While nonstationary flood frequency analysis (NSFFA) methods have proliferated, few studies have rigorously compared them for modeling changes in both the central tendency and variability of annual peak-flow series, also known as the annual maximum series (AMS), in hydrologically diverse areas. Through Monte Carlo experiments, we appraise five methods for updating estimates of 10- and 100-year floods at gauged sites using synthetic records based on sample moments and change trajectories of observed AMS in the conterminous United States (CONUS). We compare two methods that consider changes in both central tendency and variability - a Gamma generalized linear model estimated with weighted least squares and the Generalized Additive Model for Location, Scale, Shape (GAMLSS) - with a distribution-free approach (quantile regression), and baseline cases assuming stationarity or only changes in central tendency.
‘Trend-space’ plots identify realistic AMS changes for which modeling trends in both central tendency and variability were warranted based on fractional root mean squared errors (fRMSE). They also reveal statistical properties of AMS under which NSFFA models perform especially well or poorly. For instance, quantile regression performed especially well (poorly) under strong negative (positive) skewness. Although the nonstationary LP3 distribution accommodates most AMS with trends well, the sensitivity of NSFFA model performance to different sample moments and trends suggests the need for more flexibility in prescribing design-flood adjustments in the CONUS. A follow-up comparison of regional NSFFA models pooling at-site AMS would further illuminate NSFFA guidance, especially for AMS with properties less conducive to NSFFA modeling, such as positive skewness and increasing variability.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2021.100115","usgsCitation":"Hecht, J., Barth, N.A., Ryberg, K.R., and Gregory, A., 2022, Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States: Journal of Hydrology X, v. 17, 100115, 24 p., https://doi.org/10.1016/j.hydroa.2021.100115.","productDescription":"100115, 24 p.","ipdsId":"IP-129280","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":446110,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2021.100115","text":"Publisher Index Page"},{"id":435655,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PVRCDS","text":"USGS data release","linkHelpText":"Data for simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States"},{"id":408467,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.32031249999997,\n 24.5271348225978\n ],\n [\n -65.91796875,\n 24.5271348225978\n ],\n [\n -65.91796875,\n 50.958426723359935\n ],\n [\n -128.32031249999997,\n 50.958426723359935\n ],\n [\n -128.32031249999997,\n 24.5271348225978\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hecht, Jory Seth","contributorId":298019,"corporation":false,"usgs":true,"family":"Hecht","given":"Jory Seth","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":854875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":854876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gregory, Angela 0000-0002-9905-1240","orcid":"https://orcid.org/0000-0002-9905-1240","contributorId":45018,"corporation":false,"usgs":true,"family":"Gregory","given":"Angela","email":"","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854938,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237663,"text":"70237663 - 2022 - Formation of orogenic gold deposits by progressive movement of a fault-fracture mesh through the upper crustal brittle-ductile transition zone","interactions":[],"lastModifiedDate":"2022-10-18T11:56:10.635076","indexId":"70237663","displayToPublicDate":"2022-10-17T06:48:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Formation of orogenic gold deposits by progressive movement of a fault-fracture mesh through the upper crustal brittle-ductile transition zone","docAbstract":"Orogenic gold deposits are comprised of complex quartz vein arrays that form as a result of fluid flow along transcrustal fault zones in active orogenic belts. Mineral precipitation in these deposits occurs under variable pressure conditions, but a mechanism explaining how the pressure regimes evolve through time has not previously been proposed. Here we show that extensional quartz veins at the Garrcon deposit in the Abitibi greenstone belt of Canada preserve petrographic characteristics suggesting that the three recognized paragenetic stages formed within different pressure regimes. The first stage involved the growth of interlocking quartz grains competing for space in fractures held open by hydrothermal fluids at supralithostatic pressures. Subsequent fluid flow at fluctuating pressure conditions caused recrystallization of the vein quartz and the precipitation of sulfide minerals through wall-rock sulfidation, with some of the sulfide minerals containing microscopic gold. These pressure fluctuations between supralithostatic to near-hydrostatic conditions resulted in the post-entrapment modification of the fluid inclusion inventory of the quartz. Late fluid flow occurred at near-hydrostatic conditions and resulted in the formation of fluid inclusions that have not been affected by post-entrapment modification as pressure conditions never returned to supralithostatic conditions. This late fluid flow is interpreted to have formed the texturally late, coarse native gold that occurs along quartz grain boundaries and in open spaces. The systematic evolution of the pressure regimes in orogenic gold deposits such as Garrcon can be explained by relative movement of fault-fracture meshes across the base of the upper crustal brittle-ductile transition zone. We conclude that early vein quartz in orogenic deposits is precipitated at near-lithostatic conditions whereas the paragenetically late gold is introduced at distinctly lower pressure.
