In January and March 2018, coastal Massachusetts experienced flooding from two separate nor’easters. To put the January and March floods into historical context, the USGS computed statistical stillwater elevations. Stillwater elevations recorded in January 2018 in Boston (9.66 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 2 and 1 percent (between a 50- and 100-year recurrence interval). Stillwater elevations recorded in March 2018 in Boston (9.17 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 4 and 2 percent (between a 25- and 50-year recurrence interval). Flood maps show that the area inundated by the January storm is slightly more extensive than that of the March storm, reflecting the respective profiles of the two storms. On the basis of a limited dataset, the attenuation of peak water levels was estimated as a function of the hydraulic distance inland and the starting stillwater elevation computed for the flood within 0.6 foot of what was measured in the field. A simple one-dimensional model was calibrated using flood elevation data collected after the January flood, and the results of the model were validated using flood elevation data collected after the March flood to model the attenuation of the flood elevations as the storms move inland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215109","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P.J., Olson, S.A., Sturtevant, L.P., and Kalmon, R.D., 2021, Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England: U.S. Geological Survey Scientific Investigations Report 2021–5109, 13 p., https://doi.org/10.3133/sir20215109.","productDescription":"Report: iv, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125348","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390667,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RINQ4B","text":"USGS data release","linkHelpText":"Data and shapefiles used to document the floods associated with the January and March 2018 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Ecological transformation creates many challenges for public natural resource management and requires managers to grapple with new relationships to change and new ways to manage it. In the context of unfamiliar trajectories of ecological change, a manager can resist, accept, or direct change, choices that make up the resist-accept-direct (RAD) framework. In this article, we provide a conceptual framework for how to think about this new decision space that managers must navigate. We identify internal factors (mental models) and external factors (social feasibility, institutional context, and scientific uncertainty) that shape management decisions. We then apply this conceptual framework to the RAD strategies (resist, accept, direct) to illuminate how internal and external factors shape those decisions. Finally, we conclude with a discussion of how this conceptual framework shapes our understanding of management decisions, especially how these decisions are not just ecological but also social, and the implications for research and management.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biab086","usgsCitation":"Clifford, K.R., Cravens, A.E., and Knapp, C.N., 2021, Responding to ecological transformation: Mental models, external constraints, and manager decision-making: BioScience, v. 72, no. 1, p. 57-70, https://doi.org/10.1093/biosci/biab086.","productDescription":"14 p.","startPage":"57","endPage":"70","ipdsId":"IP-127232","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab086","text":"Publisher Index Page"},{"id":394010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clifford, Katherine R. 0000-0002-1385-8765","orcid":"https://orcid.org/0000-0002-1385-8765","contributorId":259886,"corporation":false,"usgs":true,"family":"Clifford","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knapp, Corrine N.","contributorId":270993,"corporation":false,"usgs":false,"family":"Knapp","given":"Corrine","email":"","middleInitial":"N.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":830321,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226753,"text":"70226753 - 2021 - Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","interactions":[],"lastModifiedDate":"2021-12-09T12:35:17.454202","indexId":"70226753","displayToPublicDate":"2021-11-17T06:32:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","docAbstract":"Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m2) and coarse (100 m2) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under-canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100-m and 1-m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine-scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance.
The U.S. Geological Survey measured cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (anaC, cyrA, taxa specific mcyE, and sxtA), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Occupancy models are often used to analyze long-term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non-detection data at a sample unit and many long-term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space-for-time substitution). Still, the efficacy of fitting occupancy models using a space-for-time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (Hypomesus transpacificus) and Longfin Smelt (Spirinchus thaleichthys) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay-Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space-for-time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space-for-time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long-term monitoring data to understand the distribution of species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8292","usgsCitation":"Duarte, A., and Peterson, J., 2021, Space-for-time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay-Delta: Ecology and Evolution, v. 11, no. 23, p. 16727-16744, https://doi.org/10.1002/ece3.8292.","productDescription":"18 p.","startPage":"16727","endPage":"16744","ipdsId":"IP-123647","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8292","text":"Publisher Index Page"},{"id":489260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Calfornia","otherGeospatial":"San Francisco Bay-Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.21496769714439,\n 38.746434916955366\n ],\n [\n -122.69121998415443,\n 38.746434916955366\n ],\n [\n -122.69121998415443,\n 37.862368554497834\n ],\n [\n -121.21496769714439,\n 37.862368554497834\n ],\n [\n -121.21496769714439,\n 38.746434916955366\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"23","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":79822,"corporation":false,"usgs":true,"family":"Duarte","given":"Adam","email":"","affiliations":[],"preferred":false,"id":938750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938749,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225747,"text":"sir20215115 - 2021 - Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio","interactions":[],"lastModifiedDate":"2021-11-16T15:03:52.608004","indexId":"sir20215115","displayToPublicDate":"2021-11-16T10:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5115","displayTitle":"Update of the Groundwater Flow Model for the Great Miami Buried-Valley Aquifer in the Vicinity of Wright-Patterson Air Force Base near Dayton, Ohio","title":"Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio","docAbstract":"A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears (Ursus maritimus). