The 1980 eruption of Mount St. Helens was instrumental in advancing understanding of how volcanoes work. Lateral edifice collapses and the generation of volcanic debris avalanches were not widely recognized prior to that eruption, making assessment of their hazards and risks challenging. The proliferation of studies since 1980 on resulting deposits and evaluation of processes leading to their generation has built on the insights from the 1980 eruption. Volcano-related destabilizing phenomena, such as strength reduction by hydrothermal alteration, deformation and structural modifications from shallow magma intrusion, and thermal pressurization of pore fluids supplement those factors also affecting nonvolcanic slopes and can lead to larger failures. Remote and ground-based monitoring techniques can aid in detecting potentially destabilizing dynamic processes and in forecasting the size and location of future large lateral collapses, although forecasting remains a topic of investigation. More than a thousand large lateral collapse events likely ≥ 0.01 km3 in volume have now been identified from deposits or inferred from source area morphology, leading to a recognition of their importance in the evolution of volcanoes and the hazards they pose. Criteria for recognition of debris-avalanche deposits include morphological factors and textural characteristics from outcrop to microscopic scale, allowing discrimination from other volcaniclastic deposits. Lateral edifice failure impacts a broad spectrum of volcanic structures in diverse tectonic settings and can occur multiple times during the evolution of individual volcanoes. Globally, collapses ≥ 0.1 km3 in volume have been documented 5–6 times per century since 1500 CE, with about one per century having a volume ≥ 1 km3. Smaller events < 0.1 km3 are underrepresented in the earlier record but also have high hazard impact.
It is important to understand species-habitat relationships to implement effective adaptive management for rare species. However, it can be challenging to assess habitat associations and their relationships to abiotic stressors in dynamic habitats without the insights that can be gained from long-term monitoring. We report results from the first six years of extensive track tube monitoring of the largest two of three remaining extant populations of federally endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a coastal Mediterranean-type ecosystem on Marine Corps Base, Camp Pendleton in southern California, USA. We used dynamic occupancy and structural equation modeling to assess potential drivers of population trends that included habitat, fire history, rainfall, disturbance, and the presence of other small mammals. We found that the variables that best predicted mouse occupancy were moderate to high forb and perennial herb cover (40–80%), and moderate to high open ground (20–70%) and low non-native grass cover (<20%), Non-native grass cover (>20%) was also a strong predictor of lower PPM colonization and increased extinction probabilities, with the extent of non-native grass cover being strongly influenced by annual rainfall and recency of fire. Our study adds to the growing literature on effects of invasive annual grasses on native species in Mediterranean-type ecosystems. We suggest that habitat management could be based upon promotion of open forb and perennial herb dominated habitats with reduction of non-native grasses by prescribed fire and other methods. These types of spatial and temporal monitoring programs can support land managers by creating a monitoring and management feedback loop. They can reveal landscape and environmental variables associated with species persistence, inform habitat management goals, and help managers to assess the success of management actions on populations of conservation concern.
We present the transverse coherence minimization method (TCM)—an approach to estimate the back-azimuth of infrasound signals that are recorded on an infrasound microphone and a colocated three-component seismometer. Accurate back-azimuth information is important for a variety of monitoring efforts, but it is currently only available for infrasound arrays and for seismoacoustic sensor pairs separated by 10 s of meters. Our TCM method allows for the analysis of colocated sensor pairs, sensors located within a few meters of each other, which may extend the capabilities of existing seismoacoustic networks and supplement operating infrasound arrays. This approach minimizes the coherence of the transverse component of seismic displacement with the infrasound wave to estimate the infrasound back-azimuth. After developing an analytical model, we investigate seismoacoustic signals from the August 2012 Humming Roadrunner experiment and the 26 May 2021 eruption of Great Sitkin Volcano, Alaska, U.S.A., at the ranges of 6.5–185 km from the source. We discuss back-azimuth estimates and potential sources of deviation (1°–15°), such as local terrain effects or deviation from common analytical models. This practical method complements existing seismoacoustic tools and may be suitable for routine application to signals of interest.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230023","usgsCitation":"Bishop, J., Haney, M.M., Fee, D., Matoza, R., McKee, K., and Lyons, J.J., 2023, Back-azimuth estimation of air-to-ground coupled infrasound from transverse coherence minimization: The Seismic Record, v. 3, no. 4, p. 249-258, https://doi.org/10.1785/0320230023.","productDescription":"10 p.","startPage":"249","endPage":"258","ipdsId":"IP-155599","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467088,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230023","text":"Publisher Index Page"},{"id":463340,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Bishop, Jordan","contributorId":345610,"corporation":false,"usgs":false,"family":"Bishop","given":"Jordan","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":917095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":917096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David","contributorId":345611,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":82656,"text":"Alaska Volcano Observatory/UAFGI","active":true,"usgs":false}],"preferred":false,"id":917097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matoza, Robin","contributorId":345612,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":917098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKee, Kathleen","contributorId":345613,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":917099,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":917100,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249323,"text":"70249323 - 2023 - Differing field methods and site conditions lead to varying bias in suspended sediment concentrations in the Lower Mississippi and Atchafalaya Rivers","interactions":[],"lastModifiedDate":"2023-10-06T13:51:06.518544","indexId":"70249323","displayToPublicDate":"2023-10-02T07:22:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Differing field methods and site conditions lead to varying bias in suspended sediment concentrations in the Lower Mississippi and Atchafalaya Rivers","docAbstract":"At sites that have been sampled for decades, changes in field and laboratory methods happen over time as instrumentation and protocols improve. Here, we compare the influence of depth- and point-integrated sampling on total, fine (< 0.0625 mm), and coarse (≥ 0.0625 mm) suspended sediment (SS) concentrations in the Lower Mississippi and Atchafalaya Rivers. Using historical field method information, we identified seven sites to test such differences. We found SS samples collected using point-integration tended to have higher concentrations than those collected using depth-integration. However, the presence and magnitude of the bias were inconsistent across sites. Bias was present at the site with less-than-ideal conditions (i.e., non-trapezoidal channel, non-uniform flow) and non-existent at the ideal site location, indicating the bias between sampling methods depends on site sampling conditions. When present, the bias is greater at higher concentrations and at moderate to high flows. At the less-than-ideal site, point-integrated samples can have 16% (total) and 34% (coarse) higher concentrations than depth-integrated samples. When flow effects are removed, this translates to a bias of 19, 9, and 8 mg per liter for total, fine, and coarse SS. When a change in field methods occurs, comparison samples and a rigorous evaluation of those samples are warranted to determine the proper course of action for a particular site. Often, the effect and solution will not be known until several years of comparison samples have been collected under a variety of hydrologic conditions.
Human activity and associated landscape modifications alter the movements of animals with consequences for populations and ecosystems worldwide. Species performing long-distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual-level movements relative to the environmental context, or movement expression, using the standardized metric Intensity of Use, reflecting both the directionality and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topography, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strongest driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human-dominated landscapes. Our work represents the first comparison of metric-based movement expression across widely distributed populations of a deer genus, contributing to the understanding and prediction of animals' responses to human activity.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16769","usgsCitation":"Mumme, S., Middleton, A.D., Ciucci, P., De Groeve, J., Corradini, A., Ossi, F., Atwood, P., Balkenhol, N., Cole, E., Debeffe, L., Dewey, S., Fischer, C., Gude, J., Heurich, M., Hurley, M.A., Jarnemo, A., Kauffman, M., Licoppe, A., van Loon, E., McWhirter, D., Mong, T., Pedrotti, L., Morellet, N., Mysterud, A., Peters, W., Proffitt, K., Saïd, S., Signer, J., Sunde, P., Stary, M., and Cagnacci, F., 2023, Wherever I may roam—Human activity alters movements of red deer (Cervus elaphus) and elk (Cervus canadensis) across two continents: Global Change Biology, v. 29, no. 20, p. 5788-5801, https://doi.org/10.1111/gcb.16769.","productDescription":"14 p.","startPage":"5788","endPage":"5801","ipdsId":"IP-148524","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.16769","text":"Publisher Index Page"},{"id":433251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Czech Republic, France, Germany, Norway, Scandinavia, United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Bavarian National Park, Cévennes National Park, Sumava National Park, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.74066959587934,\n 45.67751351997106\n ],\n [\n -111.74066959587934,\n 43.619185990194666\n ],\n [\n -109.22126425605644,\n 43.619185990194666\n ],\n [\n -109.22126425605644,\n 45.67751351997106\n ],\n [\n -111.74066959587934,\n 45.67751351997106\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"20","noUsgsAuthors":false,"publicationDate":"2023-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mumme, Steffen","contributorId":342381,"corporation":false,"usgs":false,"family":"Mumme","given":"Steffen","email":"","affiliations":[{"id":81866,"text":"University of Rome La Sapienza","active":true,"usgs":false}],"preferred":false,"id":910046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Arthur D.","contributorId":264420,"corporation":false,"usgs":false,"family":"Middleton","given":"Arthur","email":"","middleInitial":"D.","affiliations":[{"id":54468,"text":"uc","active":true,"usgs":false}],"preferred":true,"id":910047,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ciucci, Paolo","contributorId":342383,"corporation":false,"usgs":false,"family":"Ciucci","given":"Paolo","email":"","affiliations":[{"id":81866,"text":"University of Rome La Sapienza","active":true,"usgs":false}],"preferred":false,"id":910048,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"De Groeve, Johannes","contributorId":283118,"corporation":false,"usgs":false,"family":"De Groeve","given":"Johannes","email":"","affiliations":[{"id":48610,"text":"Research and Innovation Centre, Fondazione, Italy","active":true,"usgs":false}],"preferred":false,"id":910049,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corradini, Andrea","contributorId":342385,"corporation":false,"usgs":false,"family":"Corradini","given":"Andrea","email":"","affiliations":[{"id":81867,"text":"Research and Innovation Centre, Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":910050,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ossi, Federico","contributorId":342386,"corporation":false,"usgs":false,"family":"Ossi","given":"Federico","email":"","affiliations":[{"id":81867,"text":"Research and Innovation Centre, Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":910052,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Atwood, Paul","contributorId":342387,"corporation":false,"usgs":false,"family":"Atwood","given":"Paul","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":910053,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Balkenhol, Niko","contributorId":342388,"corporation":false,"usgs":false,"family":"Balkenhol","given":"Niko","email":"","affiliations":[{"id":62105,"text":"University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":910054,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cole, Eric K.","contributorId":342389,"corporation":false,"usgs":false,"family":"Cole","given":"Eric K.