This study characterized the concentration and quality of dissolved organic carbon (DOC) in the McKenzie River, a relatively undeveloped watershed in western Oregon, and its link to forming disinfection by-products (DBPs) in treated drinking water. The study aimed to identify the primary source(s) of DOC in source water for the Eugene Water & Electric Board’s (EWEB) conventional treatment plant on the McKenzie River near river mile 11, upstream of Hayden Bridge. The two classes of regulated compounds examined—trihalomethanes (THMs) and haloacetic acids (HAAs)—form when organic carbon in raw source water reacts with chlorine and (or) bromine during water treatment.
The objectives of the study were to:
Eleven storms were sampled synoptically in upstream-to-downstream fashion to provide a “snapshot” of water quality conditions at four sites on the McKenzie River from Frissell Bridge (6 miles downstream from Trail Bridge Reservoir) to the EWEB water treatment plant at Hayden Bridge and nine contributing tributaries. Storms included late summer and early autumn “first flush” events and late autumn, winter, and spring storms spanning a range in streamflows from 3,000 to 26,000 cubic feet per second as measured in the main stem McKenzie River at the EWEB water intake.
Water samples were analyzed for DOC concentrations and optical properties (fluorescence and ultraviolet absorbance [UVA]) across a range of wavelengths to characterize the quantity and quality of dissolved organic matter (DOM) in the McKenzie River at the drinking water intake and upstream locations. Paired sets of source and finished water samples were collected at the EWEB treatment plant to identify DOC quality parameters in raw source water that might predict DBP concentrations in finished drinking water.
DOC concentrations were relatively low in the McKenzie River (0.4–3 milligrams per liter [mg/L]; average 1.5 mg/L) but much higher in the tributaries. The highest DOC concentrations occurred during “first flush” storms in October 2012 and September 2013; the highest value (16 mg/L) was measured at the 52nd Street stormwater outfall. The average DOC concentration in the lower basin-tributaries was 3.8 mg/L; three middle basin tributaries—Quartz, Gate, and Haagen Creeks, which drain private forestland with less coniferous forest compared with other higher elevation tributaries— had slightly lower average DOC concentrations (2.8 mg/L). These middle-basin watersheds may be important sources of DOC and DBP precursors to the McKenzie River, even more so than the lower basin tributaries, depending on their flows (and loads). This is particularly true after the September 2020 Holiday Farm fire, which burned much of this area.
DOC concentrations increased 68 percent in the McKenzie River between the uppermost reference site at Frissell Bridge and Vida; this includes drainage from Quartz Creek, Blue River Lake and Cougar Reservoir, which all contributed DOC to the main stem. In contrast, the lowermost tributaries draining most of the agricultural and urban land did not have a large effect on DOC in the McKenzie River despite their higher DOC concentrations because of their presumed relatively low streamflows and, consequently, DOC loads. Apart from the continuous flow monitors in the McKenzie River and some tributaries (Blue River and South Fork McKenzie River, and streamflow at Hayden Bridge and Vida, Camp Creek and some other locations), streamflow was not assessed during sample collection for this study. This lack of streamflow data precludes a detailed analysis of loads, which is discussed in the future studies section.