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progressive decline in access to prey or an increase in the energetic cost of accessing prey has the potential to adversely affect polar bear population dynamics. We examined long-term (1983–2015) patterns of spring body condition (indexed using residual body mass) and maternal investment (i.e., litter mass of cubs-of-the-year and yearlings; COY and YRL) of polar bears from Alaska’s southern Beaufort Sea to evaluate potential relationships with regional- and circumpolar-scale sea ice conditions and atmospheric patterns. The length of the summer open-water (OW) season (i.e., the period of time the sea ice is mostly absent from the continental shelf) increased at a rate of 18 days decade-1 over the study period. However, the OW season duration was not a strong determinant of spring residual body mass or litter mass. Residual body mass of independent (i.e., subadults and adults) female bears varied relative to age class, reproductive status, and the strength of the prior winter’s Arctic Oscillation (i.e., a circumpolar-scale mode of climate variability driven by long-term atmospheric patterns). Spring residual mass of independent males varied with age class and variation in wind speed (i.e., regional-scale short-term atmospheric patterns) during the winter of the year preceding capture. Over the study period, mean annual body mass of adult females unaccompanied by COY declined by 4 kg/ decade-1, while no temporal trends were evident in the mean annual body mass of adult females with COY, adult males, and subadults. Litter mass of COY varied relative to capture date, maternal age class and mass, litter size, and year of capture. Litter mass of YRL varied with capture date, maternal age class and mass, litter size, variation in winter wind speed (the year of and year preceding capture), and the strength of the prior winter’s Arctic Oscillation. Mean annual litter mass of COY decreased at a rate of 2.6 kg decade-1 and declined 0.68 kg for every 10 kg reduction in maternal mass. No trend was evident in the mean annual litter mass of yearlings. These findings suggest a nuanced response of the southern Beaufort Sea polar bears to environmental change, where some demographic groups (e.g., adult males and subadults) are presently more resilient than others to changes in the Arctic marine ecosystem.
Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology
Medicine Lake Highlands/Fall River Springs Aquifer System, located in northeastern California, is home to some of the largest first-order springs in the United States. This work assesses the likely effects of projected climate change on spring flow. Four anticipated climate futures (GFDL A2, GFDL B1, CCSM4 rcp 8.5, CNRM rcp 8.5) for California, which predict a range of conditions (generally warming and transitioning from snow to rain with variable amounts of total precipitation), are postulated to affect groundwater recharge primarily by changing evapotranspiration. The linkages between climate variables and spring flow are evaluated using a water balance model that represents the physics of evapotranspiration and recharge, the Basin Characterization Model. Three of the four climate scenarios (GFDL A2, GFDL B1, CCSM4 rcp 8.5) project that by the year 2100, groundwater recharge (and consequently decreased spring flow) will decrease by 27%, 21%, and 9%, respectively. The fourth scenario (CNRM rcp 8.5) showed an increase in recharge of 32% due to a significant increase in precipitation (27%). Evapotranspiration increases due to a shift in the type of precipitation and a longer growing season. While the likelihood of each scenario is outside the scope of this work, unless total precipitation increases dramatically in the future, increased temperatures and decreasing precipitation will likely result in reduced spring flows, along with warmer water temperatures in downstream habitats.
Magma-water interaction can dramatically influence the explosivity of volcanic eruptions. However, syn- and post-eruptive diffusion of external (non-magmatic) water into volcanic glass remains poorly constrained and may bias interpretation of water in juvenile products. Hydrogen isotopes in ash from the 2009 eruption of Redoubt Volcano, Alaska, record syn-eruptive hydration by vaporized glacial meltwater. Both ash aggregation and hydration occurred in the wettest regions of the plume, which resulted in the removal and deposition of the most hydrated ash in proximal areas <50 km from the vent. Diffusion models show that the high temperatures of pyroclast-water interactions (>400°C) are more important than the cooling rate in facilitating hydration. These observations suggest that syn-eruptive glass hydration occurred where meltwater was entrained at high temperature, in the plume margins near the vent. Ash in the drier plume interior remained insulated from entrained meltwater until it cooled sufficiently to avoid significant hydration.
The origin and parental magma for layered cumulates in the Lower Banded series (LBS) and the J-M Reef Pd-Pt deposit of the Stillwater Complex remains poorly constrained. We present whole-rock lithogeochemistry and mineral chemistry from LBS rocks collected from drill holes and surface samples from the Mountain View area of the complex that in total span nearly the entirety of the LBS stratigraphy. Excess S, Pt, and Pd in the noritic and gabbronoritic cumulates of the LBS indicate that small amounts of high tenor sulfide liquid generated at very low degrees of sulfide oversaturation were ubiquitous parts of the cumulate assemblage. We show that a simple two-stage thermodynamic model of assimilation-batch crystallization of a komatiitic parental magma in the lower crust, produces a close match to a common suite of fine-grained gabbronorite dikes and sills that intrude both the complex and its footwall. After fractionating ultramafic cumulates in the lower crust, the model contaminated komatiitic liquid produces upper crustal cumulates by batch crystallization en route to or at the level of the intrusion. The modeled rocks have compositions and mineral assemblages closely resembling pyroxenite of the Bronzitite zone and both norite and gabbronorite cumulates in the lower LBS. The trends from the Bronzitite zone through Norite zone I and Gabbronorite zone I can be understood as the result of deposition of crystals from successive batches of the same contaminated parental magma, with an upward trend toward greater amounts of cooling before the separation of crystals from liquid. The olivine-bearing suite of Olivine-bearing zone I, which includes the J-M Reef, can be modeled by partial remelting of the same norite and gabbronorite cumulates due to a temporarily increased flux of hot, moderately less contaminated LBS parental magma that infiltrated partially molten cumulates because its density exceeded that of the interstitial liquid. This model suggests that infiltration of hot Mg-rich parental liquid into moderately PGE-enriched footwall cumulates may be fundamental to the formation of the extremely high tenor sulfide mineralization in the J-M Reef ore zone, and perhaps other reef-type deposits worldwide. The same metal tenors that would require silicate/sulfide mass ratios (i.e., R-factors) of 105 to 106 in a single stage of equilibration would be attained during this second stage of interaction by the incremental infiltration and passage of LBS parental magma through previously sulfide saturated cumulate mush.