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":910055,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Debeffe, Lucie","contributorId":342390,"corporation":false,"usgs":false,"family":"Debeffe","given":"Lucie","email":"","affiliations":[{"id":41661,"text":"Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":910056,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dewey, Sarah R.","contributorId":342391,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":910057,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Fischer, Claude","contributorId":342392,"corporation":false,"usgs":false,"family":"Fischer","given":"Claude","email":"","affiliations":[{"id":81869,"text":"University of Applied Sciences of Western Switzerland","active":true,"usgs":false}],"preferred":false,"id":910058,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gude, Justin","contributorId":342393,"corporation":false,"usgs":false,"family":"Gude","given":"Justin","affiliations":[{"id":81870,"text":"Department of Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":910059,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Heurich, Marco","contributorId":342394,"corporation":false,"usgs":false,"family":"Heurich","given":"Marco","affiliations":[{"id":81871,"text":"Department of Visitor Management and National Park Monitoring","active":true,"usgs":false}],"preferred":false,"id":910060,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hurley, Mark A.","contributorId":342395,"corporation":false,"usgs":false,"family":"Hurley","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":36224,"text":"Idaho Department of Fish and 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Tony","contributorId":342400,"corporation":false,"usgs":false,"family":"Mong","given":"Tony","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":910067,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Pedrotti, Luca","contributorId":342401,"corporation":false,"usgs":false,"family":"Pedrotti","given":"Luca","affiliations":[{"id":81875,"text":"Stelvio National Park - Ersaf Lombardia","active":true,"usgs":false}],"preferred":false,"id":910068,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Morellet, Nicolas","contributorId":342402,"corporation":false,"usgs":false,"family":"Morellet","given":"Nicolas","affiliations":[{"id":41661,"text":"Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":910069,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Mysterud, Atle","contributorId":342403,"corporation":false,"usgs":false,"family":"Mysterud","given":"Atle","affiliations":[{"id":48608,"text":"University of Oslo","active":true,"usgs":false}],"preferred":false,"id":910070,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Peters, Wibke","contributorId":342404,"corporation":false,"usgs":false,"family":"Peters","given":"Wibke","email":"","affiliations":[{"id":81876,"text":"Bavarian State Institute of Forestry","active":true,"usgs":false}],"preferred":false,"id":910071,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Proffitt, Kelly","contributorId":342405,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","affiliations":[{"id":35211,"text":"Montana Department of Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":910072,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Saïd, Sonia","contributorId":342406,"corporation":false,"usgs":false,"family":"Saïd","given":"Sonia","affiliations":[{"id":81877,"text":"Office Français de la Biodiversité","active":true,"usgs":false}],"preferred":false,"id":910073,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Signer, Johannes","contributorId":342407,"corporation":false,"usgs":false,"family":"Signer","given":"Johannes","email":"","affiliations":[{"id":56075,"text":"University of Göttingen","active":true,"usgs":false}],"preferred":false,"id":910074,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Sunde, Peter","contributorId":342408,"corporation":false,"usgs":false,"family":"Sunde","given":"Peter","email":"","affiliations":[{"id":81878,"text":"Department of Ecoscience—Wildlife Ecology","active":true,"usgs":false}],"preferred":false,"id":910075,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Stary, Martin","contributorId":342409,"corporation":false,"usgs":false,"family":"Stary","given":"Martin","email":"","affiliations":[{"id":81879,"text":"Šumava National Park","active":true,"usgs":false}],"preferred":false,"id":910076,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Cagnacci, Francesca","contributorId":342410,"corporation":false,"usgs":false,"family":"Cagnacci","given":"Francesca","affiliations":[{"id":81867,"text":"Research and Innovation Centre, Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":910077,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70252750,"text":"70252750 - 2023 - Mangrove habitat persistence and carbon vulnerability associated with increased nutrient loading and sea-level rise at Ding Darling National Wildlife Refuge (Sanibel Island, Florida, USA)","interactions":[],"lastModifiedDate":"2024-04-04T16:55:59.02534","indexId":"70252750","displayToPublicDate":"2023-10-01T11:47:31","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Mangrove habitat persistence and carbon vulnerability associated with increased nutrient loading and sea-level rise at Ding Darling National Wildlife Refuge (Sanibel Island, Florida, USA)","docAbstract":"J.N. “Ding” Darling National Wildlife Refuge (DDNWR) is located on Sanibel Island along the southwestern coast of Florida, USA. Sanibel Island is heavily developed, but DDNWR provides protection for a large mangrove area that supports biodiversity and recreational opportunity. However, nitrogen (N) and phosphorus (P) eutrophication attributed to agriculture discharge along the Caloosahatchee River has affected the area’s aquatic habitat with algal blooms and may be causing untimely degradation of Sanibel’s mangrove forests. We launched a series of studies to understand how additional nutrient loading to the levels expected in the future might affect DDNWR’s mangrove resource. We experimentally fertilized selected mangrove forest areas with N fertilizer (+N; NH4) and P fertilizer (+P; P2O5) for three years, and monitored soil surface elevation change, soil and pneumatophore CO2 fluxes from respiration, mangrove tree sap flow from two species (Avicennia germinans, Rhizophora mangle), and individual tree and stand water use, from which we developed carbon (C) budgets for +N and +P vs. control simulations as applied to DDNWR’s 1112 ha mangrove area. Many of the measured response variables provided hints of subtle changes in response to +P rather than +N, which were compounded when scaled. From this, we found that additional P loading is expected to stimulate CO2 uptake via net ecosystem exchange of C, likely pressing the system beyond metabolic capacity and leading to a projected 41% increase in lateral C export to the estuary. Additional lateral C export is concomitant to a reduction in vertical soil surface elevation with +P. Furthermore, an inability of DDNWR’s mangroves to bury additional P and a release of P-bound ions to lateral export may exacerbate estuarine eutrophication. We also modelled the effect of sea-level rise influences on DDNWR’s mangroves through 2100 using a soil cohort model (WARMER-Mangroves) and found that the mangroves may be resilient to current rates of sea-level rise into the future but may also be susceptible to moderate accelerations. Greater eutrophication could create additional vulnerabilities to mangrove submergence, especially to basin mangroves where P concentrations are high and already reducing soil surface elevations in some mangroves. Our results suggest that amelioration of current P concentrations and avoidance of additional P loading to Sanibel Island’s mangroves are management options to consider.
","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Krauss, K., Conrad, J.R., Duberstein, J., Ward, E., Drexler, J.Z., Buffington, K., Thorne, K., Benscoter, B.W., Miller, H., Faron, N.T., Merino, S., From, A., Peneva-Reed, E., and Zhu, Z., 2023, Mangrove habitat persistence and carbon vulnerability associated with increased nutrient loading and sea-level rise at Ding Darling National Wildlife Refuge (Sanibel Island, Florida, USA), 46 p.","productDescription":"46 p.","ipdsId":"IP-156801","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":427403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427371,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/mangrove-ecosystem-services/"}],"country":"United States","state":"Florida","otherGeospatial":"Ding Darling National Wildlife Refuge, Sanibel Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.09257743948284,\n 26.43591715803987\n ],\n [\n -82.04970400948879,\n 26.44673081335216\n ],\n [\n -82.05393096737532,\n 26.472138907162602\n ],\n [\n -82.08170811920239,\n 26.469976734538434\n ],\n [\n -82.09016203497596,\n 26.461327637778552\n ],\n [\n -82.1227699958168,\n 26.475922611484222\n ],\n [\n -82.15718951003726,\n 26.494839266177138\n ],\n [\n -82.17107808595081,\n 26.49916263597524\n ],\n [\n -82.17590889496402,\n 26.515914157596868\n ],\n [\n -82.18315510848416,\n 26.521857658684468\n ],\n [\n -82.18557051299105,\n 26.487272977733056\n ],\n [\n -82.14330093412373,\n 26.455381006749434\n ],\n [\n -82.10163520638315,\n 26.43916136120143\n ],\n [\n -82.09257743948284,\n 26.43591715803987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrad, Jeremy R.","contributorId":149347,"corporation":false,"usgs":false,"family":"Conrad","given":"Jeremy","email":"","middleInitial":"R.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":898086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duberstein, Jamie A.","contributorId":91007,"corporation":false,"usgs":false,"family":"Duberstein","given":"Jamie A.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":898087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":217389,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898088,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":898089,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":898090,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":898091,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Benscoter, Brian W.","contributorId":335331,"corporation":false,"usgs":false,"family":"Benscoter","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":40277,"text":"U.S. Department of Energy","active":true,"usgs":false}],"preferred":false,"id":898092,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, Haley 0000-0002-1745-9526","orcid":"https://orcid.org/0000-0002-1745-9526","contributorId":304983,"corporation":false,"usgs":false,"family":"Miller","given":"Haley","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":898093,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Faron, Natalie T.","contributorId":312468,"corporation":false,"usgs":false,"family":"Faron","given":"Natalie","email":"","middleInitial":"T.","affiliations":[{"id":15312,"text":"Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":898094,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Merino, Sergio 0000-0002-2834-2243 merinos@usgs.gov","orcid":"https://orcid.org/0000-0002-2834-2243","contributorId":3653,"corporation":false,"usgs":true,"family":"Merino","given":"Sergio","email":"merinos@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898095,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"From, Andrew 0000-0002-6543-2627","orcid":"https://orcid.org/0000-0002-6543-2627","contributorId":223021,"corporation":false,"usgs":true,"family":"From","given":"Andrew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898096,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peneva-Reed, Elitsa I. 0000-0002-4570-4701","orcid":"https://orcid.org/0000-0002-4570-4701","contributorId":294531,"corporation":false,"usgs":false,"family":"Peneva-Reed","given":"Elitsa I.","affiliations":[],"preferred":false,"id":898097,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":898098,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70249606,"text":"70249606 - 2023 - Lake Ontario April prey fish survey results and Alewife assessment, 2023","interactions":[],"lastModifiedDate":"2025-08-26T14:29:06.466685","indexId":"70249606","displayToPublicDate":"2023-10-01T10:08:53","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Lake Ontario April prey fish survey results and Alewife assessment, 2023","docAbstract":"The April bottom trawl survey and Alewife Alosa pseudoharengus population assessment provides science to inform Lake Ontario fisheries management. The 2023 survey included 215 trawls in the main lake and embayments, and sampled depths from 6.5 to 252 m (21-833 ft). The survey captured 1,012,178 fish from 32 species with a total weight of 12,136 kg (26,700 lbs.). Alewife were 92% of the catch by number while Rainbow Smelt, Osmerus mordax, Deepwater Sculpin, Myoxocephalus thompsonii, and Round Goby, Neogobius melanostomus, comprised 3%, 3%, and 1% of the catch, respectively. To improve the accuracy of prey fish biomass and density estimates we reanalyzed trawl sensor data from each of three participating survey vessels and created vessel-specific relationships predicting how bottom trawl bottom contact time, wing width, and area-swept varies with depth.