All DBP concentrations in finished drinking water were less than EPA maximum contaminant levels (MCLs) of 0.080 mg/L for the four trihalomethanes (THM4) and 0.060 mg/L for five haloacetic acids (HAA5). During the 11 storm sampling events the maximum summed concentrations were about 0.040 mg/L for both THM4 and HAA5. Compliance monitoring samples, collected separately by EWEB, yielded some higher concentrations—0.046 mg/L THM4 and 0.047 HAA5—during the December 2012 storm. The corresponding benchmark quotient (BQ) values, which indicate how close a measured DBP concentration is to the MCL, were 0.58 and 0.78, respectively, for THM4 and HAA5. Compared with a similar 2007–08 McKenzie River study that did not target storm events, concentrations of THM4 and HAA5 in finished water were 68 percent and 33 percent higher, respectively, during the current study.
Due to the high dilution rates in the McKenzie River main stem, many of the individual fluorescence excitation-emission measurements were low (<0.1 Raman units) and approached analytical detection limits. Parallel factor analysis (PARAFAC) resulted in a five-component model (C1–C5) that represents five unique organic fluorophores. Components C1, C2, and C3 represent DOM associated with soil-derived, humic-like, more degraded organic matter. In contrast, components C4 and C5 represent “fresher” DOM, derived from terrestrial and aquatic plants, including algae and cyanobacteria that are common in the McKenzie River and its tributaries and reservoirs. The fluorescence data and PARAFAC modeling suggest that most of the DOC in the McKenzie River originated from terrestrial sources (primarily components C1 and C2). The largest increases in DOC in the main stem occurred in the reach upstream of Vida, from inflows by Quartz Creek, Blue River, South Fork McKenzie River, and other tributaries.
Concentrations of DBPs in EWEB’s finished drinking water were positively correlated with DOC concentrations in raw source water (THM4, p<0.05; HAA5, p<0.01) for paired samples collected 12−24 hours apart. DOC concentrations were significantly positively correlated (p<0.001) with laboratory-based fluorescent dissolved organic matter (fDOM) measurements, suggesting fDOM as a useful parameter for monitoring and predicting DOC concentration in surface water and DBP concentrations in finished water.
Of all the PARAFAC components in surface water, C5 had the highest correlations with DBPs in finished water (rho = 0.77–0.84, p<0.01), followed by components C1 and C2 (rho = 0.75 and 0.71, respectively, p<0.01). This C5 carbon is associated with recently produced DOM, possibly from decomposed terrestrial and aquatic vegetation. Model loadings of these three components were considerably higher in the sampled tributaries relative to the main stem McKenzie River, with most of the observed increases in the main stem apparent at Vida. This points to Quartz Creek or other tributaries in the reach between Frissell Bridge and the sampling site near Vida (South Fork McKenzie and Blue Rivers) as potentially key contributors of DOM source material that leads to the production of DBPs in treated drinking water. A limited load analysis showed that the reservoirs contributed 8–37 percent of the instantaneous DOC loads observed at Vida at the time of sampling, which suggests other sources such as Quartz Creek and other streams in the reach between Frissell Bridge and Vida are more important.
Random forest analyses identified PARAFAC components C1 and C5 and fluorescence peaks A, C, M, T and N as the best predictors for HAA5 concentrations in finished drinking water, explaining 62.5 percent of the variation. The best predictors for THM4 were C1, C4 + C5, and peaks T, A, and N, which explained 33 percent of the variation.
Several land cover and vegetation classes were correlated with DOC concentration and other optical measurements. The percentage of evergreen forest in each of the subwatersheds sampled was negatively correlated (p<0.001) with DOC concentration and many optical indicators of DOM quantity: UVA254, fDOM, and all of the fluorescence peaks. In contrast, mixed (deciduous) forest was positively correlated (p<0.001) with DOC, fDOM, UVA254, and several fluorescence peaks, demonstrating the importance of deciduous leaf fall in generating DOC and DBP precursors.