In support of nutrient reduction efforts, total phosphorus loads and yields were computed using turbidity-surrogate and LOAD ESTimator (LOADEST) models for the Cedar River at Palo, Iowa, for January 1, 2009, to December 15, 2020. Sample data were used to create a total phosphorus concentration turbidity-surrogate model. Total phosphorus loads also were computed from two streamflow-based LOADEST load models for the periods 2009–20 and 2016–20. The 2009–20 model was used for comparison with previously published loads at this site. The 2016–20 LOADEST model was used with the turbidity-surrogate model before sensor deployment and during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents computed loads and methods needed to compute site-specific loads accurately and track annual progress toward nutrient reduction goals within the State.
A comparison of loads from Weighted Regressions on Time, Discharge, and Season; LOADEST; and surrogate models indicated substantial differences at this site among these methods. Changes in both monitoring approaches (high-frequency sensor and surrogate data) and changes in load-calculation methods present potential challenges in assessing trends, such as assessment of load reduction.
Annual total phosphorus loads for the Cedar River at Palo, Iowa, ranged from 1,370 to 2,360 U.S. short tons per year for 2018–20, based on the turbidity-surrogate model with gaps in sensor data filled with the 2016–20 LOADEST model. Annual total phosphorus yields for the Cedar River ranged from 0.67 to 1.16 pounds per acre per year for 2018–20. Although this load estimate is lower than previous estimates for the benchmark period of 2006–10, when normalized by streamflow, nearly all the apparent reduction can be attributed to differences in the load-calculation methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215127","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Garrett, J.D., 2021, Total phosphorus loadings for the Cedar River at Palo, Iowa, 2009–20: U.S. Geological Survey Scientific Investigations Report 2021–5127, 15 p., https://doi.org/10.3133/sir20215127.","productDescription":"Report vi, 15 p.: Database; Related Work","onlineOnly":"Y","ipdsId":"IP-127065","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391620,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5127/coverthb.jpg"},{"id":391621,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5127/sir20215127.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5127"},{"id":391622,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":391623,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20185090","text":"Transport of nitrogen and phosphorus in the Cedar River Basin, Iowa and Minnesota, 2000–15"}],"country":"United States","state":"Palo","otherGeospatial":"Cedar River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.83197021484375,\n 42.02889410108475\n ],\n [\n -91.71180725097655,\n 42.02889410108475\n ],\n [\n -91.71180725097655,\n 42.09312731992276\n ],\n [\n -91.83197021484375,\n 42.09312731992276\n ],\n [\n -91.83197021484375,\n 42.02889410108475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Axis deer (Axis axis) are invasive species that threaten native ecosystems and agriculture on Maui Island. To mitigate negative effects, it is necessary to understand current abundance, population trajectory, and how to most effectively reduce the population. Our objectives were to examine the population history of Maui axis deer, estimate observed population growth, and use species-specific demographic parameters in a VORTEX population viability analysis to examine removal scenarios that would most effectively reduce the population. Only nine deer were introduced in 1959, but recent estimates of >10,000 deer suggest population growth rates (r) ranging between 0.147 and 0.160 even though >11,200 have been removed by hunters and resource managers. In VORTEX simulations, we evaluated an initial population size of 6,000 females and 4,000 males, reflecting the probable 3F:2M sex ratio, with annual removal rates of 10%, 20%, and 30% over a 10-year period. A removal rate of 10% resulted in a positive growth rate of 0.103 ± 0.001. A 20% removal rate resulted in only a slightly negative growth, while a 30% removal rate resulted in –0.130 ± 0.004. By increasing the ratio of females removed to 4F:1M in the 30% harvest scenario, the decline nearly doubled, resulting in –0.223 ± 0.004. Effectively reducing axis deer will most likely require an annual removal of approximately 20–30% of the population and with a greater proportion of females to increase the population decline. Selective removal of males may not only be inefficient, but also counterproductive to population reduction goals.
We evaluate the seismic performance of modern seismically designed wood light-frame (WLF) buildings, considering regional seismic hazard characteristics that influence ground motion duration and frequency content and, thus, seismic risk. Results show that WLF building response correlates strongly with ground motion spectral shape but weakly with duration. Due to the flatter spectral shape of ground motions from subduction events, WLF buildings at sites affected by these earthquakes may experience double the economic losses for a given intensity of shaking, and collapse capacities may be reduced by up to 50%, compared to those at sites affected by crustal earthquakes. These differences could motivate significant increases in design values at sites affected by subduction earthquakes to achieve the uniform risk targets of the American Society of Civil Engineers (ASCE) 7 standard.