Total Alewife biomass increased in 2023 due to growth and survival of the abundant 2020 year class (now age-3) and an abundant 2022 year class (age-1). The 2023 mean Alewife biomass (81.1 kg·ha-1) was the largest since whole lake sampling began in 2016 and was the ninth largest value observed in the modern time series (1997-2023, maximum value in 2000 = 91.8 kg·ha-1). The 2023 Alewife density (6795 n·ha-1) was the greatest density observed in the modern time series. These high biomass and density values are due to above average Alewife reproductive success in 2020 and 2022. Simulation modeling suggests the 2024 and 2025 Alewife biomass index may be substantially higher than the 2023 observations.
In 2023, the Rainbow Smelt biomass index increased relative to the 2022 index, as did the biomass index for Cisco, Coregonus artedi. In contrast, Emerald Shiner Notropis atherinoides and Threespine Stickleback Gasterosteus aculeatus, biomass values continue to be low (< 0.01 kg·ha-1). Three Bloater Coregonus hoyi, were captured during the 2023 survey. Hydroacoustic sampling conducted during the bottom trawl survey estimated prey fish densities in pelagic habitats not sampled by the bottom trawl (3 m below the surface to 3 m above the lake bottom) and these densities were hundreds to thousands of times lower than bottom trawl-based densities. These results support the idea that, in April, when the warmest water is on the lake bottom, Alewife and most other pelagic prey fish are near the lake bottom and can be effectively sampled with bottom trawling.
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Jeremy","contributorId":168905,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":886438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mitchinson, Olivia Margaret 0009-0002-7999-1160","orcid":"https://orcid.org/0009-0002-7999-1160","contributorId":339869,"corporation":false,"usgs":true,"family":"Mitchinson","given":"Olivia","email":"","middleInitial":"Margaret","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886439,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minihkeim, Scott P. 0000-0003-4958-2462","orcid":"https://orcid.org/0000-0003-4958-2462","contributorId":265808,"corporation":false,"usgs":true,"family":"Minihkeim","given":"Scott","email":"","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886440,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266470,"text":"70266470 - 2023 - Spatially and temporally variable production pathways support the Lake Erie central basin food web","interactions":[],"lastModifiedDate":"2025-05-07T18:47:53.830989","indexId":"70266470","displayToPublicDate":"2023-09-30T13:43:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Spatially and temporally variable production pathways support the Lake Erie central basin food web","docAbstract":"In large freshwater systems, the dominant production pathways supporting food webs are often spatiotemporally variable. We used stable isotope analysis and analysis of covariance (ANCOVA) models to investigate spatial and interannual variation in the dominant production pathways supporting fish consumers within the central basin of Lake Erie. We examined C and N stable isotope ratios of zooplankton, benthic invertebrates, and four species of fish common to nearshore areas of the central basin (yellow perch, Perca flavescens; white perch, Morone americana; rainbow smelt, Osmerus mordax; and round goby, Neogobius melanostomus) using tissue samples collected in 2017 and 2019. δ 13C values varied by location consistent with expected baseline differences in nutrient loading (13C was more enriched in the southern region) in two of six ANCOVA models. Furthermore, δ 15N values varied with individual fish size and by location in a manner consistent with spatial patterns of nutrient loading from surrounding agricultural landscapes (15N was more enriched in the northern region) and a longitudinal gradient of eutrophication, decreasing from west to east. These patterns were not exhibited by all species and did not necessarily persist across years, suggesting that additional factors (e.g., regional diet differences, river plume dynamics) also contributed to observed δ 13C and δ 15N variation. We suggest that spatiotemporal variation of stable isotope ratios should be accounted for in studies of trophic basis of production and food web structure in Lake Erie.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.07.006","usgsCitation":"Tellier, J., Höök, T., Kraus, R., and Collingsworth, P., 2023, Spatially and temporally variable production pathways support the Lake Erie central basin food web: Journal of Great Lakes Research, v. 49, no. 5, p. 1137-1149, https://doi.org/10.1016/j.jglr.2023.07.006.","productDescription":"13 p.","startPage":"1137","endPage":"1149","ipdsId":"IP-144710","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":488148,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.jglr.2023.07.006","text":"Publisher Index Page"},{"id":485518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"central Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.89113891431519,\n 42.642087429833964\n ],\n [\n -81.54918796649198,\n 42.5518853383298\n ],\n [\n -82.00732338256428,\n 42.28154650023495\n ],\n [\n -82.41825696788968,\n 42.08990823363946\n ],\n [\n -82.65704269990312,\n 41.35456309813142\n ],\n [\n -81.71855924152474,\n 41.51441245425303\n ],\n [\n -80.95777679301685,\n 41.82391066009919\n ],\n [\n -80.89113891431519,\n 42.642087429833964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Tellier, Joshua M.","contributorId":354641,"corporation":false,"usgs":false,"family":"Tellier","given":"Joshua M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":936058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Höök, Tomas O.","contributorId":354642,"corporation":false,"usgs":false,"family":"Höök","given":"Tomas O.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":936059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936060,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collingsworth, Paris D.","contributorId":354643,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris D.","affiliations":[{"id":84645,"text":"Illinois-Indiana SeaGrant","active":true,"usgs":false}],"preferred":false,"id":936061,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249643,"text":"70249643 - 2023 - Cruise Report for NOAA Ship Nancy Foster Cruise NF-22-06","interactions":[],"lastModifiedDate":"2023-10-23T13:39:48.181636","indexId":"70249643","displayToPublicDate":"2023-09-30T08:29:02","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17063,"text":"DWH MDBC Cruise Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CR-23-03","title":"Cruise Report for NOAA Ship Nancy Foster Cruise NF-22-06","docAbstract":"Between 9 August and 1 September, 2022, the Mesophotic and Deep Benthic (MDBC) Habitat Assessment and Evaluation (HAE) and Mapping, Ground-truthing, and Predictive Habitat Modeling (MGM) projects implemented remotely operated vehicle (ROV) dives, multibeam surveys, and conductivity, temperature, depth (CTD) operations at deep-sea sites in the northern Gulf of Mexico. The primary sites selected are a region of known deep-sea coral habitats, including Deepwater Horizon (DWH) injured and reference sites at depths of 1,100–2,000 m.
The cruise includes objectives from MGM and HAE projects. Habitat characterization and analysis of biological samples collected with ROV Odysseus maintain long-term data flows and fill critical data gaps on the biology and ecology at impacted and reference sites, assess potential ongoing impacts from threats, refine predictive habitat models, help target locations for direct restoration and protection, and determine a baseline for health and condition. Multibeam echosounder data can help document the broadscale abundance and distribution of MDBC, characterize benthic habitats, and provide information that can help guide future ROV surveys.
","language":"English","publisher":"National Oceanic and Atmospheric Administration","doi":"10.25923/nwxc-ab95","usgsCitation":"Clark, R., and Demopoulos, A., 2023, Cruise Report for NOAA Ship Nancy Foster Cruise NF-22-06: DWH MDBC Cruise Report CR-23-03, 33 p., https://doi.org/10.25923/nwxc-ab95.","productDescription":"33 p.","ipdsId":"IP-153687","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":422038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.5,\n 30\n ],\n [\n -91.5,\n 30\n ],\n [\n -91.5,\n 27\n ],\n [\n -87.5,\n 27\n ],\n [\n -87.5,\n 30\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Randy","contributorId":218497,"corporation":false,"usgs":false,"family":"Clark","given":"Randy","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":886574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":222183,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":886575,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70260095,"text":"70260095 - 2023 - Petrology and geochronology of Cretaceous–Eocene plutonic rocks in northeastern Washington, USA: Crustal thickening, slab rollback, and origin of the Challis episode","interactions":[],"lastModifiedDate":"2024-10-28T12:02:39.587199","indexId":"70260095","displayToPublicDate":"2023-09-30T07:00:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Petrology and geochronology of Cretaceous–Eocene plutonic rocks in northeastern Washington, USA: Crustal thickening, slab rollback, and origin of the Challis episode","docAbstract":"Cretaceous through Eocene plutonic rocks in northeastern Washington, USA, document a 60 m.y. history of crustal thickening and subsequent collapse and extension in response to two terrane-accretion events. Rocks emplaced 113–53 Ma have increasing La/Yb ratios reflecting orogenic plateau development after arrival of the Insular terrane by 100 Ma. Plutons emplaced 52–45 Ma (the Challis episode) document collapse of this plateau and define a SW-younging age progression attributed to breakoff and rollback of the Farallon slab following accretion of the Siletzia terrane at ca. 50 Ma. All of the rocks have chemical traits of arc magmas, likely inherited from their lower-crustal sources, but low B/Be ratios and the lack of evidence for amphibole fractionation indicate the Eocene magmas formed under drier conditions than are typical of active subduction settings. These magmas also originated at greater depth (eclogitic vs. gabbroic source) and were emplaced more shallowly than the earlier ones. All rocks have overlapping Sr-Nd and O isotopic data, indicating significant contributions from older continental crust, and depleted mantle Nd model ages become older toward the east, defining three regions that correspond with previously inferred lower-crustal domains. Farallon slab rollback also drove extension (core complex formation, dike swarms) and crustal uplift, which, along with voluminous magmatism, define the Challis episode. This tectonic model is further supported by seismic tomography, which has identified remnants of a detached slab in the upper mantle beneath the region.