The high level of human activities in the middle and lower portion of the basin—including timber harvesting and road construction on private forestland, agricultural, rural, industrial, and urban development—have resulted in the greatest loss in native coniferous and mixed deciduous forests in the basin. DOC loading from these tributaries and reservoir releases, which contain DOC from terrestrial and aquatic productivity, both enrich the McKenzie River. Concentrations of DOC increased an average of 71 percent (range 30–120 percent) in the McKenzie River between Frissell Bridge, the upstream reference site, and Vida. PARAFAC components C1, C2, and C5—which were correlated with DBPs in finished water—increased, on average, 109–136 percent (range 20–250 percent) in this same Frissell-to-Vida reach. These increases occur from input of tributaries in the middle basin such as Quartz Creek and others, as noted above.
Future monitoring, field, and lab studies can improve our understanding of seasonal and spatial sources of organic carbon contributing DBP precursors to the McKenzie River and allow detection of long-term trends resulting from the recent Holiday Farm Fire, which burned 173,393 acres of forestland, including riparian areas along the main stem, and numerous structures, homes, and outbuildings in September 2020. Future studies could examine DOC fluxes and flushing of carbon from the watershed, investigate the role of precipitation amount and intensity in mobilizing carbon and sediment, and evaluate impacts to aquatic communities and human health as part of a post-fire assessment. Other areas ripe for study include evaluating the impacts of potential temperature increases on carbon sequestration and decomposition in the burned and unburned forests and identifying practices that foster sequestration of carbon in forest soils.
The use of fluorescence sensors such as fDOM to monitor the concentration and composition of raw water supplies may be improved for detection of specific DBP precursors, to provide continuous and real-time information to treatment plant operators. Future studies that monitor DOM amount and quality, and DBP Formation Potential (FP), particularly during storm events, paired with streamflow measurements, as suggested above, could help identify areas that contribute high DOC loads and thus help managers identify the key areas to focus restoration activities. Other studies could examine treatment options for currently regulated DBPs and potentially unregulated compounds, including advanced biological treatments for their removal.
This study was a collaboration between the U.S. Geological Survey (USGS) and EWEB in Eugene, Oregon, with additional funding provided from USGS Cooperative Matching Funds Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225010","collaboration":"Prepared in Cooperation with Eugene Water & Electric Board","usgsCitation":"Carpenter, K.D., Kraus, T.E., Hansen, A.M., Downing, B.D., Goldman, J.H., Haynes, J., Donahue, D., and Morgenstern, K., 2022, Sources and characteristics of dissolved organic carbon in the McKenzie River, Oregon, related to the formation of disinfection by-products in treated drinking water: U.S. Geological Survey Scientific Investigations Report 2022–5010, 50 p., https://doi.org/10.3133/sir20225010.","productDescription":"Report: viii, 50 p.; Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117763","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":408395,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QPSIG3","text":"USGS data release","description":"USGS data release.","linkHelpText":"Absorbance and fluorescence measurements and concentrations of disinfection by-products in source water and finished water in the McKenzie River Basin, Oregon: 2012-2014"},{"id":408366,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010_table1.1.xlsx","text":"Table 1.1","size":"37 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5010 table 1.1"},{"id":408301,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5010/images"},{"id":408299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5010/coverthb2.jpg"},{"id":408302,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010.XML"},{"id":408300,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5010/sir20225010.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5010"},{"id":502297,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113766.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","otherGeospatial":"McKenzie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.125,\n 43.8\n ],\n [\n -121.875,\n 43.8\n ],\n [\n -121.875,\n 44.3\n ],\n [\n -123.125,\n 44.3\n ],\n [\n -123.125,\n 43.8\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