","language":"English","publisher":"Springer","doi":"10.1007/s10518-021-01258-y","usgsCitation":"Chase, R.E., Liel, A.B., Luco, N., and Bullock, Z., 2021, Hazard-consistent seismic losses and collapse capacities for light-frame wood buildings in California and Cascadia: Bulletin of Earthquake Engineering, v. 19, p. 6615-6639, https://doi.org/10.1007/s10518-021-01258-y.","productDescription":"25 p.","startPage":"6615","endPage":"6639","ipdsId":"IP-129311","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":450224,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10518-021-01258-y","text":"Publisher Index Page"},{"id":394517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, California, Oregon Washington","city":"Anchorage, Eugene, Los Angeles, Portland, San Francisco, 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-122.76123046875,\n 45.44471679159555\n ],\n [\n -122.38494873046875,\n 45.44471679159555\n ],\n [\n -122.38494873046875,\n 45.60395019421033\n ],\n [\n -122.76123046875,\n 45.60395019421033\n ],\n [\n -122.76123046875,\n 45.44471679159555\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1728744506836,\n 44.00343436215528\n ],\n [\n -123.04309844970705,\n 44.00343436215528\n ],\n [\n -123.04309844970705,\n 44.109281923355645\n ],\n [\n -123.1728744506836,\n 44.109281923355645\n ],\n [\n -123.1728744506836,\n 44.00343436215528\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.4591064453125,\n 47.47637579720933\n ],\n [\n -122.24212646484375,\n 47.47637579720933\n ],\n [\n -122.24212646484375,\n 47.758714187846294\n ],\n [\n -122.4591064453125,\n 47.758714187846294\n ],\n [\n -122.4591064453125,\n 47.47637579720933\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.08697509765622,\n 61.062272494474065\n ],\n [\n -149.67498779296875,\n 61.062272494474065\n ],\n [\n -149.67498779296875,\n 61.28739102214365\n ],\n [\n -150.08697509765622,\n 61.28739102214365\n ],\n [\n -150.08697509765622,\n 61.062272494474065\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2021-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Chase, Robert Edward 0000-0002-8155-6830","orcid":"https://orcid.org/0000-0002-8155-6830","contributorId":271198,"corporation":false,"usgs":true,"family":"Chase","given":"Robert","email":"","middleInitial":"Edward","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":831149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liel, Abbie B.","contributorId":184158,"corporation":false,"usgs":false,"family":"Liel","given":"Abbie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":831150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":831151,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bullock, Zach","contributorId":271199,"corporation":false,"usgs":false,"family":"Bullock","given":"Zach","email":"","affiliations":[{"id":56314,"text":"Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA 91125","active":true,"usgs":false}],"preferred":false,"id":831152,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229519,"text":"70229519 - 2021 - Mineral deposit discovery order and three-part quantitative assessments","interactions":[],"lastModifiedDate":"2022-03-11T13:08:11.334928","indexId":"70229519","displayToPublicDate":"2021-11-11T07:07:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Mineral deposit discovery order and three-part quantitative assessments","docAbstract":"Larger oil pools tending to be discovered earlier in an exploration play suggests the same pattern might exist for mineral deposits and could be used in predicting sizes of undiscovered deposits in mineral assessments. The volume of individual petroleum pools is highly correlated with surface projection area of pools in basins. The gradual additions to individual oil pool reserves over time adds to the appearance of larger pools being discovered earlier.
Comparisons of surface projected areas of mineral deposits to their tonnages showed significant positive relationships in all 10 deposit types analyzed, suggesting that larger deposits should be discovered earlier than small deposits.
Analysis of deposits consistent with three-part mineral assessments identified 9 combinations of mineral deposit types in large regions each containing multiple geological permissive tracts showing negative and 1 positive relationships of deposit size with discovery date significant at the 1% level. Twenty other tests of regions containing multiple permissive settings had either negative or positive relationships, none significantly different from those that might occur by chance. The large regions are mostly based on political boundaries. These results suggest mineral deposit discovery order is not the same as observed in oil pool exploration.
The widely employed three-part quantitative mineral resource assessments are an obvious choice to benefit from patterns of declining deposit sizes with order of discovery. The 30 tests of relationships of discovery dates to deposit sizes demonstrated here were performed with deposits consistent with those in three-part assessments, but the large areas are not consistent with permissive tracts used in these assessments because they also contain substantial non-permissive geology.
In 100 permissive tracts assessed with three-part assessments of multiple deposit types located throughout the world, the median number of known well-explored deposits is 1 and 90 percent of tracts report less than 9 deposits. The number of well-explored deposits in three-part assessed tracts tends to be quite small, limiting any ability to recognize a discovery order versus size relationship.