Current methods for determining the 1-percent annual exceedance probability (AEP) for a streamflow assume stationarity (the assumption that the statistical distribution of data from past observations does not contain trends and will continue unchanged in the future). This assumption allows the 1-percent AEP to be determined based on historical streamflow records. However, the assumption of stationarity is challenged by observed trends in streamflow records.
In response, the U.S. Geological Survey, in cooperation with the Federal Emergency Management Agency, studied potential changes to the 1-percent AEP streamflows at streamgages in the Housatonic River watershed in Massachusetts, Connecticut, and New York. The study used the Precipitation-Runoff Modeling System—a deterministic hydrologic model. Climate inputs to the model of temperature and precipitation were scaled to anticipated changes based on global climate models that could occur in 2030, 2050, and 2100. The model outputs were used to characterize the 1-percent AEP streamflows for 2030, 2050, and 2100 and compare the results to baseline conditions for 1950 to 2015. Results indicated that the 1-percent AEP streamflow for unregulated streams and rivers may increase from the 1950–2015 baseline period by 7.4, 11.7, and 17.3 percent in 2030, 2050, and 2100, respectively, because of climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Olson, S.A., 2023, Characterizing changes in the 1-percent annual exceedance probability streamflows for climate-change scenarios in the Housatonic River watershed of Massachusetts, Connecticut, and New York: U.S. Geological Survey Scientific Investigations Report 2023–5090, 16 p., https://doi.org/10.3133/sir20235090.","productDescription":"Report: iv, 16 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149676","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501055,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115441.htm","linkFileType":{"id":5,"text":"html"}},{"id":421394,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91CSH0P","text":"USGS data release","linkHelpText":"Data for characterizing changes in the 1-percent annual exceedance probability streamflows for climate change scenarios in the Housatonic River watershed—Massachusetts, Connecticut, and New York"},{"id":421390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5090/coverthb.jpg"},{"id":421391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5090/sir20235090.pdf","text":"Report","size":"8.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5090"},{"id":421392,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5090/sir20235090.XML"},{"id":421393,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5090/images"},{"id":421395,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235090/full"}],"country":"United States","state":"Connecticut, Massachusetts, New York","otherGeospatial":"Housatonic River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.73443369971218,\n 41.34286607604693\n ],\n [\n -72.73443369971218,\n 42.92331687334064\n ],\n [\n -74.64605479346228,\n 42.92331687334064\n ],\n [\n -74.64605479346228,\n 41.34286607604693\n ],\n [\n -72.73443369971218,\n 41.34286607604693\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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Northborough, MA 01532
Ecological restoration is critical for recovering degraded ecosystems but is challenged by variable success and low predictability. Understanding which outcomes are more predictable and less variable following restoration can improve restoration effectiveness. Recent theory asserts that the predictability of outcomes would follow an order from most to least predictable from coarse to fine community properties (physical structure > taxonomic diversity > functional composition > taxonomic composition) and that predictability would increase with more severe environmental conditions constraining species establishment. We tested this “hierarchy of predictability” hypothesis by synthesizing outcomes along an aridity gradient with 11 grassland restoration projects across the United States. We used 1829 vegetation monitoring plots from 227 restoration treatments, spread across 52 sites. We fit generalized linear mixed-effects models to predict six indicators of restoration outcomes as a function of restoration characteristics (i.e., seed mixes, disturbance, management actions, time since restoration) and used variance explained by models and model residuals as proxies for restoration predictability. We did not find consistent support for our hypotheses. Physical structure was among the most predictable outcomes when the response variable was relative abundance of grasses, but unpredictable for total canopy cover. Similarly, one dimension of taxonomic composition related to species identities was unpredictable, but another dimension of taxonomic composition indicating whether exotic or native species dominated the community was highly predictable. Taxonomic diversity (i.e., species richness) and functional composition (i.e., mean trait values) were intermittently predictable. Predictability also did not increase consistently with aridity. The dimension of taxonomic composition related to the identity of species in restored communities was more predictable (i.e., smaller residuals) in more arid sites, but functional composition was less predictable (i.e., larger residuals), and other outcomes showed no significant trend. Restoration outcomes were most predictable when they related to variation in dominant species, while those responding to rare species were harder to predict, indicating a potential role of scale in restoration predictability. Overall, our results highlight additional factors that might influence restoration predictability and add support to the importance of continuous monitoring and active management beyond one-time seed addition for successful grassland restoration in the United States.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2922","usgsCitation":"Bertuol-Garcia, D., Ladouceur, E., Brudvig, L.A., Laughlin, D.C., Munson, S.M., Curran, M.F., Davies, K.W., Svejcar, L.N., and Shackelford, N., 2023, Testing the hierarchy of predictability in grassland restoration across a gradient of environmental severity: Ecological Applications, v. 33, e2922, 21 p., https://doi.org/10.1002/eap.2922.","productDescription":"e2922, 21 p.","ipdsId":"IP-153040","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":441997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2922","text":"Publisher Index Page"},{"id":427818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Continental United States","geographicExtents":"{\n \"type\": 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Canada","active":true,"usgs":false}],"preferred":false,"id":898925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ladouceur, Emma","contributorId":270938,"corporation":false,"usgs":false,"family":"Ladouceur","given":"Emma","email":"","affiliations":[{"id":56222,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Biodiversity Synthesis & Physiological Diversity","active":true,"usgs":false}],"preferred":false,"id":898926,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brudvig, Lars A.","contributorId":335644,"corporation":false,"usgs":false,"family":"Brudvig","given":"Lars","email":"","middleInitial":"A.","affiliations":[{"id":80453,"text":"Department of Plant Biology and Program in Ecology, Evolution, and Behavior, Michigan State University, Lansing, MI, USA","active":true,"usgs":false}],"preferred":false,"id":898927,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laughlin, Daniel C.","contributorId":200543,"corporation":false,"usgs":false,"family":"Laughlin","given":"Daniel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":898928,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":898929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Curran, Michael F.","contributorId":261573,"corporation":false,"usgs":false,"family":"Curran","given":"Michael","email":"","middleInitial":"F.","affiliations":[{"id":52887,"text":"Program in Ecology, University of Wyoming, 1000 E. University Avenue, Laramie, WY, USA 82071","active":true,"usgs":false}],"preferred":false,"id":898930,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davies, Kirk W.","contributorId":255108,"corporation":false,"usgs":false,"family":"Davies","given":"Kirk","email":"","middleInitial":"W.","affiliations":[{"id":51433,"text":"Eastern Oregon Agricultural Research Center, USDA Agricultural Research Service, Burns, OR 97720 USA","active":true,"usgs":false}],"preferred":false,"id":898931,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Svejcar, Lauren N.","contributorId":127492,"corporation":false,"usgs":false,"family":"Svejcar","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":898932,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shackelford, Nancy","contributorId":261567,"corporation":false,"usgs":false,"family":"Shackelford","given":"Nancy","email":"","affiliations":[{"id":52880,"text":"Ecology and Evolutionary Biology, University of Colorado Boulder, 1900 Pleasant St, Boulder, Colorado 80309, USA","active":true,"usgs":false}],"preferred":false,"id":898933,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249184,"text":"sir20235107 - 2023 - Assessing the effects of chloride deicer applications on groundwater near the Siskiyou Pass, southwestern Oregon, July 2018–February 2021","interactions":[],"lastModifiedDate":"2025-07-28T13:13:06.240353","indexId":"sir20235107","displayToPublicDate":"2023-09-29T09:18:15","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":"2023-5107","displayTitle":"Assessing the Effects of Chloride Deicer Applications on Groundwater near the Siskiyou Pass, Southwestern Oregon, July 2018–February 2021","title":"Assessing the effects of chloride deicer applications on groundwater near the Siskiyou Pass, southwestern Oregon, July 2018–February 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Oregon Department of Transportation (ODOT), evaluated the effects of cold-weather chloride deicers (road deicing chemicals) on groundwater quality, with a focus on chloride, near the Siskiyou Pass in southwestern Oregon. The study covered the period during July 2018 through February 2021. Between the years 2016 and 2020 ODOT applied up to 16,000 gallons per mile of chloride deicer and 143,000 pounds per mile of road salt along an 11-mile stretch of Interstate 5 (I-5) through the Siskiyou Pass. Despite the benefit of safer driving conditions, there are potentially negative environmental effects associated with the use of chloride-based deicers (such as magnesium chloride and sodium chloride). The results from this study are intended to help ODOT assess the water-quality effects from the application of chloride deicers at the Siskiyou Pass and inform decisions on how those chemicals are used.
Dissolved chloride concentrations tended to be greater in groundwater downgradient from I-5 compared to groundwater upgradient from the interstate. Specific conductance was a good predictor of dissolved chloride concentration (R2 = 0.905). Continuous monitoring showed that specific conductance measurements were greater at four downgradient spring-fed sites at the end of the study period compared with measurements at the beginning of the study. The study results indicate that chloride levels in shallow groundwater downgradient from I-5 are increasing, but dissolved chloride concentrations in domestic wells are not above the U.S. Environmental Protection Agency drinking water recommendations. The approach and methods used in this study, with modifications as site conditions warrant, can be applied in other areas of chloride deicer application to determine if groundwater is affected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235107","collaboration":"Prepared in cooperation with Oregon Department of Transportation","usgsCitation":"Gingerich, S.B., Wise, D.R., and Stonewall, A.J., 2023, Assessing the effects of chloride deicer applications on groundwater near the Siskiyou Pass, southwestern Oregon, July 2018–February 2021 (ver. 1.1, July 2025): U.S. Geological Survey Scientific Investigations Report 2023–5107, 39 p., https://doi.org/10.3133/sir20235107.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140151","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":492943,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5107/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version history"},{"id":421408,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D6XDIJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Specific conductance and other groundwater quality data, Siskiyou Pass area, southwestern Oregon, 2018 to 2021"},{"id":421403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5107/coverthb2.jpg"},{"id":421404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107.pdf","text":"Report","size":"7.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5107"},{"id":421405,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235107/full","text":"Report","description":"SIR 2023-5107"},{"id":421406,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5107/images"},{"id":421407,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107.XML"},{"id":421409,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107_appendix1.xlsx","text":"Appendix 1","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2023-5107 Appendix 1"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.79414603940278,\n 42.22368397834566\n ],\n [\n -122.79414603940278,\n 41.9878942723297\n ],\n [\n -122.42473672058753,\n 41.9878942723297\n ],\n [\n -122.42473672058753,\n 42.22368397834566\n ],\n [\n -122.79414603940278,\n 42.22368397834566\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: September 29, 2023; Version 1.1: July 25, 2025","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Introduction: Large dam removals provide a restoration opportunity for shrinking coastal wetland habitats. Dam removal can increase sediment delivery to sediment-starved river deltas and estuaries by restoring natural sediment transport and mobilizing reservoir-impounded sediment. However, rapid mobilization of massive quantities of sediment stored behind large dams also constitutes a major ecological perturbation. Information is lacking on coastal habitat responses to sediment pulses of this magnitude.
