Flushing time, the time scale for exchange and mixing between embayed and oceanic waters in an estuary, plays an integral role in determining water quality and aquatic ecosystem health. Here, we investigated the spatiotemporal variability of flushing times throughout Morro Bay, a short, low-inflow estuary (LIE) on the California coast, using a calibrated and validated hydrodynamic model (Delft3D). Morro Bay has historically supported an extensive eelgrass (Zostera marina) habitat, which declined substantially from 139 to 5.4 ha during 2007–2017. Eelgrass decline motivated the current research into the role of changing bed roughness and oceanic drivers (i.e., tide and sea-level rise) on estuarine hydrodynamics and flushing times. We found that tidal variability exerts the strongest control on flushing times compared to other effects, i.e., bed roughness or sea-level rise. Additionally, we found that increasing sea level and decreasing bed roughness (associated with declining seagrass coverage) yielded higher rates of mixing (lower flushing times). We detected a strong correspondence between areas having shorter flushing times (e.g., near the estuary mouth) and areas occupied by resilient eelgrass populations in Morro Bay. Our findings further indicated that flushing times in short LIEs are particularly sensitive to several factors (e.g., bed roughness, sea level) that are susceptible to anthropogenic disturbance and future climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2023.108277","usgsCitation":"Taherkhani, M., Vitousek, S., Walter, R.K., O’Leary, J., and Khodadoust, A.P., 2023, Flushing time variability in a short, low-inflow estuary: Estuarine, Coastal and Shelf Science, v. 284, 108277, 16 p., https://doi.org/10.1016/j.ecss.2023.108277.","productDescription":"108277, 16 p.","ipdsId":"IP-149301","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443542,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2023.108277","text":"Publisher Index Page"},{"id":417031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.86550507818211,\n 35.37559917233456\n ],\n [\n -120.8609393073724,\n 35.352727225376455\n ],\n [\n -120.86354831926357,\n 35.33197729831399\n ],\n [\n -120.87072310196481,\n 35.306963896126035\n ],\n [\n -120.834849188459,\n 35.3218664291451\n ],\n [\n -120.81267258738302,\n 35.32931666587521\n ],\n [\n -120.82441314089395,\n 35.36389805553908\n ],\n [\n -120.86550507818211,\n 35.37559917233456\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"284","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taherkhani, Mohsen","contributorId":223951,"corporation":false,"usgs":false,"family":"Taherkhani","given":"Mohsen","affiliations":[{"id":18137,"text":"University of Illinois at Chicago","active":true,"usgs":false}],"preferred":false,"id":872546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walter, Ryan K.","contributorId":241045,"corporation":false,"usgs":false,"family":"Walter","given":"Ryan","email":"","middleInitial":"K.","affiliations":[{"id":16725,"text":"California Polytechnic State University, San Luis Obispo","active":true,"usgs":false}],"preferred":false,"id":872548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Leary, Jennifer","contributorId":305371,"corporation":false,"usgs":false,"family":"O’Leary","given":"Jennifer","email":"","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":872549,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Khodadoust, Amid P.","contributorId":305372,"corporation":false,"usgs":false,"family":"Khodadoust","given":"Amid","email":"","middleInitial":"P.","affiliations":[{"id":18137,"text":"University of Illinois at Chicago","active":true,"usgs":false}],"preferred":false,"id":872550,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269799,"text":"70269799 - 2023 - Assessment and characterization of ephemeral stream channel stability in the Grand Valley, Colorado, 2018-22","interactions":[],"lastModifiedDate":"2025-08-04T13:54:33.905692","indexId":"70269799","displayToPublicDate":"2023-05-15T08:44:42","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessment and characterization of ephemeral stream channel stability in the Grand Valley, Colorado, 2018-22","docAbstract":"The purpose of this study is to provide information regarding the stability of ephemeral streams on the north side of the Grand Valley, Colorado. The ungaged ephemeral streams in this semiarid region are of particular interest because (1) the underlying bedrock geology, Mancos Shale, is a sedimentary rock deposit that has been identified as a major contributor of salinity to the Colorado River and (2) despite infrequent flows of short duration, monsoon derived floods in these ephemeral streams can carry substantial amounts of sediment downstream, affecting up and downstream banks and channel cross sections. The study area is of interest as salinity, or the total dissolved solids concentration, in the Colorado River causes an estimated $300 to $400 million per year in economic damages in the United States and it is estimated that 62% of Upper Colorado River Basin dissolved-solid loads originate from geologic sources. In an effort to minimize salt contributions to the Colorado River from public lands administered by the Bureau of Land Management (BLM) a comprehensive three-pronged salinity control approach is being used which incorporates (1) controlling point sources of salinity; (2) controlling nonpoint sources of salinity; and (3) preventing nonpoint sources of salinity from persisting.\n\nIn 2018, the U.S. Geological Survey, in cooperation with BLM, began an assessment of ephemeral streams located in the north side of the Grand Valley, Colorado, to characterize stream channel stability. The USGS developed a method for automatically extracting channel cross-section geometry from existing remotely sensed terrain models. Based on estimated flood stage and surrogate streamflows, hydraulic characteristics were calculated. Furthermore, the channel geometries and hydraulic characteristics were used to estimate channel stability utilizing a statistical model. \n\nIn this ongoing study, cross-section stabilities were determined from a stream channel stability assessment for a subset of 1,406 visited locations out of a desired 13,415 cross sections which were delineated from remotely sensed terrain models. The application of Manning’s resistance equation in combination with multiple Logistic Regression models demonstrated that channel stability can be estimated with an 0.85 goodness of fit for a validation dataset when using a combination of drainage area, width to depth ratio, sinuosity, and shear stress as the explanatory variables. Stream channel stability was extrapolated for the remaining 13,415 unvisited cross sections using the multiple Logistic Regression model and defined explanatory variables. Mapping the ephemeral streams and their associated stabilities could be used to prioritize areas for BLM remediation or changes in management strategies to reduce sediment and salinity loading to the Colorado River.