Oil spills can inflict mortality and injury on bird populations; many of these deaths involve starvation resulting from thermoregulatory costs incurred by oiling of birds’ feathers. However, the fates and responses of sublethally oiled birds are poorly known. Due to this knowledge gap and the potential for birds to die far from the spill site, resource risk and injury assessors need tools to make informed estimates for delayed deaths and lost reproductive capacity in these birds. Focusing on the thermoregulatory cost of oiled feathers, we present a model addressing one facet of the effects of sublethal oiling on birds. Using mallard-like ducks as a model organism, we combined values from previous laboratory studies of oiled birds with a modified version of an existing temperature-influenced avian migration energetics model. Using this model, we examined the potential effects of oiling on general migration patterns, changes in energetic gains required to compensate for oiling, and starvation. We assessed all metrics across multiple oiling severities; we assessed starvation across both oiling severity and body condition. Median estimates for delays in spring migration were one to two months for trace and lightly oiled birds, and we predicted arrested spring migration in moderately oiled birds. Median estimates of required increases in energetic gains to offset costs of increased thermoregulation ranged from 20.3% to 88.6% depending on severity of oiling. We predicted starvation within four weeks for most combinations of oiling severity and body condition at the median predicted minimum wintering temperature of unoiled birds (-4.9°C). However, at the average winter temperature of the southernmost model latitude (10.8°C), we predicted only moderately oiled birds in less-than-excellent body condition had the potential to starve within a four-week time frame. Due to the potential for even trace oiling to delay spring migration and decrease body condition, the thermoregulatory costs of sublethal oiling during spring migration could reduce a bird's reproductive capacity. Future research integrating this initial energetics-based model into a spatially explicit, population scale migration model could provide additional insight into the potential effects of sublethal oiling on reproduction and survival. Such an integrated model could strengthen risk predictions and injury assessments for birds subjected to sublethal oiling.
The late Pleistocene was a climatically dynamic period, with abrupt shifts between cool-wet and warm-dry conditions. Increased effective precipitation supported large pluvial lakes and long-lived spring ecosystems in valleys and basins throughout the western and southwestern U.S., but the source and seasonality of the increased precipitation are debated. Increases in the proportions of C4/(C4+ C3) grasses in the diets of large grazers have been ascribed both to increases in summer precipitation and lower atmospheric CO2 levels. Here we present stable carbon and oxygen isotope data from tooth enamel of late Pleistocene herbivores recovered from paleowetland deposits at Tule Spring Fossil Beds National Monument in the Las Vegas Valley of southern Nevada, as well as modern herbivores from the surrounding area. We use these data to investigate whether winter or summer precipitation was responsible for driving the relatively wet hydroclimate conditions that prevailed in the region during the late Pleistocene. We also evaluate whether late Pleistocene grass C4/(C4+ C3) was higher than today, and potential drivers of any changes.
Tooth enamel δ18O values for Pleistocene Equus, Bison, and Mammuthus are generally low (average 22.0 ± 0.7‰, 2 s.e., VSMOW) compared to modern equids (27.8 ± 1.5‰), and imply lower water δ18O values (−16.1 ± 0.8‰) than modern precipitation (−10.5‰) or in waters present in active springs and wells in the Las Vegas Valley (−12.9‰), an area dominated by winter precipitation. In contrast, tooth enamel of Camelops (a browser) generally yielded higher δ18O values (23.9 ± 1.1‰), possibly suggesting drought tolerance. Mean δ13C values for the Pleistocene grazers (−6.6 ± 0.7‰, 2 s.e., VPDB) are considerably higher than for modern equids (−9.6 ± 0.4‰) and indicate more consumption of C4 grass (17 ± 5%) than today (4 ± 4%). However, calculated C4 grass consumption in the late Pleistocene is strikingly lower than the proportion of C4 grass taxa currently present in the valley (55–60%). δ13C values in Camelops tooth enamel (−7.7 ± 1.0‰) are interpreted as reflecting moderate consumption (14 ± 8%) of Atriplex (saltbush), a C4 shrub that flourishes in regions with hot, dry summers.