Methods: Removal of two large dams along the Elwha River (Washington, USA) in 2011–2014 released ~20.5 Mt of impounded sediment, ~5.4 Mt of which were deposited in the delta and estuary (hereafter, delta). We used time series of aerial imagery, digital elevation models, and vegetation field sampling to examine plant community responses to this sediment pulse across seven years during and after dam removal.
Results: Between 2011 and 2018, the Elwha River delta increased by ~26.8 ha. Vegetation colonized ~16.4 ha of new surfaces, with mixed pioneer vegetation on supratidal beach, river bars, and river mouth bars and emergent marsh vegetation in intertidal aquatic habitats. Colonization occurred on surfaces that were higher and more stable in elevation and farther from the shoreline. Compared to established delta plant communities, vegetation on new surfaces had lower cover of dominant species and functional groups, with very low woody cover, and lower graminoid cover than dunegrass and emergent marsh communities. Over time following surface stabilization, however, vegetation on new surfaces increased in species richness, cover, and similarity to established communities. By 2018, ~1.0 ha of vegetation on new surfaces had developed into dunegrass or willow–alder communities and ~5.9 ha had developed into emergent marsh. At the same time, dam removal had few discernible effects on established delta plant communities.
Discussion: Together, these results suggest that rapid sediment mobilization during large dam removal has potential to expand coastal wetland habitat without negatively affecting established plant communities. However, as sediment loads declined in 2016–2018, new delta surfaces decreased by ~4.5 ha, and ~1.6 ha of new vegetation reverted to no vegetation. Long-term persistence of the expanded coastal habitat will depend on ongoing erosional and depositional processes under the restored natural sediment regime.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1233903","usgsCitation":"Perry, L.G., Shafroth, P., Alfieri, S.J., and Miller, I.M., 2023, Coastal vegetation responses to large dam removal on the Elwha River: Frontiers in Ecology and Evolution, v. 11, 1233903, 21 p., https://doi.org/10.3389/fevo.2023.1233903.","productDescription":"1233903, 21 p.","ipdsId":"IP-153758","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":442000,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1233903","text":"Publisher Index Page"},{"id":435165,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O6NML1","text":"USGS data release","linkHelpText":"Vegetation and geomorphic surfaces in the Elwha River delta, Washington, after dam removal, derived from 2016 and 2018 aerial imagery and 2007, 2014, and 2018 field surveys"},{"id":422399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.8042115532547,\n 48.18296274689851\n ],\n [\n -123.8042115532547,\n 47.722552996994835\n ],\n [\n -123.34685932477454,\n 47.722552996994835\n ],\n [\n -123.34685932477454,\n 48.18296274689851\n ],\n [\n -123.8042115532547,\n 48.18296274689851\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G.","contributorId":220048,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":887673,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":887674,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alfieri, Samuel J.","contributorId":329742,"corporation":false,"usgs":false,"family":"Alfieri","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":78705,"text":"self","active":true,"usgs":false}],"preferred":false,"id":887675,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ian M. 0000-0002-3289-6337","orcid":"https://orcid.org/0000-0002-3289-6337","contributorId":41951,"corporation":false,"usgs":false,"family":"Miller","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":887676,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249968,"text":"70249968 - 2023 - Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin","interactions":[],"lastModifiedDate":"2024-07-17T21:34:54.964345","indexId":"70249968","displayToPublicDate":"2023-09-29T07:00:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin","docAbstract":"Biocrusts are sensitive to changes in livestock grazing intensity in arid rangelands and may be useful indicators of ecosystem functions, particularly soil properties like soil stability, which may suggest the potential for soil erosion. We compared biocrust community composition and surface soil stability in a big sagebrush (Artemisia tridentata) steppe rangeland in the northwestern Great Basin in several paired sites, with or without long-term cattle grazing exclusion, and similar soils (mostly sandy loams), climate, and vegetation composition. We found that livestock grazing was associated with both lower surface soil stability and cover of several biocrust morphogroups, especially lichens, compared with sites with long-term livestock exclusion. Surface soil stability did not modify the effects of grazing on most biocrust components via interactive effects. Livestock grazing effects on total biocrust cover were partially mediated by changes in surface soil stability. Though lichens were more sensitive to grazing disturbance, our results suggest that moss (mostly Tortula ruralis in this site) might be a more readily observable indicator of grazing-related soil stability change in this area due to their relatively higher abundance compared with lichens (moss: mean, 8.5% cover, maximum, 96.1%, lichens: mean, 1.0% cover, maximum, 14.1%). These results highlight the potential for biocrust components as sensitive indicators of change in soil-related ecosystem functions in sagebrush steppe rangelands. However, further research is needed to identify relevant indicator groups across the wide range of biocrust community composition associated with site environmental characteristics, variable grazing systems, other rangeland health metrics, and other disturbance types such as wildfire.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2023.09.001","usgsCitation":"Copeland, S., Condon, L.A., Rosentreter, R., Miller, J., and Kahn-Abrams, M., 2023, Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin: Rangeland Ecology & Management, v. 91, p. 82-86, https://doi.org/10.1016/j.rama.2023.09.001.","productDescription":"5 p.","startPage":"82","endPage":"86","ipdsId":"IP-147342","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":442004,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2023.09.001","text":"Publisher Index Page"},{"id":422476,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Great Basin","volume":"91","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Copeland, Stella M.","contributorId":196218,"corporation":false,"usgs":false,"family":"Copeland","given":"Stella M.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":887850,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Condon, Lea A. 0000-0002-9357-3881","orcid":"https://orcid.org/0000-0002-9357-3881","contributorId":202908,"corporation":false,"usgs":true,"family":"Condon","given":"Lea","email":"","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":887851,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosentreter, Roger","contributorId":257441,"corporation":false,"usgs":false,"family":"Rosentreter","given":"Roger","affiliations":[{"id":52018,"text":"Biology Department, Boise State University, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":887852,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Jesse","contributorId":147734,"corporation":false,"usgs":false,"family":"Miller","given":"Jesse","email":"","affiliations":[{"id":16916,"text":"Dept. of Zoology, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":887853,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kahn-Abrams, Maya","contributorId":331492,"corporation":false,"usgs":false,"family":"Kahn-Abrams","given":"Maya","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":887854,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248979,"text":"sir20235022 - 2023 - Identifying the relative importance of water-budget information needed to quantify how land-cover change affects recharge, Hawaiian Islands","interactions":[],"lastModifiedDate":"2026-03-06T20:41:42.483451","indexId":"sir20235022","displayToPublicDate":"2023-09-28T12:49:58","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":"2023-5022","displayTitle":"Identifying the Relative Importance of Water-Budget Information Needed to Quantify How Land-Cover Change Affects Recharge, Hawaiian Islands","title":"Identifying the relative importance of water-budget information needed to quantify how land-cover change affects recharge, Hawaiian Islands","docAbstract":"This report describes a sensitivity analysis of a water-budget model that was completed to identify the most important types of hydrologic information needed to reduce the uncertainty of model recharge estimates. The sensitivity of model recharge estimates for the Hawaiian Islands of Oʻahu and Maui was analyzed for seven model parameters potentially affected by land-cover changes within a watershed. The seven model parameters tested were canopy capacity, canopy-cover fraction, crop coefficient, fog-catch efficiency, root depth, stemflow, and trunk-storage capacity.
Results of the sensitivity analysis were used to (1) quantify the relative importance of the seven model parameters to recharge assessments for three moisture zones (dry, mesic, and wet) on Oʻahu and Maui and (2) prepare a list of critical information needs for each moisture zone. The list of critical information needs was developed for three general types of land cover (forest, shrubland, and grassland) that are assumed to be affected by watershed management in the Hawaiian Islands. Identified information needs included estimates or measurements of (1) evapotranspiration processes needed to determine crop coefficients for land-cover types in all moisture zones, (2) rooting depths for land-cover types in the dry and mesic moisture zones, (3) canopy-cover fraction for forests in the wet and mesic moisture zones, (4) ratios of fog interception to rainfall for forests and shrublands in the wet moisture zone, and (5) canopy capacity for forests in the wet and mesic moisture zones. The list of information needs can guide data-collection strategies of future projects. Collection and analysis of the identified hydrologic information may help model users develop a better parameterization scheme, reduce uncertainty of values that model users assign to land-cover dependent parameters, and therefore allow future applications of the water-budget model to more accurately quantify how recharge in the Hawaiian Islands might be affected by future land-cover changes within a watershed.
","language":"English","publisher":"U.S. Geological Center","publisherLocation":"Reston, VA","doi":"10.3133/sir20235022","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Johnson, A.G., Mair, A., and Oki, D.S., 2023, Identifying the relative importance of water-budget information needed to quantify how land-cover change affects recharge, Hawaiian Islands: U.S. Geological Survey Scientific Investigations Report 2023–5022, 28 p., https://doi.org/10.3133/sir20235022.","productDescription":"Report: vi, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-129378","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":500874,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115438.htm","text":"Maui","linkFileType":{"id":5,"text":"html"}},{"id":500873,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115437.htm","text":"Oahu","linkFileType":{"id":5,"text":"html"}},{"id":421316,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X9ZEE3","text":"USGS Data Release","description":"Johnson, A.G., and Kāne, H.L., 2023, Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the islands of Oahu and Maui, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P9X9ZEE3.","linkHelpText":"Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the islands of Oahu and Maui, Hawaii"},{"id":421315,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5022/sir20235022.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":421314,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5022/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.75858532451014,\n 21.145379373074235\n ],\n [\n -156.75858532451014,\n 20.508739201099033\n ],\n [\n -155.89066540263516,\n 20.508739201099033\n ],\n [\n -155.89066540263516,\n 21.145379373074235\n ],\n [\n -156.75858532451014,\n 21.145379373074235\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.3516029026351,\n 21.83029675022702\n ],\n [\n -158.3516029026351,\n 21.17099365241016\n ],\n [\n -157.59354626201016,\n 21.17099365241016\n ],\n [\n -157.59354626201016,\n 21.83029675022702\n ],\n [\n -158.3516029026351,\n 21.83029675022702\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. Current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost portion of the western Snake River Plain aquifer system, called the Treasure Valley.