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"SEDHYD","usgsCitation":"Homan, J.W., 2023, Assessment and characterization of ephemeral stream channel stability in the Grand Valley, Colorado, 2018-22, in Proceedings of SEDHYD 2023, 11 p.","productDescription":"11 p.","ipdsId":"IP-148840","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":493408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493407,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/past/"}],"country":"United States","state":"Colorado","otherGeospatial":"Grand Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.0445924965195,\n 39.26086521028847\n ],\n [\n -109.0445924965195,\n 38.99358861682228\n ],\n [\n -108.24066157334559,\n 38.99358861682228\n ],\n [\n -108.24066157334559,\n 39.26086521028847\n ],\n [\n -109.0445924965195,\n 39.26086521028847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Homan, Joel William 0000-0002-6709-123X","orcid":"https://orcid.org/0000-0002-6709-123X","contributorId":315495,"corporation":false,"usgs":true,"family":"Homan","given":"Joel","email":"","middleInitial":"William","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944644,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70243604,"text":"70243604 - 2023 - A numerical investigation of the mechanisms controlling salt intrusion in the Delaware Bay Estuary","interactions":[],"lastModifiedDate":"2023-05-15T14:03:31.182073","indexId":"70243604","displayToPublicDate":"2023-05-15T08:43:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"A numerical investigation of the mechanisms controlling salt intrusion in the Delaware Bay Estuary","docAbstract":"Salinity intrusion in coastal systems is mainly controlled by freshwater inflows. However, extreme events like drought, low-pressure storms, and longer-term sea level rise can exacerbate the landward salt migration and threaten economic infrastructure and ecological health. Along the eastern seaboard of the United States, approximately 13 million people rely on the water resources of the Delaware River basin. Salinity intrusion is actively managed through river discharge targets to suppress the propagation of the salt front (∼0.52 daily averaged psu line). The purpose of this study is to examine the mechanisms controlling the location of the salt front in the Delaware Bay estuary using a calibrated three-dimensional hydrodynamic model, the Coupled Ocean Atmosphere Wave and Sediment Transport modeling system. This study explored how river discharge, tidal motions, interactions with bathymetric and topographic features, and meteorological events affected the location of the salt front. The model was forced with tides, subtidal water levels, bulk atmospheric conditions, and waves. Compared with the observationally derived location of the salt front line, the model captured the major dynamics throughout the year and performed particularly well during times of low discharge, when salinity intruded up estuary at a constant rate of 0.4 km/day. The daily average salt front moved almost 16 km (10 mi) within a neap-spring tidal cycle, and low-pressure storm systems were found to move the daily averaged salt front by 13–16 km in one event.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2023.108257","usgsCitation":"Cook, S.E., Warner, J.C., and Russell, K.L., 2023, A numerical investigation of the mechanisms controlling salt intrusion in the Delaware Bay Estuary: Estuarine, Coastal and Shelf Science, v. 283, 108257, 16 p., https://doi.org/10.1016/j.ecss.2023.108257.","productDescription":"108257, 16 p.","ipdsId":"IP-144529","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443551,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2023.108257","text":"Publisher Index Page"},{"id":417021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, Pennsylvania","otherGeospatial":"Delaware Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.11619955096559,\n 38.73763237303737\n ],\n [\n -74.81060422137368,\n 39.208395097691294\n ],\n [\n -74.91812850400805,\n 39.25661375840713\n ],\n [\n -75.01433444110185,\n 39.41418851915901\n ],\n [\n -75.23504217914052,\n 39.44041644236643\n ],\n [\n -75.38218067116583,\n 39.43604580737218\n ],\n [\n -75.48404578102958,\n 39.50157653385622\n ],\n [\n -75.42745405332741,\n 39.58449350233724\n ],\n [\n -75.438772398868,\n 39.70650629113666\n ],\n [\n -75.35954398008468,\n 39.815264281891444\n ],\n [\n -75.1218587237355,\n 39.854375102531634\n ],\n [\n -75.07658534157395,\n 39.96289938512186\n ],\n [\n -74.82192256691424,\n 40.092901497017834\n ],\n [\n -74.68610242042881,\n 40.11454442418824\n ],\n [\n -74.73703497536101,\n 40.244257356638315\n ],\n [\n -74.80494504860373,\n 40.2312972236667\n ],\n [\n -74.88417346738633,\n 40.131853808272524\n ],\n [\n -75.16713210589779,\n 39.98458360741816\n ],\n [\n -75.21240548805935,\n 39.906488214275356\n ],\n [\n -75.38218067116583,\n 39.87609371208512\n ],\n [\n -75.50102329934073,\n 39.81091726046725\n ],\n [\n -75.58591089089398,\n 39.69779823272037\n ],\n [\n -75.65948013690662,\n 39.61937626699262\n ],\n [\n -75.61420675474504,\n 39.54086534174442\n ],\n [\n -75.61986592751498,\n 39.47100320828065\n ],\n [\n -75.438772398868,\n 39.199624508869135\n ],\n [\n -75.438772398868,\n 39.16014330842418\n ],\n [\n -75.438772398868,\n 39.06354039027241\n ],\n [\n -75.35388480731474,\n 38.997598861666404\n ],\n [\n -75.33690728900422,\n 38.89636906587077\n ],\n [\n -75.11619955096559,\n 38.73763237303737\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Salme Ellen 0000-0003-1129-6209","orcid":"https://orcid.org/0000-0003-1129-6209","contributorId":303775,"corporation":false,"usgs":true,"family":"Cook","given":"Salme","email":"","middleInitial":"Ellen","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Russell, Kendra L. 0000-0002-3046-7440","orcid":"https://orcid.org/0000-0002-3046-7440","contributorId":218135,"corporation":false,"usgs":true,"family":"Russell","given":"Kendra","email":"","middleInitial":"L.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"preferred":true,"id":872581,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246609,"text":"70246609 - 2023 - Environmental, morphological, and molecular data reveal a new species of freshwater mussel, Strophitus howellsi, endemic to the Edwards Plateau in Texas","interactions":[],"lastModifiedDate":"2023-10-11T15:32:56.36612","indexId":"70246609","displayToPublicDate":"2023-05-15T06:39:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Environmental, morphological, and molecular data reveal a new species of freshwater mussel, Strophitus howellsi, endemic to the Edwards Plateau in Texas","docAbstract":"Freshwater mussels are considered the most imperiled group of organisms in North America and systematics research has played an integral role in the development and implementation of their conservation. Despite the importance of systematics in conservation planning, the evolutionary relationships between many mussel taxa remain poorly explored, clearly illustrated by Strophitus undulatus. This species is wide-ranging, occurring in streams across the United States and Canada with a disjunct population in the Colorado River drainage in central Texas. The widespread distribution of S. undulatus, as well as high intraspecific morphological variation, has led previous authors to doubt the taxon is representative of a single species. In this study, we set out to investigate species boundaries in S. undulatus by integrating environmental, molecular, and morphological datasets. Molecular and morphological data supported S. undulatus from the Colorado River as distinct, which was supplemented by a species distribution modeling approach, suggesting potential adaptation to Edwards Plateau streams has contributed to speciation. Given our findings, we formally describe a new species of freshwater mussel, Strophitus howellsi, endemic to streams along the Edwards Plateau in the Colorado River drainage. A conservation assessment of S. howellsi suggests the species is extremely rare within a highly restricted distribution and may warrant future recovery actions. Our findings build on a growing body of literature highlighting aquatic endemism along the Edwards Plateau and have significant conservation implications for freshwater mussels in Texas.