In a three-part assessment of undiscovered porphyry copper deposits of South America, only 7 of 26 delineated tracts contained more than 2 known deposits and only 1 had a significant negative relationship between tonnage of known deposits and year of discovery (p = 0.04). Most predicted undiscovered deposits in this tract were expected to be under extensive unexplored post-mineralization cover, meaning the general grade and tonnage model should be applied because the discovery order process starts over. Projection of deposit sizes based on discovery order would provide a biased estimate of the undiscovered deposit sizes in this case. Thus, although a discovery order versus size relationship could exist in three-part mineral assessments, only rarely might the pattern be useful to predict sizes of undiscovered deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2021.104566","usgsCitation":"Singer, D., and Zientek, M., 2021, Mineral deposit discovery order and three-part quantitative assessments: Ore Geology Reviews, v. 139, no. Part B, 104566, 9 p., https://doi.org/10.1016/j.oregeorev.2021.104566.","productDescription":"104566, 9 p.","ipdsId":"IP-127845","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467221,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2021.104566","text":"Publisher Index Page"},{"id":397016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396983,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2021.104566"}],"volume":"139","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Singer, Donald A. 0000-0001-6812-6441","orcid":"https://orcid.org/0000-0001-6812-6441","contributorId":288318,"corporation":false,"usgs":false,"family":"Singer","given":"Donald A.","affiliations":[],"preferred":false,"id":837729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zientek, Michael L. 0000-0002-8522-9626","orcid":"https://orcid.org/0000-0002-8522-9626","contributorId":210763,"corporation":false,"usgs":true,"family":"Zientek","given":"Michael L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":837728,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228315,"text":"70228315 - 2021 - Characterization of the biological, physical, and chemical properties of a toxic thin layer in a temperate marine system","interactions":[],"lastModifiedDate":"2022-02-08T13:03:32.116162","indexId":"70228315","displayToPublicDate":"2021-11-11T06:59:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10098,"text":"Marine Ecology Progress Series (MEPS)","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of the biological, physical, and chemical properties of a toxic thin layer in a temperate marine system","docAbstract":"The distribution of plankton in the ocean is patchy across a wide range of spatial and temporal scales. One type of oceanographic feature that exemplifies this patchiness is a ‘thin layer’. Thin layers are subsurface aggregations of plankton that range in vertical thickness from centimeters to a few meters, which may extend horizontally for kilometers and persist for days. We undertook a field campaign to characterize the biological, physical, and chemical properties of thin layers in Monterey Bay, California (USA), an area where these features can be persistent. The particle aggregates (marine snow) sampled in the study had several quantifiable properties indicating how the layer was formed and how its structure was maintained. Particles were more elongated above the layer, and then changed orientation angle and increased in size within the layer, suggesting passive accumulation of particles along a physical gradient. The shift in particle aggregate orientation angle near the pycnocline suggests that shear may also have played a role in generating the thin layer. Pseudo-nitzschia spp. were the most abundant phytoplankton within the thin layer. Further, both dissolved and particulate domoic acid were highest within the thin layer. We suggest that phosphate stress is responsible for the formation of Pseudo-nitzschia spp. aggregates. This stress together with increased nitrogen in the layer may lead to increased bloom toxicity in the subsurface blooms of Pseudo-nitzschia spp. Several zooplankton groups were observed to aggregate above and below the layer. With the knowledge that harmful algal bloom events can occur in subsurface thin layers, modified sampling methods to monitor for these hidden incubators could greatly improve the efficacy of early-warning systems designed to detect harmful algal blooms in coastal waters.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps13879","usgsCitation":"McManus, M., Greer, A.T., Timmerman, A.H., Sevadjian, J.C., Woodson, C.B., Cowen, R., Fong, D.A., Monismith, S.G., and Cheriton, O.M., 2021, Characterization of the biological, physical, and chemical properties of a toxic thin layer in a temperate marine system: Marine Ecology Progress Series (MEPS), v. 678, p. 17-35, https://doi.org/10.3354/meps13879.","productDescription":"19 p.","startPage":"17","endPage":"35","ipdsId":"IP-129200","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450228,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13879","text":"Publisher Index Page"},{"id":395606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"678","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McManus, Margaret A","contributorId":275122,"corporation":false,"usgs":false,"family":"McManus","given":"Margaret A","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":833672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greer, Adam T","contributorId":275123,"corporation":false,"usgs":false,"family":"Greer","given":"Adam","email":"","middleInitial":"T","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":833673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Timmerman, Amanda HV","contributorId":275126,"corporation":false,"usgs":false,"family":"Timmerman","given":"Amanda","email":"","middleInitial":"HV","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":833674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sevadjian, Jeff C","contributorId":275129,"corporation":false,"usgs":false,"family":"Sevadjian","given":"Jeff","email":"","middleInitial":"C","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":833675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodson, C. Brock","contributorId":275132,"corporation":false,"usgs":false,"family":"Woodson","given":"C.","email":"","middleInitial":"Brock","affiliations":[{"id":56710,"text":"School of ECAM Engineering, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":833676,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cowen, Robert","contributorId":275135,"corporation":false,"usgs":false,"family":"Cowen","given":"Robert","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":833677,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fong, Derek A","contributorId":275136,"corporation":false,"usgs":false,"family":"Fong","given":"Derek","email":"","middleInitial":"A","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":833678,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Monismith, Stephen G.","contributorId":196322,"corporation":false,"usgs":false,"family":"Monismith","given":"Stephen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":833679,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cheriton, Olivia M. 0000-0003-3011-9136","orcid":"https://orcid.org/0000-0003-3011-9136","contributorId":204459,"corporation":false,"usgs":true,"family":"Cheriton","given":"Olivia","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":833680,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70226847,"text":"70226847 - 2021 - Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland","interactions":[],"lastModifiedDate":"2021-12-15T12:40:09.70423","indexId":"70226847","displayToPublicDate":"2021-11-11T06:37:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland","docAbstract":"The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R
The Buenos Aires National Wildlife Refuge is located in the southern part of Altar Valley, southwest of Tucson in southeastern Arizona. The primary water-supply well at the Buenos Aires National Wildlife Refuge has experienced a two-decade decrease in groundwater levels in the well, as have other wells in the southern part of Altar Valley. In part to understand this trend, a study was undertaken by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, to summarize what is known about the geohydrologic system on the refuge and analyze groundwater-level trends and precipitation-groundwater correlations. In addition, available data were compiled where possible on the climate, land cover, soils, geology, and hydrology to provide a foundation for future modeling of the system.