The development of the model was guided by several objectives, including:
The model was constructed with a spatial scale and level of detail that aimed to meet these objectives while balancing the sometimes-competing goals of fast runtimes, numerical stability, usability, and parsimony.
The Treasure Valley Groundwater Flow Model (TVGWFM) is a three-dimensional finite-difference numerical model constructed using MODFLOW 6 (Langevin and others, 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55). The model covers the westernmost portion of the western Snake River Plain and is discretized into a regular grid of 64 by 65 cells with a side length of 1 mile and 6 layers of varying depth and active area. A historical model period was developed consisting of 360 month-long stress periods for 1986–2015. The model builds upon previous modeling efforts by adding a transient period, incorporating new head and discharge observations to constrain parameters, incorporating information from the hydrogeologic framework model (HFM) of Bartolino (2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon: U.S. Geological Survey Scientific Investigations Report 2019–5138, https://doi.org/10.3133/sir20195138) and incorporating refined estimates of evapotranspiration and irrigation classification of lands in the study area.
The TVGWFM includes all significant components of recharge to and discharge from the aquifer. Inflows include canal seepage, irrigation and precipitation recharge, mountain-front recharge, rivers and stream seepage, and seepage from Lake Lowell. Outflows include discharge to agricultural drainage ditches, discharge to rivers and streams, pumping, and discharge to Lake Lowell. Each recharge or discharge component is represented separately using individual MODFLOW 6 packages.
Parameter values were derived with a combination of trial-and-error steps and automated parameter estimation using PEST software (Doherty, J.E., 2005, PEST, model-independent parameter estimation–User manual: Watermark Numerical Computing, https://pesthomepage.org/documentation). Parameter estimates were constrained with several types of observation data, including water levels, water level changes, vertical water level differences, drain discharges, change in drain discharges, river seepage, seepage from Lake Lowell, and change in seepage from Lake Lowell. Material properties from the hydrogeologic framework were also used to assign the minimum and maximum values of some parameters.
A final parameter realization was reached that minimized residuals between the observed and modelled values for the various observation groups. Mean residuals for the observation groups were 15.4 feet (ft) for water levels, 0.2 ft for water level changes, 19.4 ft for vertical water level differences, −3.9 cubic feet per second (ft3/s) for drain discharges, 0.0 ft3/s for changes in drain discharge, 45.0 ft3/s for river seepage, −40.1 ft3/s for Lake Lowell seepage, and 126.3 ft3/s for changes in Lake Lowell seepage. The quality of the model’s fit to observations varied spatially, with notable areas of under- or over-simulation of water levels present to the northwest and southwest of Lake Lowell, in the foothills along the eastern model boundary, and near the City of Eagle. Trends were observed in the residuals of many of the observation groups, indicating that the model is missing or not fully reproducing some phenomena that are observed in the system.
The TVGWFM can be used as a tool for water resource planning, for understanding the interactions of groundwater and surface water at a basin scale, and for facilitating conjunctive management, but may lack the precision needed for water rights administration at a local scale. Additional sources of uncertainty or limitations of the model are noted. The quantity and spatial distribution of canal seepage and infiltration of irrigation water recharge, the largest sources of recharge to the system, are unknown and approximated indirectly. There is poor understanding of how canal seepage and incidental recharge change as land is converted from agricultural (irrigated) to suburban (semi-irrigated). These uncertainties will affect any scenarios that investigate changes to land use or irrigation practices. Finally, the model has relatively high water-level residuals around and to the southwest of Lake Lowell and should not be used to estimate water level effects in that region.
The model was built with multiple, broadly expressed objectives and did not optimize performance for specific uses. However, the model and the tools included in an associated data release provide ample flexibility to improve the model for future uses. Adjustments and improvements could be made by refining the model in an area of interest, collecting additional calibration data, applying more rigorous boundary conditions, or re-estimating model parameters to optimize model performance for a specific model forecast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235096","collaboration":"Prepared in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources","usgsCitation":"Hundt, S.A., and Bartolino, J.R., 2023, Groundwater-flow model of the Treasure Valley, southwestern Idaho, 1986–2015: U.S. Geological Survey Scientific Investigations Report 2023–5096, 107 p., https://doi.org/10.3133/sir20235096.","productDescription":"Report: xii, 107 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127901","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":501062,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115439.htm","linkFileType":{"id":5,"text":"html"}},{"id":421318,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5096"},{"id":421321,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5096/images"},{"id":421317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5096/coverthb.jpg"},{"id":421320,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6OOPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and archive for a groundwater flow model of the Treasure Valley aquifer system, southwestern Idaho"},{"id":421322,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.XML"}],"country":"United States","state":"Idaho","otherGeospatial":"Treasure Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.26392993194762,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 44.27650517719664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Sparta-Memphis aquifer, present across much of eastern Arkansas, is the second most used groundwater resource in the State, with the Mississippi River Valley alluvial aquifer being the primary groundwater resource. The U.S. Geological Survey, in cooperation with Arkansas Department of Agriculture-Natural Resources Division, Arkansas Geological Survey, Natural Resources Conservation Service, Union County Water Conservation Board, and the Union County Conservation District, collects groundwater data across the Sparta-Memphis aquifer extent in Arkansas. This report presents water-level data for measurements conducted during two time periods, January–May 2013 and January–June 2015, and discusses water-level altitude changes for the 2011–13 and 2013–15 periods in the Sparta-Memphis aquifer. Accompanying water-level data in this report include groundwater-quality data for the period 2010–15 in the Sparta-Memphis aquifer. Groundwater data can guide ongoing and future groundwater-monitoring efforts and inform management of the aquifers in Arkansas.
Water levels measured at 306 wells from January to May 2013 and 273 wells from January to June 2015 are graphically presented as potentiometric-surface maps. Measurements from 2011, 2013, and 2015 were used in the construction of 2011–13 and 2013–15 water-level change maps. Select long-term hydrographs are included in the report to illustrate water-level changes at the local scale.
Water-level data show the influence of climate, pumping, and conservation and management efforts on groundwater levels. With respect to climate, the study area experienced extreme drought conditions between January 2011 and December 2012. The proximate effects of drought—increased evapotranspiration, decreased recharge, and increased irrigation needs—resulted in water-level declines that were particularly notable in the northern and central portions of the study area.
Groundwater sampled in 2010–15 from 148 wells completed in the Sparta-Memphis aquifer was analyzed for specific conductance, pH, chloride (Cl) concentration, and bromide (Br) concentration. In 2015, groundwater-quality data from 103 wells completed in the Sparta-Memphis aquifer had a median specific conductance of 356 microsiemens per centimeter at 25 degrees Celsius and a median Cl concentration of 9.5 milligrams per liter (mg/L). The data show two areas of higher Cl (greater than 10 mg/L) and higher Br (greater than 0.5 mg/L) concentrations in Union, Calhoun, and Bradley Counties in southern Arkansas and Monroe and Phillips Counties in eastern-central Arkansas. A Cl and Br mixing model indicates the two regions of wells may have different sources of higher salinity. In the greater Union County area, water in most wells may be a mixture of recharge or precipitation and higher salinity groundwater from the Nacatoch aquifer. Water in wells in eastern-central Arkansas may be sourced from aquifers having a higher Cl concentration (and thus, also a higher Cl-to-Br ratio).