Wildfire has been shown to increase, decrease, or have no detectable effect on actual evapotranspiration (ETa) fluxes in the western United States. Where disturbance-induced shifts are significant, source-water hydrology may be impacted as ETa constitutes the largest outgoing water flux in much of the arid West. We conducted pixel-scale analysis of 30-m ETa data and various meteorologic and landscape variables at 13 burn scars to understand how wildfire disturbance impacted hillslope-to-burn scar-scale hydrology and vegetation conversion. Significant fire-induced ETa reductions (between approximately −15 to −50%) were detected at nine burn scars through the tenth post-fire year, while ETa recovery rate varied substantially by ecoregion and pre-fire vegetation type. Along elevation gradients, both climate and land disturbance influenced the location of runoff/recharge generation zones, and more net water was generated from a snow-dominated burn scar in dry post-fire years than in wet pre-fire years. However, especially in arid locations where ETa is water-limited, compensatory ETa pathways may be more likely to dampen fire effects on total basin water yield where intact vegetation is located between the disturbance footprint and the basin outlet. Relationships between post-fire ETa shifts and early-successional vegetation conversion were also tracked. The majority of burn scars with significant fire-induced ETa reductions experienced conversion patterns typical of the western United States following stand-replacing disturbance, with forests converting to shrub/scrub and/or grassland/herbaceous cover through at least the end of the study period (eight to 15 years depending on date of the fire event). This could have important implications for high-elevation, snow-dominated watersheds – some of the most critical source water areas - as previous research indicates that wildfire activity is moving upslope and into vegetation communities that have not evolved to withstand fire. Finally, we show that much of the Colorado River Basin’s high-yield source water areas are vulnerable to the fire-induced ETa reductions and vegetation conversion observed herein. As such, water managers in the Colorado River Basin can anticipate changes in burn scar hydrology and snowpack mechanics following fire disturbance.
Prior to the current era of digital geomorphological mapping, global and regional-scale land surface characterization was advanced by qualitative interpretations that relied on human visualization aided by disciplinary knowledge of geophysical processes combined with extensive field study. In the early twentieth century, Fenneman proposed to devise systematic physiographic divisions of the United States and in 1916 produced what is still regarded as an authoritative map of these divisions. His physiographic regions were developed to provide context when describing land surface characteristics of smaller areas using well-known regional characteristics and descriptors. In 1968, geographer Richard E. Murphy published a large-format map of the “Landforms of the World” to fill a gap in the suite of standard classroom maps. In 1990, the British geomorphologist E. M. Bridges published World Geomorphology, providing the first global treatment and description of divisions, provinces, and sections—the same hierarchical land partitioning concepts that Fenneman used decades earlier. In the twenty-first century, geographic information systems (GIS) technologies are nearly ubiquitous, yet neither Murphy’s nor Bridges’s work existed as GIS data. To further illuminate their pioneering work, we (1) recompiled Murphy’s landforms as a spatial combination of modern existing data layers, and (2) used the recompiled Murphy’s landforms as a basis for the boundaries of the divisions, provinces, and sections described by Bridges. Our aggregation yields a new resource, Named Landforms of the World, version 2.0, which provides a reference-level, basemap-quality data layer that can significantly facilitate mapping, assessing, and understanding Earth surface features.
Io is unlike any other body in the Solar System making questions about its chemical composition especially interesting and challenging. This chapter examines the many different, but frustratingly indirect, constraints we have on the bulk composition of this restless moon. A detailed consideration of Io’s lavas is used to illustrate how decades of research have bounded, but not pinned down, the chemistry of Io. A self-consistent model for the core, mantle and crust is constructed based on a conventional chondritic composition but exotic alternatives cannot be ruled out. The study of Io’s composition should provide a fertile and exciting realm for future scientists.
Information about population sizes, trends, and habitat use is key for species conservation and management. The Buff-breasted Sandpiper Calidris subruficollis (BBSA) is a long-distance migratory shorebird that breeds in the Arctic and migrates to south-eastern South America, wintering in the grasslands of southern Brazil, Uruguay, and Argentina. Most studies of Nearctic migratory species occur in the Northern Hemisphere, but monitoring these species at non-breeding areas is crucial for conservation during this phase of the annual cycle. Our first objective was to estimate trends of BBSA at four key areas in southern Brazil during the non-breeding season. We surveyed for BBSA and measured vegetation height in most years from 2008/09 to 2019/20. We used hierarchical distance sampling models in which BBSA abundance and density were modelled as a function of vegetation height and corrected for detectability. Next, we used on-the-ground surveys combined with satellite imagery and habitat classification models to estimate BBSA population size in 2019/20 at two major non-breeding areas. We found that abundance and density were negatively affected by increasing vegetation height. Abundance fluctuated five- to eight-fold over the study period, with peaks in the middle of the study (2014/15). We estimated the BBSA wintering population size as 1,201 (95% credible interval [CI]: 637–1,946) birds in Torotama Island and 2,232 (95% CI: 1,199–3,584) in Lagoa do Peixe National Park during the 2019/20 austral summer. Although no pronounced trend was detected, BBSA abundance fluctuated greatly from year to year. Our results demonstrate that only two of the four key areas hold high densities of BBSA and highlight the positive effect of short grass on BBSA numbers. Short-grass coastal habitats used by BBSA are strongly influenced by livestock grazing and climate, and are expected to shrink in size with future development and climatic changes.
