"Universita degli Studi di Firenzi","active":true,"usgs":false}],"preferred":false,"id":880788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cashman, Katharine V.","contributorId":199542,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine","email":"","middleInitial":"V.","affiliations":[{"id":13025,"text":"Department of Geological Sciences, University of Oregon","active":true,"usgs":false}],"preferred":false,"id":880789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mothes, Patricia","contributorId":178532,"corporation":false,"usgs":false,"family":"Mothes","given":"Patricia","affiliations":[],"preferred":false,"id":880790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosi, Mauro","contributorId":206499,"corporation":false,"usgs":false,"family":"Rosi","given":"Mauro","email":"","affiliations":[],"preferred":false,"id":880791,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240144,"text":"70240144 - 2023 - New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences","interactions":[],"lastModifiedDate":"2023-01-30T12:51:57.454932","indexId":"70240144","displayToPublicDate":"2023-01-27T06:50:11","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences","docAbstract":"Geothermal well data from Southern Methodist University and the U.S. Geological Survey (USGS) were used to create maps of estimated background conductive heat flow across the Great Basin region of the western United States. These heat flow maps were generated as part of the USGS hydrothermal and Enhanced Geothermal Systems resource assessment process, and the creation process seeks to remove the influence of hydrothermal convection from the predictions of the background conductive heat flow. The heat flow maps were constructed using a custom-developed iterative process using weighted regression, in which convectively influenced outliers were de-emphasized by assigning lower weights to measurements with heat flow values further from the estimated local trend (e.g., local convective influence). The local linear weighted regression algorithm is two-dimensional locally estimated scatterplot smoothing where smoothness was controlled by varying the number of nearby wells used for each local interpolation.\nThree maps resulting from conductive heat flow models are detailed in this paper, highlighting the influence of measurement confidence. The three maps use either: measurements from all wells with equal weight (no confidence weights), or one of two different published categorization methods to de-emphasize low-quality measurements; one categorization method graded thermal gradient quality, the other categorization method graded thermal conductivity quality. Each map is an estimate of background conductive heat flow as a function of reported data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the departure of a well from the estimated background heat flow conditions, and the value of the residual may be useful in identifying the influence of fluids (hydrothermal or groundwater) on conductive heat flow. Of the three maps presented, the map that de-emphasized the impact of wells with low-quality thermal gradient measurements appears to perform best because it did not incorporate many of the wells in the Snake River Plain that do not penetrate the aquifer and are therefore very unlikely to reflect true conductive conditions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 48th Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"48th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 6-8, 2023","conferenceLocation":"Stanford, California","language":"English","publisher":"Stanford Geothermal Workshop","usgsCitation":"DeAngelo, J., Burns, E., Gentry, E., Batir, J.F., Lindsey, C.R., and Mordensky, S.P., 2023, New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences, in Proceedings, 48th Workshop on Geothermal Reservoir Engineering, Stanford, California, February 6-8, 2023, 13 p.","productDescription":"13 p.","ipdsId":"IP-149016","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":412439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412435,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2023/Deangelo.pdf?t=1674862190"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.94931681460088,\n 43.31269307515126\n ],\n [\n -121.94931681460088,\n 34.37043992080774\n ],\n [\n -110.40572044760874,\n 34.37043992080774\n ],\n [\n -110.40572044760874,\n 43.31269307515126\n ],\n [\n -121.94931681460088,\n 43.31269307515126\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gentry, Emilie","contributorId":293494,"corporation":false,"usgs":false,"family":"Gentry","given":"Emilie","email":"","affiliations":[{"id":63314,"text":"Petrolern","active":true,"usgs":false}],"preferred":false,"id":862756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batir, Joseph F.","contributorId":293495,"corporation":false,"usgs":false,"family":"Batir","given":"Joseph","email":"","middleInitial":"F.","affiliations":[{"id":63314,"text":"Petrolern","active":true,"usgs":false}],"preferred":false,"id":862757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862759,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240125,"text":"70240125 - 2023 - Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska","interactions":[],"lastModifiedDate":"2023-01-30T12:36:58.473885","indexId":"70240125","displayToPublicDate":"2023-01-27T06:33:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska","docAbstract":"The frequency of large supraglacial landslides (rock avalanches) occurring in glacial environments is thought to be increasing due to feedbacks with climate warming and permafrost degradation. However, it is difficult to (i) test this; (ii) establish cause–effect relationships; and (iii) determine associated lag-times, due to both temporal and spatial biases in detection rates. Here we applied the Google Earth Engine supraglacial debris input detector (GERALDINE) to Glacier Bay National Park & Preserve (GLBA), Alaska. We find that the number of rock avalanches (RAs) has previously been underestimated by 53 %, with a bias in past detections towards large area RAs. In total, GLBA experienced 69 RAs during 1984–2020, with the highest frequency in the last three years. Of these, 58 % were deposited into the accumulation zone and then sequestered into the ice within two years. RA sources clustered spatially at high elevations and around certain peaks and ridges, predominantly at the boundary of modelled permafrost likelihood. They also clustered temporally, occurring mainly between May and September when air temperatures were high enough to initiate rock-permafrost degradation mechanisms. There was a chronic background debris supply from RAs, with at least one RA occurring in all but nine years; however, a debris rich period during 2012–2016 was driven by three large RAs delivering 44 % of all (1984–2020) debris (by area). Comparable investigation of slope-failures in other remote currently glaciated regions is lacking. If RA rates are similar elsewhere, especially the bias towards emplacement onto/into accumulation zones, their contribution to glacial sediment budgets has been globally underestimated.