Altar Valley is a sedimentary basin bounded by a mixture of Paleozoic to Tertiary sedimentary, volcanic, granitic, and metamorphic rocks. The valley fill is undifferentiated Tertiary to Quaternary sediments underlain by middle Miocene to Pliocene rocks that consist of moderately to strongly consolidated conglomerate and sandstone. Surface water, when present in the predominantly ephemeral streams of the valley, flows from south to north. Arivaca Creek has a cienega (or wetland) where groundwater surfaces before it flows as a short perennial reach out of Arivaca Basin. Groundwater maps compiled between 1934 and 2016 showed groundwater flowing from south to north. Before the 1980s, temporal patterns of groundwater levels in wells in Altar Valley varied substantially from one well to another. In the mid-1980s, comparatively high levels of precipitation occurred: the 1980s median value was 15.3 inches, whereas the median for the period of record was 13.2 inches. In addition, apparently corresponding groundwater level increases were seen in nearly all wells studied. After this initial increase, two different groundwater-level trends began to be observed in two spatially distinct sets of wells: in the northern part, groundwater levels were relatively steady, whereas in the southern part, groundwater levels declined from 10 to 20 feet between 1990 and 2019. Annual groundwater pumpage declined substantially in the northern part of the valley beginning in the early 1980s, but it began to increase again in the 1990s. Pumpage in the southern part has remained low and relatively steady compared to the northern part. Although the precise reasons for the declining groundwater levels in the southern part remain unclear, groundwater levels may be affected by factors such as climate cycles, long-term drought, and temperature-induced declines in recharge, resulting in increased evapotranspiration.
Preliminary analyses of two wells, one selected from each part of the valley, using linear regression and lag correlation to investigate correlation between annual precipitation and groundwater levels, showed a maximum correlation at a lag of about 17 years in the southern part of the valley and about 25 years in the northern part, indicating that, although variable sources and traveltimes of recharged water may be needed to propagate to each location, the strongest correlation at each well is with precipitation that was recharged 17 and 25 years prior to the groundwater response in that well. Assuming a constant flow of groundwater from the southern to the northern part of the valley, a decrease in recharge is expected to lead to a decrease in aquifer storage. As to the comparatively stable groundwater levels in the northern part, pumpage is still only about one-half what it was in the early 1980s, even though pumpage has increased there since the 1990s. Water levels in most wells in the northern part were drawn down prior to the decrease in pumping in the early 1980s, possibly owing to a combination of pumping and the nearly 20-year midcentury drought that occurred between 1940 and 1960. Water levels were in the process of recovering when the increase in pumping occurred in the 1990s. Because the water levels were recovering (increasing) instead of remaining static, the increased pumping may have only limited the recovery rather than causing a decrease in water levels, as a new quasi-equilibrium state may have been reached. Additional possible causes for the stable groundwater levels include (1) upgradient aquifer transmissivity that was high enough to offset pumping, (2) a low-permeability barrier, such as bedrock or clay, at the north end of the valley that caused groundwater pooling, (3) higher lateral inflow of groundwater in the northern part of the valley, (4) a delay in the effect of storage declines propagating from the south, or (5) some combination thereof.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215050","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Owen-Joyce, S.J., Callegary, J.B., and Rosebrough, A.E., 2021, Preliminary geohydrologic assessment of Buenos Aires National Wildlife Refuge, Altar Valley, southeastern Arizona: U.S. Geological Survey Scientific Investigations Report 2021–5050, 29 p., https://doi.org/10.3133/sir20215050.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-118417","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":391517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5050/sir20215050.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391518,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QST8OX","linkHelpText":"Groundwater well data and annual groundwater pumpage data (1984–2019) in Altar Valley, Arizona"},{"id":391516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5050/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Altar Valley, Buenos Aires National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.56341552734375,\n 31.459125370764387\n ],\n [\n -111.34780883789062,\n 31.459125370764387\n ],\n [\n -111.34780883789062,\n 31.81864727496152\n ],\n [\n -111.56341552734375,\n 31.81864727496152\n ],\n [\n -111.56341552734375,\n 31.459125370764387\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The Great Basin in the western United States hosts various hydrothermal systems, including both active geothermal systems and ancient systems preserved as mineral deposits. New magnetotelluric and structural geologic data were collected in the Gabbs Valley area of western Nevada to demonstrate the advantage of imaging the full crustal column below known hydrothermal systems. Three-dimensional models are developed and jointly interpreted where the key findings are bottom-up and top-down controls on hydrothermal systems. Bottom-up control is dictated by weaknesses in the brittle-ductile transition that allow hydrothermal fluids to propagate into the crust; these are often collocated with Miocene volcanic structures. Top-down control is dominated by modern Walker Lane and Basin and Range tectonics that control fluid transport through the middle and upper crust. This study demonstrates that the characterization of regional mineral and geothermal resources is better informed by imaging lower crustal structures and preferential pathways to the surface.