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S.","affiliations":[{"id":66213,"text":"University of Castilla La Mancha","active":true,"usgs":false}],"preferred":false,"id":872368,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":872369,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243557,"text":"70243557 - 2023 - Survival of Common Loon chicks appears unaffected by Bald Eagle recovery in northern Minnesota","interactions":[],"lastModifiedDate":"2023-05-23T14:57:20.200377","indexId":"70243557","displayToPublicDate":"2023-05-12T06:47:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Survival of Common Loon chicks appears unaffected by Bald Eagle recovery in northern Minnesota","docAbstract":"Recovering species are not returning to the same environments or communities from which they disappeared. Conservation researchers and practitioners are thus faced with additional challenges in ensuring species resilience in these rapidly changing ecosystems. Assessing the resilience of species in these novel systems can still be guided by species’ ecology, including knowledge of their population size, life history traits, and behavioral adaptations, as well as the type, strength, and number of ways that they interact with other species in the community. We summarized broad trends of Common Loons (Gavia immer) breeding at Voyageurs National Park from 1973 to 2009, and evaluated the effects of increased risk from recovering Bald Eagles (Haliaeetus leucocephalus) on chick survival from 2004 to 2006. Adult Common Loons appear to have increased over time. Using Bayesian survival models that accounted for imperfect detection of unmarked individuals, we determined that chick survival of Common Loons was high from year to year and was unrelated to predation risk from Bald Eagles because chicks in territories closer to active nests did not experience greater mortality than those farther away. We suggest that Common Loon chicks were unaffected by the recovery of this top predator during the three years of sampling. Previous research indicates that Bald Eagles and other predators are an important source of egg losses, but Common Loons can compensate by re-nesting. Despite current uncertainties from anthropogenic threats, knowledge of a species’ ecology remains instrumental in determining its resilience during recovery.
","language":"English","publisher":"Society of Canadian Ornithologists","doi":"10.5751/ACE-02395-180107","usgsCitation":"Cruz, J., Windels, S.K., Thogmartin, W.E., Crimmins, S.M., and Zuckerberg, B., 2023, Survival of Common Loon chicks appears unaffected by Bald Eagle recovery in northern Minnesota: Avian Conservation and Ecology, v. 18, no. 1, 7, 10 p., https://doi.org/10.5751/ACE-02395-180107.","productDescription":"7, 10 p.","ipdsId":"IP-139148","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":443574,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-02395-180107","text":"Publisher Index Page"},{"id":416982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Voyageurs National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.45997931356037,\n 48.555906738582024\n ],\n [\n -93.45335928031174,\n 48.39059241959737\n ],\n [\n -92.75604911141959,\n 48.389127056374264\n ],\n [\n -92.59716831344394,\n 48.26147889892954\n ],\n [\n -92.41622073797222,\n 48.29672409407081\n ],\n [\n -92.47359435946323,\n 48.36860754048001\n ],\n [\n -92.44711422646755,\n 48.399383712816814\n ],\n [\n -92.45594093746577,\n 48.414032492614126\n ],\n [\n -92.50890120345764,\n 48.45209957470354\n ],\n [\n -92.53979469195295,\n 48.45063598351126\n ],\n [\n -92.64792190168626,\n 48.44185355044462\n ],\n [\n -92.68984877892986,\n 48.449172350133125\n ],\n [\n -92.70529552317724,\n 48.46234353186699\n ],\n [\n -92.69426213442897,\n 48.49160059158902\n ],\n [\n -92.61482173544141,\n 48.497449978895816\n ],\n [\n -92.6302684796888,\n 48.54714252606428\n ],\n [\n -92.65674861268499,\n 48.54860333357004\n ],\n [\n -92.72736230067383,\n 48.53983785600303\n ],\n [\n -92.94582339789001,\n 48.61283712114866\n ],\n [\n -92.95023675338965,\n 48.634716347957436\n ],\n [\n -92.98995695288315,\n 48.62596579539513\n ],\n [\n -93.10911755136476,\n 48.63034126136506\n ],\n [\n -93.17090452835536,\n 48.62596579539513\n ],\n [\n -93.20400469460023,\n 48.64492340802602\n ],\n [\n -93.25034492734294,\n 48.64638139086094\n ],\n [\n -93.35626545932672,\n 48.62596579539513\n ],\n [\n -93.37391888132419,\n 48.60991917427066\n ],\n [\n -93.42246579181642,\n 48.61283712114866\n ],\n [\n -93.47542605780829,\n 48.5967863265239\n ],\n [\n -93.45997931356037,\n 48.555906738582024\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cruz, Jennyffer","contributorId":202194,"corporation":false,"usgs":false,"family":"Cruz","given":"Jennyffer","email":"","affiliations":[{"id":36365,"text":"Department of Forest and Wildlife Ecology, University of Wisconsin – Madison","active":true,"usgs":false}],"preferred":false,"id":872356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windels, Steve K.","contributorId":182422,"corporation":false,"usgs":false,"family":"Windels","given":"Steve","email":"","middleInitial":"K.","affiliations":[{"id":18939,"text":"Voyageurs National Park","active":true,"usgs":false}],"preferred":false,"id":872357,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":872358,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crimmins, Shawn M. 0000-0001-6229-5543 scrimmins@usgs.gov","orcid":"https://orcid.org/0000-0001-6229-5543","contributorId":5498,"corporation":false,"usgs":true,"family":"Crimmins","given":"Shawn","email":"scrimmins@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":872359,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zuckerberg, Benjamin","contributorId":200298,"corporation":false,"usgs":false,"family":"Zuckerberg","given":"Benjamin","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":872360,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249210,"text":"70249210 - 2023 - Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery","interactions":[],"lastModifiedDate":"2023-10-02T11:56:19.176955","indexId":"70249210","displayToPublicDate":"2023-05-11T06:54:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery","docAbstract":"Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.
This paper describes the results of a study that was done by the USGS to assess recent (2017) water availability, forecast long-term trends in water availability, assess changes in water availability, and forecast future water availability in the Trinity River Basin in Texas. The Trinity River Basin surface water model and Trinity River alluvium aquifer (TRAA) groundwater model were created to evaluate future conditions under different global climate models (GCM). The results of this study show minimal overall changes in water availability for both surface water and groundwater. Trend analyses using historical data (1900–2017) indicated an increase of annual precipitation on the watersheds that drain into the reservoirs in Regional Water Planning Group C. However, the Trinity River Basin surface water model GCM ensemble mean annual precipitation indicates a downward trend, resulting in a downward trend in surface runoff. Additionally, the GCM ensemble mean for the Trinity River Basin surface water model and the TRAA groundwater model both indicate a downward trend in recharge while the TRAA model GCM ensemble mean indicates an upward trend in the amount of groundwater leaving the aquifer to rivers and streams resulting in an upward trend of cumulative storage change.