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Compound earthquakes involving simultaneous ruptures along multiple faults often define a region’s upper threshold of maximum magnitude. Yet, the potential for linked faulting remains poorly understood given the infrequency of these events in the historic era. Geological records provide longer perspectives, although temporal uncertainties are too broad to clearly pinpoint single multifault events. Here, we use dendrochronological dating and a cosmogenic radiation pulse to constrain the death dates of earthquake-killed trees along two adjacent fault zones near Seattle, Washington to within a 6-month period between the 923 and 924 CE growing seasons. Our narrow constraints conclusively show linked rupturing that occurred either as a single composite earthquake of estimated magnitude 7.8 or as a closely spaced double earthquake sequence with estimated magnitudes of 7.5 and 7.3. These scenarios, which are not recognized in current hazard models, increase the maximum earthquake size needed for seismic preparedness and engineering design within the Puget Sound region of >4 million residents.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adh4973","usgsCitation":"Black, B., Pearl, J., Pearson, C., Pringle, P., Frank, D., Page, M.T., Buckley, B., Cook, E.R., Harley, G.L., King, K., Hughes, J.F., Reynolds, D.J., and Sherrod, B.L., 2023, A multifault earthquake threat for the Seattle metropolitan region revealed by mass tree mortality: Science Advances, v. 9, no. 39, eadh4973, 9 p., https://doi.org/10.1126/sciadv.adh4973.","productDescription":"eadh4973, 9 p.","ipdsId":"IP-143345","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adh4973","text":"Publisher Index Page"},{"id":482643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","city":"Seattle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.4,\n 47.7\n ],\n [\n -123.4,\n 47\n ],\n [\n -122,\n 47\n ],\n [\n -122,\n 47.7\n ],\n [\n -123.4,\n 47.7\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","issue":"39","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Black, Bryan","contributorId":300775,"corporation":false,"usgs":false,"family":"Black","given":"Bryan","affiliations":[{"id":65257,"text":"University of Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":929114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearl, Jessie K. 0000-0002-1556-2159","orcid":"https://orcid.org/0000-0002-1556-2159","contributorId":336799,"corporation":false,"usgs":false,"family":"Pearl","given":"Jessie K.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":929115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearson, Charlotte","contributorId":351616,"corporation":false,"usgs":false,"family":"Pearson","given":"Charlotte","affiliations":[{"id":28236,"text":"Univ of Arizona","active":true,"usgs":false}],"preferred":false,"id":929116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pringle, Patrick T.","contributorId":330195,"corporation":false,"usgs":false,"family":"Pringle","given":"Patrick T.","affiliations":[{"id":78849,"text":"Centralia College, Washington","active":true,"usgs":false}],"preferred":false,"id":929117,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frank, David C.","contributorId":351617,"corporation":false,"usgs":false,"family":"Frank","given":"David C.","affiliations":[{"id":28236,"text":"Univ of Arizona","active":true,"usgs":false}],"preferred":false,"id":929118,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929119,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buckley, Brendan M.","contributorId":351618,"corporation":false,"usgs":false,"family":"Buckley","given":"Brendan M.","affiliations":[{"id":84016,"text":"Lamont-Dohtery Earth Obs.","active":true,"usgs":false}],"preferred":false,"id":929120,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cook, Edward R.","contributorId":225235,"corporation":false,"usgs":false,"family":"Cook","given":"Edward","email":"","middleInitial":"R.","affiliations":[{"id":17701,"text":"Lamont-Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":929121,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Harley, Grant L.","contributorId":204186,"corporation":false,"usgs":false,"family":"Harley","given":"Grant","email":"","middleInitial":"L.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":929122,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"King, Karen J.","contributorId":351635,"corporation":false,"usgs":false,"family":"King","given":"Karen J.","affiliations":[],"preferred":false,"id":929123,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hughes, Jonathan F.","contributorId":184055,"corporation":false,"usgs":false,"family":"Hughes","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":929124,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Reynolds, David J.","contributorId":279711,"corporation":false,"usgs":false,"family":"Reynolds","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":57351,"text":"Centre for Geography and Environmental Sciences, University of Exeter, Penryn, Cornwall, TR10 9EZ, UK","active":true,"usgs":false}],"preferred":false,"id":929125,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929126,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70240871,"text":"sir20235001 - 2023 - Flood-inundation maps created using a synthetic rating curve for a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021","interactions":[],"lastModifiedDate":"2026-02-24T18:06:51.154849","indexId":"sir20235001","displayToPublicDate":"2023-09-26T15:06:54","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":"2023-5001","displayTitle":"Flood-Inundation Maps Created Using a Synthetic Rating Curve for a 10-Mile Reach of the Sabinal River and a 7-Mile Reach of the West Sabinal River Near Utopia, Texas, 2021","title":"Flood-inundation maps created using a synthetic rating curve for a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021","docAbstract":"In 2021, the U.S. Geological Survey (USGS), in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, studied floods to produce a library of flood-inundation maps for the Sabinal River near Utopia, Texas. Digital flood-inundation maps were created for a 10-mile reach of the Sabinal River from USGS streamgage 08197936 Sabinal River below Mill Creek near Vanderpool, Tex., at the upstream boundary of the study reach, to USGS streamgage 08197970 Sabinal River at Utopia, Tex. (hereinafter referred to as the “Utopia gage”), at the downstream boundary of the study reach, and for a 7-mile reach of the West Sabinal River. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to selected gage heights (the water-surface elevation at a streamgage, commonly referred to as “stage”) at the Utopia gage. Water-surface elevations were computed for the stream reach by means of a two-dimensional unsteady-state diffusion wave model with the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System program. A synthetic stage-discharge rating curve at the Utopia gage was developed using a regional regression equation to construct the model boundary condition inputs, and the upper bound of the stage-discharge relation was matched to a major flood event in July 2002. The hydraulic model was used to compute water-surface elevations for 35 stages at 0.5-foot (ft) increments referenced to the Utopia gage datum and ranging from 11 ft (near bankfull) to 28 ft (estimated peak stage during the July 2002 flood event). These flood-inundation maps, in conjunction with the real-time stage data from the Utopia gage, are intended to help guide the public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235001","issn":"2328-0328 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood-inundation maps created using a synthetic rating curve for a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021 (ver. 2.0, September 2023): U.S. Geological Survey Scientific Investigations Report 2023–5001, 18 p., https://doi.org/10.3133/sir20235001.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-136311","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":435168,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CIK9ZF","text":"USGS data release","linkHelpText":"Geospatial and model dataset for flood-Inundation maps in a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021"},{"id":421129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5001/coverthb.jpg"},{"id":500486,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114428.htm","linkFileType":{"id":5,"text":"html"}},{"id":421198,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20233001","text":"Fact Sheet 2023–3001","description":"USGS Fact Sheet 2023–3001","linkHelpText":"- Flood Warning Toolset for the Sabinal River Near Utopia, Texas"},{"id":421197,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5001/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2023-5001 version history"},{"id":421196,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5001/sir20235001.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5001 XML"},{"id":421193,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5001/images"},{"id":421599,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235001/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5001 HTML"},{"id":421194,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5001/sir20235001.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5001"}],"country":"United States","state":"Texas","city":"Utopia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.38878556700459,\n 29.515266260991964\n ],\n [\n -99.38878556700459,\n 29.797981198043047\n ],\n [\n -99.67156342604174,\n 29.797981198043047\n ],\n [\n -99.67156342604174,\n 29.515266260991964\n ],\n [\n -99.38878556700459,\n 29.515266260991964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
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The Equus Beds aquifer and Cheney Reservoir are primary sources for the city of Wichita’s current (2023) water supply. The Equus Beds aquifer storage and recovery (ASR) project was developed by the city of Wichita in the early 1990s to meet future water demands using the Little Arkansas River as an artificial aquifer recharge water source during above-base-flow conditions. Little Arkansas River water is removed from the river at an ASR Facility intake structure, treated using National Primary Drinking Water Regulations as a guideline, and is infiltrated into the Equus Beds aquifer through recharge basins or injected into the aquifer through recharge wells for later use. The U.S. Geological Survey, in cooperation with the city of Wichita, completed this study to quantify and characterize Little Arkansas River water-quality data. Data in this report can be used to evaluate changing conditions, provide science-based information for decision making, and help meet regulatory requirements.
Continuous (hourly) physicochemical properties were measured, and discrete water-quality samples were collected from three Little Arkansas River sites located along the easternmost extent of the Equus Beds aquifer during 1995 through 2021 over a range of streamflow conditions. The Little Arkansas River at Highway 50 near Halstead, Kansas, streamgage (U.S. Geological Survey station 07143672; hereafter referred to as the “Highway 50 site”) is located upstream from the other two sites, and the Little Arkansas River near Sedgwick, Kans., streamgage (U.S. Geological Survey station 07144100; hereafter referred to as the “Sedgwick site”) is located downstream from the other two sites; these two sites bracket most of the easternmost part of the Equus Beds aquifer. The Little Arkansas River upstream of ASR Facility near Sedgwick, Kans., streamgage (U.S. Geological Survey station 375350097262800; hereafter referred to as the “Upstream ASR site”) is located between the Highway 50 and Sedgwick sites, about 14.7 river miles (mi) downstream from the Highway 50 site, about 1.7 river mi upstream from the Sedgwick site, and immediately upstream from the ASR Facility intake structure. Surrogate models for water-quality constituents of interest (including bromide, dissolved organic carbon, 2-chloro-4-isopropylamino-6-amino-s-triazine [deethylatrazine], atrazine, and metolachlor) were updated or developed using continuously measured and concomitant discrete data. These surrogate models, along with previously developed regression models, were used to compute concentrations (at the Highway 50, Sedgwick, and Upstream ASR sites) and loads (at the Highway 50 and Sedgwick sites) during the study period. Federal criteria were used to evaluate water quality. Where applicable, water-quality data were compared to Federal national drinking-water regulations. Flow-normalized water-quality constituent trends were evaluated using Weighted Regressions on Time, Discharge, and Season (WRTDS) statistical models and water-quality trends were described using WRTDS bootstrap tests.
Continuously computed primary ion concentrations were generally larger at the Highway 50 site compared to the Sedgwick site. During the study period, the Federal secondary maximum contaminant level (SMCL) for dissolved solids was exceeded 57 percent of the time at the Highway 50 site and 38 percent of the time at the Sedgwick site. Computed bromide concentrations were larger at the Highway 50 site and exceeded the city of Wichita treatment threshold about 70, 21, and 19 percent of the time at the Highway 50, Sedgwick, and Upstream ASR sites, respectively. Chloride concentrations exceeded the Federal SMCL about 16 percent of the time at the Highway 50 site and did not exceed the SMCL at the Sedgwick site. Continuous arsenic concentrations exceeded the Federal Maximum Contaminant Level (MCL) 9 to 15 percent of the time at the Sedgwick and Highway 50 sites, respectively, during the study. Atrazine concentrations exceeded the Federal MCL 10 percent of the time at the Highway 50 and Sedgwick sites and 14 percent of the time at the Upstream ASR site during the study; computed glyphosate concentrations at the Sedgwick site never exceeded the MCL during the study.
Little Arkansas River flow-normalized primary ion concentrations during 1995 through 2021 generally had downward trends and decreases were generally larger at the Highway 50 site compared to the Sedgwick site. Dissolved solids and chloride concentrations decreased at the Highway 50 and Sedgwick sites. Bromide had no trend at the Highway 50 site and a downward trend at the Sedgwick site. Nitrate plus nitrite and total phosphorus concentrations had upward trends at the Highway 50 site but downward trends at the Sedgwick site, whereas total organic carbon had upward trends at both sites. Nitrate plus nitrite, total nitrogen, total phosphorus, and total organic carbon fluxes had upward trends at the Highway 50 and Sedgwick sites. Suspended-sediment concentrations had an upward trend at the Highway 50 site and had no trend at the Sedgwick site. Arsenic concentrations had downward trends at the Highway 50 and Sedgwick sites.