Artificial neural networks (ANNs) are adaptable systems that can solve problems that are difficult to describe with a mathematical relationship. They seek relationships between different types of datasets with their abilities to learn either with supervision or without. ANNs recognize patterns between input and output space and generalize solutions, in a way simulating the human brain’s learning experience with many relatively simple individual processing elements, called neurons. Neurons are networked (network topology) in a number of ways depending on the problem type and complexity. One of the most widely used ANN learning techniques is supervised learning coupled with a multilayer perceptron (MLP) topology due to its flexible applicability to a wide range of modeling problems involving both general classification and regression. ANNs, due to this flexibility, have been applied to many fields since the 1990s and their theory, types (such as radial basis functions, random...
Anas acuta (Northern pintail; hereafter pintail) was selected as a model species on which to base a decision-support framework linking regional actions to continental-scale population and harvest objectives. This framework was then used to engage stakeholders, such as Landscape Conservation Cooperatives’ (LCCs’) habitat management partners within areas of importance to pintails, while maximizing cross-taxa effects from the framework. The mathematical framework for the model had been previously developed for pintails. A key assumption incorporated into the model is that density dependence in survival occurs during the post-hunting (winter) period, where resources are hypothesized to be limiting. Because few data are available to directly inform this process, the approach used was to build a hierarchical Bayesian integrated population model (IPM) that simultaneously uses data from bird-band recoveries, breeding population counts, and harvest surveys to estimate values of parameters of an annual population projection model, including population size, survival rate, reproductive rate, and process and observation error variances, that are logically consistent with each other, given the mathematical structure imposed through the IPM.
The main accomplishments of this study are (1) development of an IPM for pintail to guide harvest and habitat management, (2) development of a Prairie Parkland Region breeding submodel to predict pintail productivity, (3) development of statistical methodology to estimate pintail productivity (as measured by the ratio of juvenile to adults in hunter-collected wing samples) and winter survival and to relate these estimates to covariates, and (4) illustration of how to use a model and estimated parameter values to predict pintail population size and sustainable harvest as a function of habitat.
Estimation of pintail survival from bird-banding data shows that there has been relatively little variation in survival over the period 1960–2013. A productivity model showed strong effects of breeding ground conditions, wintering-ground precipitation, and density dependence on pintail productivity. Thus, most temporal variation in pintail demographic rates has been due to effects on reproduction and not survival, including effects of breeding or wintering-ground habitat. These results indicate that habitat conservation efforts may be most effective if they focus on maintaining or increasing breeding and wintering-ground habitat to increase pintail productivity rather than pintail survival. Environmental perturbations in excess of historical experience, such as what could occur under climate change, might have meaningful effects on survival but cannot be estimated with current data. Direct effects of climate, land use, or management are likely to be greater on productivity than survival, but substantial uncertainty remains about predictions of equilibrium population size and sustainable yield.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201084","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Osnas, E.E., Boomer, G.S., Devries, J.H., and Runge, M.C., 2021, Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species: U.S. Geological Survey Open-File Report 2020–1084, 31 p., https://doi.org/10.3133/ofr20201084.","productDescription":"Report: vi, 31 p.; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-083951","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":391433,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93YTR3X","text":"USGS data release","linkHelpText":"Data release—Decision-support framework for linking regional-scale management actions to continental-scale conservation of wide-ranging species"},{"id":391431,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1084/coverthb.jpg"},{"id":391432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1084/ofr20201084.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1084"}],"contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
The Digital Shoreline Analysis System version 5 software is an add-in to Esri ArcGIS Desktop version 10.4–10.7 that enables a user to calculate rate-of-change statistics from a time series of vector shoreline positions. The Digital Shoreline Analysis System provides an automated method for establishing measurement locations, performs rate calculations, provides the statistical data necessary to assess the reliability of the rates, and includes a beta model for forecasting shoreline position. The Digital Shoreline Analysis System version 5.1 includes updates to the interface and the application of proxy-datum bias. This in-depth user guide provides comprehensive instruction on the installation and use of the program, including how to create a reference baseline for measurements, steps needed to generate measurement transects and metadata, guidelines on how to manually add or edit existing transects, and an explanation of the visualization options to display calculated rates of shoreline change.