","language":"English","publisher":"Texas Water Journal","doi":"10.21423/twj.v14i1.7146","usgsCitation":"Milmo, M.J., McDowell, J., Yesildirek, M.V., and Harwell, G.R., 2023, The use of historical data and global climate models to assess historical and future surface water and groundwater availability in the Trinity River Basin in Texas: Texas Water Journal, v. 14, p. 34-61, https://doi.org/10.21423/twj.v14i1.7146.","productDescription":"28 p.","startPage":"34","endPage":"61","ipdsId":"IP-126619","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":443587,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.21423/twj.v14i1.7146","text":"Publisher Index Page"},{"id":435342,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BVOEJ3","text":"USGS data release","linkHelpText":"Hydrologic simulations using projected climate data as input to the Precipitation-Runoff Modeling System (PRMS) for the Trinity River Basin Integrated Water Availability Assessment, Texas, 2023"},{"id":435341,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XO5F9G","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to assess historical and future trends in groundwater availability in the Trinity River alluvium aquifer, Texas"},{"id":416955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Trinity River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.0568823566713,\n 29.605130308347057\n ],\n [\n -94.82111695509118,\n 29.47268831477325\n ],\n [\n -94.56341988824927,\n 29.61102156169673\n ],\n [\n -94.98560444456466,\n 31.578939128932277\n ],\n [\n -95.40230608456515,\n 32.304435780613815\n ],\n [\n -95.72579814719658,\n 32.762064472265905\n ],\n [\n -95.93963188351171,\n 33.33195284298357\n ],\n [\n -96.42212851930111,\n 33.574406176811095\n ],\n [\n -97.90799884087924,\n 33.77061473287188\n ],\n [\n -98.1218325771938,\n 33.49213956579713\n ],\n [\n -97.53516053140494,\n 32.558961831908476\n ],\n [\n -96.68530850245952,\n 32.141316395990245\n ],\n [\n -96.30698727667026,\n 31.217473563861276\n ],\n [\n -95.89576855298657,\n 30.48316869632812\n ],\n [\n -95.23781859509266,\n 29.709952489208263\n ],\n [\n -95.0568823566713,\n 29.605130308347057\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","edition":"1","noUsgsAuthors":false,"publicationDate":"2023-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Milmo, Molly J. 0000-0001-9074-0982","orcid":"https://orcid.org/0000-0001-9074-0982","contributorId":245854,"corporation":false,"usgs":true,"family":"Milmo","given":"Molly","email":"","middleInitial":"J.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDowell, Jeremy 0000-0002-8132-9806","orcid":"https://orcid.org/0000-0002-8132-9806","contributorId":221296,"corporation":false,"usgs":true,"family":"McDowell","given":"Jeremy","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872224,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yesildirek, Monica Veale 0000-0002-0320-8531","orcid":"https://orcid.org/0000-0002-0320-8531","contributorId":228880,"corporation":false,"usgs":true,"family":"Yesildirek","given":"Monica","email":"","middleInitial":"Veale","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872225,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harwell, Glenn R. 0000-0003-4265-2296","orcid":"https://orcid.org/0000-0003-4265-2296","contributorId":205197,"corporation":false,"usgs":true,"family":"Harwell","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872226,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243688,"text":"70243688 - 2023 - Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data","interactions":[],"lastModifiedDate":"2023-05-17T13:49:27.788978","indexId":"70243688","displayToPublicDate":"2023-05-10T08:48:03","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data","docAbstract":"Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)-based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three-layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three-layer composite generally provided lower overall losses for the training samples, a higher F1-score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN-based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN-based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three-layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023EA002845","usgsCitation":"Maxwell, A.E., Odom, W.E., Shobe, C.M., Doctor, D.H., Bester, M.S., and Ore, T., 2023, Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data: Earth and Space Science, v. 10, no. 5, e2023EA002845, 25 p., https://doi.org/10.1029/2023EA002845.","productDescription":"e2023EA002845, 25 p.","ipdsId":"IP-150908","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443593,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023ea002845","text":"Publisher Index Page"},{"id":417130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Maxwell, Aaron E.","contributorId":305483,"corporation":false,"usgs":false,"family":"Maxwell","given":"Aaron","email":"","middleInitial":"E.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Odom, William E. 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shobe, Charles M.","contributorId":305484,"corporation":false,"usgs":false,"family":"Shobe","given":"Charles","email":"","middleInitial":"M.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bester, Michelle S.","contributorId":305485,"corporation":false,"usgs":false,"family":"Bester","given":"Michelle","email":"","middleInitial":"S.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872920,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ore, Tobi","contributorId":305487,"corporation":false,"usgs":false,"family":"Ore","given":"Tobi","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":872921,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243953,"text":"70243953 - 2023 - Evaluation of Copernicus DEM and comparison to the DEM used for Landsat collection-2 processing","interactions":[],"lastModifiedDate":"2023-06-12T21:50:00.086818","indexId":"70243953","displayToPublicDate":"2023-05-10T07:04:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of Copernicus DEM and comparison to the DEM used for Landsat collection-2 processing","docAbstract":"High interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. In this study, we use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production as approximated by remotely sensed vegetation data. Because, among other variables, this model uses observed summer climate data, the model output cannot be produced early enough in the year (e.g., spring months) to inform within-season management decisions. To address this issue, we developed summer climate scenarios (e.g., extremely warm and dry and moderately cool and wet) that serve as an input in the model in combination with observed winter and spring climate data from a particular year. The summer climate scenarios used historical summer precipitation and temperature data (1950–2018) categorized into three, five, and seven percentile categories. These percentile values were then combined to represent summer climate scenarios, which were further used as the ForageAhead model input. We tested the optimal number of percentile categories to be used as the model input to obtain accurate prediction of forage production while also minimizing the number of possible temperature and precipitation combinations, which increases with the number of percentile categories. For the 19-year period analysis (2000–2018), we also determined the most and least common scenarios that occurred in the western United States. When using five percentile categories for summer precipitation and temperature, we were able to capture the interannual variability in the spatial extent of abnormally low and high biomass production. The ForageAhead predictions captured similar spatial patterns of forage anomalies as another similar model (Grass-Cast). This method can be made available in a user-friendly automated system that can be used by livestock producers and rangeland managers to inform within-season management decisions. This method can be especially valuable for flexible stocking as it provides a range of possible annual forage production scenarios by the end of May.