About one-quarter to one-half of the Little Arkansas River loads, including nutrients and sediment, were transported during 1 percent of the time during the study. Because streamflows are highly sensitive to climatic variation and an increase of extreme precipitation events in the Great Plains is expected, similar disproportionately large pollutant loading events may increase into the future. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. Continuation of this water-quality monitoring will provide data to characterize changing conditions in the Little Arkansas River and possibly identify new and changing trends. Information in this report allows the city of Wichita to make informed municipal water-supply decisions using past and present water-quality conditions and trends in the watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235102","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2023, Long-term water-quality constituent trends in the Little Arkansas River, south-central Kansas, 1995–2021: U.S. Geological Survey Scientific Investigations Report 2023–5102, 103 p., https://doi.org/10.3133/sir20235102.","productDescription":"Report: ix, 103 p.; 1 Figure; 9 Tables; 5 Appendixes; Dataset","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-146544","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":421187,"rank":26,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix10.zip","text":"Appendix 10","size":"46 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Weighted Regressions on Time, Discharge, 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A, YSI 6026 (YSI6026) and YSI 6136 (YSI6136) at the Little Arkansas River at Highway 50 near Halstead, Kansas"},{"id":421190,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":421169,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5102/images/"},{"id":421168,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102.XML","linkFileType":{"id":8,"text":"xml"}},{"id":501150,"rank":27,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115440.htm","linkFileType":{"id":5,"text":"html"}},{"id":421182,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_tables8.1-8.3.xlsx","text":"Tables 8.1–8.3","size":"112 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":421167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5102"},{"id":421188,"rank":20,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table11.1.xlsx","text":"Table 11.1","size":"51 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated yearly water-quality constituent loads at the Little Arkansas River at Highway 50 near Halstead, Kansas and near Sedgwick, Kans., 1998–2021"},{"id":421166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5102/coverthb.jpg"},{"id":421189,"rank":21,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table11.1.csv","text":"Table 11.1","size":"14 KB","linkFileType":{"id":7,"text":"csv"}},{"id":421201,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235102/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.1667,\n 38.6\n ],\n [\n -98.1667,\n 37.5\n ],\n [\n -97.25,\n 37.5\n ],\n [\n -97.25,\n 38.6\n ],\n [\n -98.1667,\n 38.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
PyLith, a community, open-source code for modelling quasi-static and dynamic crustal deformation with an emphasis on earthquake faulting, has recently been updated with a flexible multiphysics implementation. We demonstrate the versatility of the multiphysics implementation by extending the code to model fully coupled continuum poromechanics. We verify the newly incorporated physics using standard benchmarks for a porous medium saturated with a slightly compressible fluid. The benchmarks include the 1-D consolidation problem as outlined by Terzaghi, Mandel’s problem for the 2-D case, and Cryer’s problem for the 3-D case. All three benchmarks have been added to the PyLith continuous integration test suite. We compare the closed form analytical solution for each benchmark against solutions generated by our updated code, and lastly, demonstrate that the poroelastic material formulation may be used alongside the existing fault implementation in PyLith.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggad370","usgsCitation":"Walker, R.L., Knepley, M.G., Aagaard, B.T., and Williams, C.A., 2023, Multiphysics modelling in PyLith: Poroelasticity: Geophysical Journal International, v. 235, no. 3, p. 2442-2475, https://doi.org/10.1093/gji/ggad370.","productDescription":"34 p.","startPage":"2442","endPage":"2475","ipdsId":"IP-146478","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":424189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"235","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Robert L.","contributorId":333017,"corporation":false,"usgs":false,"family":"Walker","given":"Robert","email":"","middleInitial":"L.","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":891663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knepley, Matthew G.","contributorId":333018,"corporation":false,"usgs":false,"family":"Knepley","given":"Matthew","email":"","middleInitial":"G.","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":891664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":891665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Charles A.","contributorId":333019,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","middleInitial":"A.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":891666,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250550,"text":"70250550 - 2023 - Vegetation change over 140 years in a sagebrush landscape of the Rio Grande del Norte National Monument, New Mexico, USA","interactions":[],"lastModifiedDate":"2023-12-15T13:11:04.845576","indexId":"70250550","displayToPublicDate":"2023-09-26T06:58:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2490,"text":"Journal of Vegetation Science","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation change over 140 years in a sagebrush landscape of the Rio Grande del Norte National Monument, New Mexico, USA","docAbstract":"Big sagebrush (Artemisia tridentata) ecosystems across the western United States have experienced many changes in ecosystem dynamics and vegetation composition over the last century due to livestock grazing, non-native species, and changing climate and fire regimes. We conducted the first systematic investigation of historical vegetation composition and vegetation change in a sagebrush landscape in the southwestern United States, asking whether sagebrush or grass dominated the landscape historically?
The Rio Grande del Norte National Monument (RGDN), northern New Mexico, USA.
We combined General Land Office (GLO) surveys from 1881 with modern vegetation maps, field vegetation surveys, and sagebrush ages from growth ring analysis to test for changes in vegetation in the RGDN over the last 140 years.
We found that big sagebrush presence across the study area increased significantly, from being present on 16% of section lines in 1881 to 79% in 2019, and only three section lines lost sagebrush presence during that period. Concurrently, the number of section lines with low grass index more than doubled since 1881, while moderate and high grass index declined. Grass declined equally in areas where sagebrush increased and areas with no change in sagebrush, suggesting that changes in both vegetation types were catalyzed by external factors, likely including overgrazing. The growth ring analysis of 93 sagebrush revealed a maximum age of 87 years and establishment in every decade since the 1930s, consistent with the GLO results.
The significant vegetation changes in the RGDN over the last century, including an increase of sagebrush, provide important context about the shifting mosaic of grasslands and shrublands relevant to current and future management and ecosystem dynamics.
","language":"English","publisher":"Wiley","doi":"10.1111/jvs.13202","usgsCitation":"Fox, K., Margolis, E.Q., Lopez, M.K., Kasten, E., and Stevens, J., 2023, Vegetation change over 140 years in a sagebrush landscape of the Rio Grande del Norte National Monument, New Mexico, USA: Journal of Vegetation Science, v. 34, no. 5, e13202, 17 p., https://doi.org/10.1111/jvs.13202.","productDescription":"e13202, 17 p.","ipdsId":"IP-151212","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":423620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio Grande del Norte National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5540076425751,\n 37.026637990156544\n ],\n [\n -106.5540076425751,\n 36.224664603824266\n ],\n [\n -105.13109401921486,\n 36.224664603824266\n ],\n [\n -105.13109401921486,\n 37.026637990156544\n ],\n [\n -106.5540076425751,\n 37.026637990156544\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Fox, Kara","contributorId":261706,"corporation":false,"usgs":false,"family":"Fox","given":"Kara","email":"","affiliations":[],"preferred":false,"id":890344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":890345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez, Manuel K.","contributorId":298167,"corporation":false,"usgs":false,"family":"Lopez","given":"Manuel","email":"","middleInitial":"K.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":890346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasten, Ella","contributorId":332521,"corporation":false,"usgs":false,"family":"Kasten","given":"Ella","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":890347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stevens, J.T.","contributorId":332522,"corporation":false,"usgs":false,"family":"Stevens","given":"J.T.","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":890348,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256445,"text":"70256445 - 2023 - Lessons learned in applying decision analysis to natural resource management for high stakes issues surrounded by uncertainty","interactions":[],"lastModifiedDate":"2024-08-02T15:54:38.642442","indexId":"70256445","displayToPublicDate":"2023-09-25T10:49:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14243,"text":"Decision Analysis","active":true,"publicationSubtype":{"id":10}},"title":"Lessons learned in applying decision analysis to natural resource management for high stakes issues surrounded by uncertainty","docAbstract":"Management agencies are tasked with difficult decisions for conservation and management of natural resources. These decisions are difficult because of ecological and social uncertainties, the potential for multiple decision makers from multiple jurisdictions, and the need to account for the diverse values of stakeholders. Decision analysis provides a framework for accounting for these difficulties when making conservation and management decisions. We discuss the benefits of the application of decision analysis for these types of issues and provide insights from three case studies from the Laurentian Great Lakes. These case studies describe applications of decision analysis for decisions within an agency (management of double-crested cormorant), among agencies (response to invasive grass carp), and among agencies and stakeholders (sustainable fisheries harvest management). These case studies provide insight into the ways that decision analysis can be useful for conservation and management of natural resources, but we also highlight future needs for decision making for these resources. In particular, applications of decision analysis for conservation and management would benefit from enhanced integration of both ecological and social science, inclusion of a broader base of stakeholders and rightsholders, and better educational opportunities surrounding decision analysis for undergraduates and graduate students of natural resources management programs. Specific lessons from our experiences include the importance of establishing trust and transparency early through the formation of a working group, collaboratively defining objectives and evaluating uncertainties, risks, and tradeoffs, and implementing participatory modeling processes with an independent facilitator with appropriate quantitative skills.
","language":"English","publisher":"Informs","doi":"10.1287/deca.2023.0015","usgsCitation":"Robinson, K.F., Dufour, M.R., Fischer, J., Herbst, S.J., Jones, M., Nathan, L.R., and Newcomb, T.J., 2023, Lessons learned in applying decision analysis to natural resource management for high stakes issues surrounded by uncertainty: Decision Analysis, v. 20, no. 4, p. 326-342, https://doi.org/10.1287/deca.2023.0015.","productDescription":"17 p.","startPage":"326","endPage":"342","ipdsId":"IP-149566","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":442032,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://osf.io/3bgt9","text":"External Repository"},{"id":432151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Kelly Filer 0000-0001-8109-9492","orcid":"https://orcid.org/0000-0001-8109-9492","contributorId":340631,"corporation":false,"usgs":true,"family":"Robinson","given":"Kelly","email":"","middleInitial":"Filer","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":907415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischer, Jason L.","contributorId":241112,"corporation":false,"usgs":false,"family":"Fischer","given":"Jason L.","affiliations":[],"preferred":false,"id":907416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herbst, Seth J.","contributorId":11102,"corporation":false,"usgs":true,"family":"Herbst","given":"Seth","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":907417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Michael L.","contributorId":126763,"corporation":false,"usgs":false,"family":"Jones","given":"Michael L.","affiliations":[{"id":6600,"text":"Qauntitative Fisheries Center, Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907418,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nathan, Lucas R.","contributorId":340047,"corporation":false,"usgs":false,"family":"Nathan","given":"Lucas","email":"","middleInitial":"R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907419,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Newcomb, Tammy J.","contributorId":13908,"corporation":false,"usgs":true,"family":"Newcomb","given":"Tammy","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":907420,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248995,"text":"70248995 - 2023 - Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin","interactions":[],"lastModifiedDate":"2025-12-11T22:25:34.900135","indexId":"70248995","displayToPublicDate":"2023-09-25T06:49:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16883,"text":"European Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin","docAbstract":"Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.