Note: As of May 2024, the current version of the Digital Shoreline Analysis System (DSAS), version 6.0, is a standalone desktop application for calculating shoreline or boundary change over time. The user guide for DSAS version 5.1 is applicable to many aspects of version 6.0. The user guide provides relevant information on the DSAS workflow, including how to define a reference baseline for measurements, attribute requirements for baselines and shorelines, and supporting information on rate calculations and statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211091","usgsCitation":"Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2021, Digital Shoreline Analysis System (DSAS) version 5.1 user guide: U.S. Geological Survey Open-File Report 2021–1091, 104 p., https://doi.org/10.3133/ofr20211091.","productDescription":"Report: xi, 104 p.; Software Release","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123671","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":390774,"rank":4,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/whcmsc/science/digital-shoreline-analysis-system-dsas","text":"Digital Shoreline Analysis System (DSAS)"},{"id":390775,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P13WIZ8M","text":"Digital Shoreline Analysis System version 6.0"},{"id":390767,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1091/ofr20211091.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1091"},{"id":390766,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1091/coverthb.jpg"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
Wildfire-induced changes to soil and vegetation promote runoff-generated debris flows in steep watersheds. Postfire debris flows are most commonly observed in steep watersheds during the first wet season following a wildfire, but it is unclear how long the elevated threat of debris flow persists and why debris-flow potential changes in recovering burned areas. This work quantifies how rainfall intensity-duration (ID) thresholds for debris-flow initiation change with time since burning and provides a mechanistic explanation for these changes. We constrained a hydrologic model using field and remotely sensed measurements of soil-infiltration capacity, vegetation cover, runoff, and debris-flow activity. We applied this model to estimate rainfall ID thresholds for debris-flow initiation within three burned areas in the southwestern United States over a postfire recovery period of three to four years. Modeling suggests ID thresholds are lowest immediately following the fire (below a one-year recurrence interval [RI] storm) and increase with time, such that a 10- to 25-year RI storm would be required to generate a debris flow after three years of recovery. Modeled changes in rainfall ID thresholds result from increases in soil infiltration capacity, canopy interception, hydraulic roughness, and median grain size of sediment entrained in an incipient debris flow. The relative importance of each of these factors varied among our three sites. Results improve our ability to assess temporal changes in postfire debris-flow potential, highlight how site-specific factors may alter the persistence of postfire debris-flow hazards, and provide additional constraints on the timescale of recovery following wildfire.
During September and October 2019, the U.S. Geological Survey mapped the shoreface and inner continental shelf offshore of the Rockaway Peninsula in New York using high-resolution chirp seismic reflection and single-beam bathymetry geophysical techniques. The results from this study are important for assessing the Quaternary evolution of the Rockaway Peninsula and determining coastal sediment availability, which is crucial for establishing sediment budgets, understanding sediment dispersal, and managing coastlines. This report presents preliminary interpretations of seismic profiles and maps of shoreface and Holocene sediment thickness from the shoreline to about 2 kilometers offshore. The results indicate that shoreface and Holocene sediment thickness demonstrates zonal variability because of underlying geology and sediment availability. Based on geomorphic features and underlying stratigraphy, the study area is separated into west, west-central, east-central, and east zones. Holocene sediment, which includes the shoreface and seafloor features with positive morphology (for example, nearshore bars, ebb-tide deltas, and sorted bedforms), thickens to the west and may be related to accommodation and westward dip of the regional unconformity. Shoreface units, which are thought to represent the active volume of littoral sediment, are thickest in the west-central peninsula where the geologic base of the shoreface is deeper. Shoreface units with moderate thickness are in the western and eastern peninsula where there are positive morphological features (for example, deposits accumulating updrift from the jetty, ebb-tide deltas, and so on). The thinnest shorefaces are in the east-central Rockaway Peninsula because of less accommodation caused by the shoaling regional unconformity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211100","collaboration":"Prepared in cooperation with the National Fish and Wildlife Foundation","usgsCitation":"Wei, E.A., Miselis, J.L., and Forde, A.S., 2021, Shoreface and Holocene sediment thickness offshore of Rockaway Peninsula, New York: U.S. Geological Survey Open-File Report 2021–1100, 14 p., https://doi.org/10.3133/ofr20211100.","productDescription":"Report: iv, 14 p.; 2 Data Releases","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125818","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":391426,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211100/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":391345,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1100/images/"},{"id":391343,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZO8QKJ","linkHelpText":"Archive of chirp subbottom profile data collected in 2019 from Rockaway Peninsula, New York"},{"id":391346,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1100/ofr20211100.XML"},{"id":391344,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WNJSFN","linkHelpText":"Coastal bathymetry and backscatter data collected in September and October 2019 from Rockaway Peninsula, New York"},{"id":391342,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1100/ofr20211100.pdf","text":"Report","size":"11.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1100"},{"id":391341,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1100/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Rockaway Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.76152038574219,\n 40.57067539946112\n ],\n [\n -73.74229431152344,\n 40.593620934177494\n ],\n [\n -73.76083374023438,\n 40.59414233212419\n ],\n [\n -73.82469177246094,\n 40.58527801407785\n ],\n [\n -73.8885498046875,\n 40.563372896916164\n ],\n [\n -73.92974853515625,\n 40.549287249082035\n ],\n [\n -73.94622802734375,\n 40.53937335015618\n ],\n [\n -73.9441680908203,\n 40.529979881843865\n ],\n [\n -73.92974853515625,\n 40.526326510744006\n ],\n [\n -73.883056640625,\n 40.53311118427234\n ],\n [\n -73.83018493652344,\n 40.54772199417569\n ],\n [\n -73.77388000488281,\n 40.56389453066509\n ],\n [\n -73.76152038574219,\n 40.57067539946112\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701