Sex ratio, and the extent to which it varies over time, is an important factor in the demography, management, and conservation of wildlife populations. Greater sage-grouse Centrocercus urophasianus populations in western North America are monitored using counts of males at leks in spring. Population estimates derived from lek-count data typically assume a constant, female-biased sex ratio, yet few rigorous, empirically derived estimates of sex ratio are available to test that assumption. We estimated pre-breeding sex ratio of greater sage-grouse in a peripheral, geographically isolated population in northwestern Colorado during two consecutive winters using closed-population, robust-design, multi-state, genetic mark–recapture models in program MARK. Sex ratio varied markedly between years, with estimates of 3.29 (95% CI: 2.36–4.59) females per male in winter 2012–2013 and 1.54 (95% CI: 1.22–1.95) females per male in winter 2013–2014. Rather than assuming a constant sex ratio, biologists should consider the potential for large annual variation in sex ratio of greater sage-grouse populations when estimating population size or trend from male lek-count data.
The recent development of animal-borne sensors coupled with location data can provide insights into how individuals modify their behaviour with respect to specific habitat features. Animals can express a diverse array of behaviours as they navigate heterogenous landscapes, yet few studies have specifically evaluated the interaction of behaviours with habitat characteristics. We used a novel broadcast acoustic transmitter to investigate the interaction between vocal behaviours of an endemic Hawaiian thrush, the ʻōmaʻo, Myadestes obscurus, and habitat features across a naturally fragmented forest landscape. Through the development of behavioural landscape models that link specific vocalizations with space use, we found that the use of different vocalization types (calls, songs, whisper songs) were highly variable across the landscape but were associated with distinct habitat features. The likelihood of calls increased in an open lava matrix between forest patches, while whisper songs were more strongly associated with the dense interior areas of forest fragments. In contrast, the rate of ʻōmaʻo vocalizations overall decreased in the open lava matrix, suggesting that ʻōmaʻo may shift behaviours from territory defence to foraging as they transition through different habitats. Our study revealed context-specific changes in behaviour across ʻōmaʻo home ranges, including courtship, aggression and social interactions between individuals. Combining the use of a novel acoustic tool with automated radiotelemetry allowed us to overcome challenges associated with detection and analysis of variation in behaviour and resource selection across a highly heterogeneous landscape that would have been otherwise difficult to impossible.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2023.04.006","usgsCitation":"Netoskie, E.C., Paxton, K.L., Paxton, E.H., Asner, G.P., and Hart, P.J., 2023, Linking vocal behaviours to habitat structure to create behavioural landscapes: Animal Behaviour, v. 201, p. 1-11 p., https://doi.org/10.1016/j.anbehav.2023.04.006.","productDescription":"11 p.","startPage":"1","endPage":"11 p.","ipdsId":"IP-136322","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":443605,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.anbehav.2023.04.006","text":"Publisher Index Page"},{"id":420907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii, Mauna Loa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.58292548105314,\n 19.5421499770886\n ],\n [\n -155.58292548105314,\n 19.33132215765302\n ],\n [\n -155.42705047437903,\n 19.33132215765302\n ],\n [\n -155.42705047437903,\n 19.5421499770886\n ],\n [\n -155.58292548105314,\n 19.5421499770886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"201","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Netoskie, Erin C","contributorId":329790,"corporation":false,"usgs":false,"family":"Netoskie","given":"Erin","email":"","middleInitial":"C","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":883317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false},{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":883318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":883319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":883320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":883321,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256482,"text":"70256482 - 2023 - Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan","interactions":[],"lastModifiedDate":"2024-08-07T15:11:02.429549","indexId":"70256482","displayToPublicDate":"2023-05-09T09:49:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan","docAbstract":"Effective fishery management in large systems relies on understanding how individual stocks contribute to a fishery over spatial and temporal scales. The current conceptual model for management of Walleye Sander vitreus in Green Bay designates Walleye in the northern and southern parts of the bay as distinct stocks, with little mixing between the northern and southern fisheries, and assumes that Walleye in both northern and southern Green Bay primarily spawn in tributaries as opposed to shoreline or offshore reef areas. We used acoustic telemetry to test this conceptual model for Walleye management in Green Bay. Telemetry indicated that the majority of Green Bay Walleye use tributaries for spawning. However, many individuals were assigned to open-water spawning locations during consecutive years in both northern (26%) and southern (21%) Green Bay, suggesting that open-water spawners may represent a larger proportion of the Walleye stocks than previously thought. Differential movement was observed between northern and southern portions of Green Bay, with 56% of Walleye tagged in northern Green Bay crossing receiver lines to move south compared to only 19% of Walleye tagged in southern Green Bay crossing receiver lines to move north. Walleye typically transitioned across these boundaries in summer and fall, suggesting that stock contributions to the fishery in each zone may differ seasonally. Differential movements of northern Green Bay Walleye may be influenced by broad-scale differences in habitat and prey availability, which are likely related to the differential effects of dreissenid mussel invasion in Green Bay. Our results suggest that adjustment of monitoring efforts to account for open-water spawners may provide a more complete picture of stock status. Additionally, more research examining potential food web effects of northern Green Bay Walleye moving into southern Green Bay may be needed to determine how these movements might influence other important species.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10883","usgsCitation":"Izzo, L., Dembkowski, D., Hayden, T., Binder, T., Christopher Vandergoot, Hogler, S., Donofrio, M., Zorn, T., Krueger, C., and Isermann, D.A., 2023, Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan: North American Journal of Fisheries Management, v. 43, no. 3, p. 695-714, https://doi.org/10.1002/nafm.10883.","productDescription":"20 p.","startPage":"695","endPage":"714","ipdsId":"IP-145656","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10883","text":"Publisher Index Page"},{"id":432339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.32040853136192,\n 44.37812529340982\n ],\n [\n -87.91846002557772,\n 44.3585534932721\n ],\n [\n -86.60288078784984,\n 45.624769896015266\n ],\n [\n -86.99575779919539,\n 45.917890769552486\n ],\n [\n -87.69003998623847,\n 45.15642614432045\n ],\n [\n -88.32040853136192,\n 44.37812529340982\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":340807,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel","contributorId":340808,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayden, Todd","contributorId":340810,"corporation":false,"usgs":false,"family":"Hayden","given":"Todd","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Binder, Tom","contributorId":340812,"corporation":false,"usgs":false,"family":"Binder","given":"Tom","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christopher Vandergoot","contributorId":340814,"corporation":false,"usgs":false,"family":"Christopher Vandergoot","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogler, Steven","contributorId":340817,"corporation":false,"usgs":false,"family":"Hogler","given":"Steven","email":"","affiliations":[{"id":81669,"text":"Wisconsin Department of Natural Resource (retired)","active":true,"usgs":false}],"preferred":false,"id":907579,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael","contributorId":340818,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael","email":"","affiliations":[{"id":81669,"text":"Wisconsin Department of Natural Resource (retired)","active":true,"usgs":false}],"preferred":false,"id":907580,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zorn, Troy","contributorId":340819,"corporation":false,"usgs":false,"family":"Zorn","given":"Troy","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907581,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles","contributorId":340820,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907582,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907583,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70249368,"text":"70249368 - 2023 - The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","interactions":[],"lastModifiedDate":"2023-10-05T12:06:20.98252","indexId":"70249368","displayToPublicDate":"2023-05-09T07:05:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","docAbstract":"Accurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo-referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.