The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, by 7 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the sixth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. Prior reports in this series documented data collected from October 2015 to September 2020. This report contains information pertinent to data collection during the 2021 water year, from October 2020 to September 2021. There were no modifications this year to the previously installed monitoring network. Data at each station were compared for the length of the project and on a yearly basis to show the annual variability of discharge and salinity in the project area.
Discharge and salinity varied widely during the 2021 water year data collection period, which included above-average rainfall for four of the five counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 6 years considered for this study. Annual mean discharge at Durbin Creek was highest among the tributaries, followed by Trout River, Clapboard Creek, Ortega River, Pottsburg Creek at U.S. 90, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, Cedar River, and Broward River, whose annual mean discharge was lowest. Annual mean discharge at 7 of the 10 tributary monitoring sites was higher for the 2021 water year than for the 2020 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 6 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2021 water year than for the 2020 water year and ranked second among the annual totals computed for the 6 years considered for this study.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2020 water year, annual mean salinity at all monitoring locations was lower for the 2021 water year except at the tributary site of Durbin Creek, which remained the same. The 2021 annual mean salinity at all sites ranked second lowest since the beginning of the study in 2016 except at Julington Creek and Racy Point, which tied for lowest, and Durbin Creek, which had the same value for each year.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221111","issn":"ISSN 2331-1258","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2023, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2021: U.S. Geological Survey Open-File Report 2022–1111, 48 p., https://doi.org/10.3133/ofr20221111.","productDescription":"Report: x, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-139675","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":413532,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221111/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412288,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.XML","linkFileType":{"id":8,"text":"xml"}},{"id":412285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1111/coverthb.jpg"},{"id":412286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":412287,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1111/images"},{"id":412289,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.31115628870195,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 30.583300030597925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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4446 Pet Lane, Suite 108
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We conducted a field study during 2020–21 to describe habitat use patterns of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the mainstem Willamette, McKenzie, and Santiam Rivers and to evaluate how habitat suitability criteria affected the predictive accuracy of a hydraulic habitat model. Two approaches were used to collect habitat use data: a stratified sampling design was used to ensure that a representative sample of available habitats was included in our sampling; and a targeted sampling design was used to collect additional data in habitat cells where juvenile Chinook salmon were observed. Habitat attributes and fish presence data were collected in habitat cells that were approximately 2 square meters during April, June, and July. A total of 632 cells were sampled during the study and included habitat located in the main channel (373 cells), side channels (228 cells), and in alcoves (31 cells). Juvenile Chinook salmon were observed in 42 percent of the cells located in the main channel, 38 percent of the cells located in side channels, and 7 percent of the cells located in alcoves. We used logistic regression to develop resource selection functions for April, June, and July, which produced probability-based predictions of habitat use for juvenile Chinook salmon based on water velocity and water depth. The resource selection functions revealed a habitat shift by juvenile Chinook salmon to locations with higher water velocities and greater water depths from April to July as juvenile Chinook salmon size increased. The resource selection functions that we developed are an important addition to habitat modeling in the Willamette River basin because they were developed from in-basin data, capture seasonal differences in habitat use, and facilitate probability-based estimates of habitat use for juvenile Chinook salmon. These advancements will improve habitat modeling efforts for juvenile Chinook salmon during spring and summer months within the Willamette River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hansen, G.S., Perry, R.W., Kock, T.J., White, J.S., Haner, P.V., Plumb, J.M., and Wallick, J.R., 2023, Assessment of habitat use by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Willamette River Basin, 2020–21: U.S. Geological Survey Open-File Report 2023–1001, 20 p., https://doi.org/10.3133/ofr20231001.","productDescription":"vii, 20 p.","onlineOnly":"Y","ipdsId":"IP-141847","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":412251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1001/coverthb.jpg"},{"id":412252,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1001/ofr20231001.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1001"},{"id":412254,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1001/images"},{"id":412255,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1001/ofr20231001.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.70681047535611,\n 46.26773381073258\n ],\n [\n -124.70681047535611,\n 42.583539358952294\n ],\n [\n -121.08286121390995,\n 42.583539358952294\n ],\n [\n -121.08286121390995,\n 46.26773381073258\n ],\n [\n -124.70681047535611,\n 46.26773381073258\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016