The parasitic copepod Salmincola californiensis infects Pacific salmon and trout (Oncorhynchus spp.) and often reaches high prevalence and intensity in reservoirs compared to stream systems. Recent research indicates that temperature plays a fundamental role in copepod development and fish susceptibility. Here, we expand a linked foraging and bioenergetics model to simulate infection risk. Based on juvenile salmon vertical migration patterns, we add estimates of copepod generations produced and thermal strata metrics that appear associated with copepodid aggregations and increased infection. Severe damage on hosts may be caused by the infectious copepodid, a life-stage not readily visible and thus not detectable using traditional fish screenings. We discuss model limitations, opportunities for future research, and the potential for inclusion of copepod expansion equations to existing linked bioenergetics models or observed behaviors of salmonids in other lentic systems. We demonstrate that using a temperature sensitive model framework that includes copepod infection dynamics is useful in interpreting other lines of evidence, such as fish mortality estimates. Collectively, our work provides a testable framework for future comparisons of infection potential and demonstrates how bioenergetics models may be useful in understanding host–parasite interactions.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-023-01420-2","usgsCitation":"Murphy, C.A., Pollock, A., Johnson, S.L., and Arismendi, I., 2023, Linked foraging and bioenergetics modeling may inform fish parasite infection dynamics: Environmental Biology of Fishes, v. 106, p. 1345-1356, https://doi.org/10.1007/s10641-023-01420-2.","productDescription":"12 p.","startPage":"1345","endPage":"1356","ipdsId":"IP-147898","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","noUsgsAuthors":false,"publicationDate":"2023-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Christina Amy 0000-0002-3467-6610","orcid":"https://orcid.org/0000-0002-3467-6610","contributorId":335232,"corporation":false,"usgs":true,"family":"Murphy","given":"Christina","email":"","middleInitial":"Amy","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollock, Amanda","contributorId":150244,"corporation":false,"usgs":false,"family":"Pollock","given":"Amanda","email":"","affiliations":[{"id":17945,"text":"U.S. Fish and Wildlife Service, Pacific Islands Refuge and Monuments","active":true,"usgs":false}],"preferred":false,"id":910079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Sherri L 0000-0002-4223-3465","orcid":"https://orcid.org/0000-0002-4223-3465","contributorId":192210,"corporation":false,"usgs":false,"family":"Johnson","given":"Sherri","email":"","middleInitial":"L","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":910080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arismendi, Ivan 0000-0002-8774-9350","orcid":"https://orcid.org/0000-0002-8774-9350","contributorId":202207,"corporation":false,"usgs":false,"family":"Arismendi","given":"Ivan","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":910081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243176,"text":"sir20235028 - 2023 - Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","interactions":[],"lastModifiedDate":"2026-03-06T20:53:37.591262","indexId":"sir20235028","displayToPublicDate":"2023-05-08T11:03:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5028","displayTitle":"Development of an Integrated Hydrologic Flow Model of the Rio San Jose Basin and Surrounding Areas, New Mexico","title":"Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","docAbstract":"The Rio San Jose Integrated Hydrologic Model (RSJIHM) was developed to provide a tool for analyzing the hydrologic system response to historical water use and potential changes in water supplies and demands in the Rio San Jose Basin. The study area encompasses about 6,300 square miles in west-central New Mexico and includes the communities of Grants, Bluewater, and San Rafael and three Native American Tribal lands: the Acoma and Laguna Pueblos and the Navajo Nation. Perennial surface water features are sparse in the study area and most water resources consist of groundwater pumped from sedimentary and basalt aquifers.
Calibration of the RSJIHM was performed using PEST++ (version 4.3.20) and BeoPEST (version 13.6). Model parameter values were adjusted during calibration to fit model simulated values to the measured or estimated values for several observation groups: (1) solar radiation, (2) potential evapotranspiration, (3) actual evapotranspiration, (4) precipitation and minimum and maximum air temperature, (5) snow water equivalent, (6) snow-covered area, (7) streamflow, (8) hydraulic head, (9) springflow at Ojo del Gallo, (10) springflow at Horace Springs, (11) surface-water releases from Bluewater Lake, and (12) surface-water diversions for irrigation within the Bluewater-Toltec Irrigation District.
The simulated average annual hydrologic budget from 1950 through 2018 indicated that the majority (greater than 98 percent) of precipitation within the basin was consumed by evapotranspiration, leaving 1.2 percent to recharge the groundwater system, 0.47 percent to direct runoff to streams, and 0.20 percent to infiltrate the soil zone and interflow to streams. The average annual recharge to the groundwater system and runoff to streams simulated by the RSJIHM was about 28,000 and 11,000 acre-feet, respectively. The RSJIHM simulated about 590,000 acre-feet of cumulative aquifer storage depletion from 1950 through 2018.
Additional work that could improve the simulation capability of the RSJIHM includes (1) further data collection (streamflow, head, springflow) in the southwestern subbasin that includes the El Malpais National Monument, (2) incorporating temporally variable vegetation parameters, (3) spatial downscaling of the hydrometeorological input datasets, (4) incorporating additional spatial variability to hydraulic property parameters on the basis of new data collection, and (5) using environmental tracers to verify and calibrate model parameters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235028","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Pueblo of Acoma, and Pueblo of Laguna","usgsCitation":"Ritchie, A.B., Chavarria, S.B., Galanter, A.E., Flickinger, A.K., Robertson, A.J., and Sweetkind, D.S., 2023, Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5028, 76 p., 1 pl., https://doi.org/10.3133/sir20235028.","productDescription":"Report: x, 76 p.; 1 Plate: 25.37 x 40.38 inches; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-111893","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":416632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5028/coverthb.jpg"},{"id":416635,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235028/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5028 HTML"},{"id":416634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.XML","size":"482 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5028 XML"},{"id":416638,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028_plate1.pdf","size":"550 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028 plate 1"},{"id":416633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.pdf","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028"},{"id":416637,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRTKTM","text":"USGS data release—GSFLOW, used to run PRMS and MODFLOW-NWT models, to simulate the effects of natural and anthropogenic impacts on water resources in the Rio San Jose Basin and surrounding areas, New Mexico"},{"id":416636,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5028/Images/"},{"id":500886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114717.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5,\n 36\n ],\n [\n -108.5,\n 36\n ],\n [\n -108.5,\n 34\n ],\n [\n -106.5,\n 34\n ],\n [\n -106.5,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
New York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post-construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post-glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard.
The Paleoproterozoic Nagssugtoqidian Orogen is one of the principal tectonic features related to the assembly of Nuna, extending across Greenland from east to west and forming an orogenic belt separating the North Atlantic Craton on the south from the Rae Craton on the north. In South-East Greenland, the Ammassalik Intrusive Complex (AIC) (∼1910 to 1870 Ma) occupies the central part of the orogenic belt, was formed by subduction- and magmatic arc-related processes, and has significant potential for undiscovered deposits of critical minerals. Previous interpretations of aeromagnetic data have been hindered by terrain effects, and we use a novel mix of geophysical analysis tools to develop new tectonomagmatic interpretations of the central Nagssugtoqidian Orogen in South-East Greenland. These interpretations extend into areas covered by ocean and ice. Results show that Archean rocks of the juxtaposed North Atlantic (Isertoq Terrane) and Rae (Kuummiut Terrane) Cratons are relatively weakly magnetized (with the exception of rocks of the Schweizerland Terrane) and have a NW-striking structural fabric that likely formed or was enhanced during the Nagssugtoqidian Orogeny. The AIC is structurally complex, with weakly magnetized metasedimentary rocks, and both weakly and strongly magnetized intrusions, arrayed in a NW-striking tectonic fabric. The strongly magnetized intrusions are largely concealed and distributed in a broader and more spatially complex fashion than previously known, suggesting that additional areas may be considered for mineral exploration. Strongly magnetized NW-striking dikes are imaged within the AIC, where they are spatially closely related to the strongly magnetized intrusions, and extend southward into the North Atlantic Craton (Isertoq Terrane). This spatial pattern of arc magmatism is consistent with previously developed models of SW-directed subduction that preceded collision during the Nagssugtoqidian Orogeny. The strongly magnetized Ammassalik Batholith (∼1670 Ma) and related intrusions form a cluster of plutons within ∼ 50 km of the Nagssugtoqidian suture. Their tectonomagmatic setting is unknown, although are speculatively related to delamination of a lithospheric keel formed during the Nagssugtoqidian Orogeny ∼ 200 m.y. prior. Numerous strongly magnetized NNE-striking Paleogene dikes, related to the opening of the Atlantic Ocean, are imaged cutting most other geologic units.
Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew).
Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212).
Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region.
Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235014","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Transportation","usgsCitation":"Mitchell, J.N., Wagner, D.M., and Veilleux, A.G., 2023, Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020: U.S. Geological Survey Scientific Investigations Report 2023–5014, 66 p. plus 4 appendixes, https://doi.org/10.3133/sir20235014.","productDescription":"Report: vii, ; 8 Tables; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-139812","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":416577,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GGPPV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of 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States","state":"Hawaii","otherGeospatial":"Kauaʻi, Oʻahu, Molokaʻi, Maui, Hawaiʻi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.92521972722102,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 22.39025206306377\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Islands Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
Geodetic velocity data provide first-order constraints on crustal surface strain rates, which in turn are linked to seismic hazard. Estimating the 2-D surface strain tensor everywhere requires knowledge of the surface velocity field everywhere, while geodetic data such as Global Navigation Satellite System (GNSS) only have spatially scattered measurements on the surface of the Earth. To use these data to estimate strain rates, some type of interpolation is required. In this study, we review methodologies for strain rate estimation and compare a suite of methods, including a new implementation based on the geostatistical method of kriging, to compare variation between methods with uncertainty based on one method. We estimate the velocity field and calculate strain rates in southern California using a GNSS velocity field and five different interpolation methods to understand the sources of variability in inferred strain rates. Uncertainty related to data noise and station spacing (aleatoric uncertainty) is minimal where station spacing is dense and maximum far from observations. Differences between methods, related to epistemic uncertainty, are usually highest in areas of high strain rate due to differences in how gradients in the velocity field are handled by different interpolation methods. Parameter choices, unsurprisingly, have a strong influence on strain rate field, and we propose the traditional L-curve approach as one method for quantifying the inherent trade-off between fit to the data and models that are reflective of tectonic strain rates. Doing so, we find total variability between five representative strain rate models to be roughly 40 per cent, a much lower value than roughly 100 per cent that was found in previous studies (Hearn et al.). Using multiple methods to tune parameters and calculate strain rates provides a better understanding of the range of acceptable models for a given velocity field. Finally, we present an open-source Python package (Materna et al.) for calculating strain rates, Strain_2D, which allows for the same data and model grid to be used in multiple strain rate methods, can be extended with other methods from the community, and provides an interface for comparing strain rate models, calculating statistics and estimating strain rate uncertainty for a given GNSS data set.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggad191","usgsCitation":"Maurer, J., and Materna, K.Z., 2023, Quantification of geodetic strain rate uncertainties and implications for seismic hazard estimates: Geophysical Journal International, v. 234, no. 3, p. 2128-2142, https://doi.org/10.1093/gji/ggad191.","productDescription":"15 p.","startPage":"2128","endPage":"2142","ipdsId":"IP-142818","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggad191","text":"Publisher Index Page"},{"id":435346,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJW0DY","text":"USGS data release","linkHelpText":"Strain_2D: a package to compute and compare strain rate maps from geodetic velocities"},{"id":417484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"234","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Maurer, Jeremy","contributorId":305786,"corporation":false,"usgs":false,"family":"Maurer","given":"Jeremy","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":873851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}