Lower water δ18O values, lower abundance of C4 grasses, and the inferred presence of Atriplex are all consistent with general circulation models for the late Pleistocene that show enhanced delivery of winter precipitation, sourced from the north Pacific, into the interior western U.S. but do not support alternative models that infer enhanced delivery of summer precipitation, sourced from the tropics. In addition, we hypothesize that dietary competition among the diverse and abundant Pleistocene fauna may have driven the grazers analyzed here to feed preferentially on C4 grasses. Dietary partitioning, especially when combined with decreased pCO2 levels during the late Pleistocene, can explain the relatively high δ13C values observed in late Pleistocene grazers in the Las Vegas Valley and elsewhere in the southwestern U.S. without requiring additional summer precipitation. Pleistocene hydroclimate parameters derived from dietary and floral records may need to be reevaluated in the context of the potential effects of dietary preferences and lower pCO2 levels on the stability of C3 vs. C4 plants.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2022.107784","usgsCitation":"Kohn, M.J., Springer, K.B., Pigati, J.S., Reynard, L., Drewicz, A.E., Crevier, J., and Scott, E., 2022, Seasonality of precipitation in the southwestern United States during the late Pleistocene inferred from stable isotopes in herbivore tooth enamel: Quaternary Science Reviews, v. 296, 107784, 21 p., https://doi.org/10.1016/j.quascirev.2022.107784.","productDescription":"107784, 21 p.","ipdsId":"IP-141465","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":446131,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2022.107784","text":"Publisher Index Page"},{"id":435657,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DBH6V7","text":"USGS data release","linkHelpText":"Data release for Seasonality of precipitation in the southwestern United States during the late Pleistocene inferred from stable isotopes in herbivore tooth enamel"},{"id":408304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","city":"Las Vegas","otherGeospatial":"Tule Springs Fossil Beds National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.28606414794922,\n 36.33614694088851\n ],\n [\n -115.26082992553711,\n 36.33573209193246\n ],\n [\n -115.26271820068361,\n 36.35287734039248\n ],\n [\n -115.26288986206055,\n 36.37886454827749\n ],\n [\n -115.30014038085936,\n 36.37955557893362\n ],\n [\n -115.31507492065428,\n 36.38052301153702\n ],\n [\n -115.31455993652342,\n 36.42515002455931\n ],\n [\n -115.3780746459961,\n 36.42570252039198\n ],\n [\n -115.3780746459961,\n 36.417829083625676\n ],\n [\n -115.3875160217285,\n 36.4175528081898\n ],\n [\n -115.38562774658202,\n 36.46988944681576\n ],\n [\n -115.41961669921875,\n 36.46754264939998\n ],\n [\n -115.43575286865234,\n 36.46491967409621\n ],\n [\n -115.44742584228514,\n 36.46478162030615\n ],\n [\n -115.42854309082031,\n 36.43509434800219\n ],\n [\n -115.4204750061035,\n 36.42100618049883\n ],\n [\n -115.39815902709962,\n 36.40138898484862\n ],\n [\n -115.38305282592775,\n 36.401665317773336\n ],\n [\n -115.38219451904295,\n 36.39917828607653\n ],\n [\n -115.37824630737303,\n 36.39931645659218\n ],\n [\n -115.37773132324217,\n 36.39752022072802\n ],\n [\n -115.37378311157225,\n 36.39765839419146\n ],\n [\n -115.37275314331055,\n 36.39572394334955\n ],\n [\n -115.36640167236327,\n 36.39558576644709\n ],\n [\n -115.36605834960938,\n 36.39378944436183\n ],\n [\n -115.36434173583984,\n 36.393513083433376\n ],\n [\n -115.36348342895508,\n 36.391854897229315\n ],\n [\n -115.36022186279297,\n 36.391854897229315\n ],\n [\n -115.35970687866211,\n 36.39005848892647\n ],\n [\n -115.35748600959778,\n 36.39009303563144\n ],\n [\n -115.35728216171265,\n 36.39005848892647\n ],\n [\n -115.35722851753236,\n 36.38811521205252\n ],\n [\n -115.35277605056763,\n 36.3881324858387\n ],\n [\n -115.35271167755126,\n 36.38636190279206\n ],\n [\n -115.35064101219177,\n 36.38633599152129\n ],\n [\n -115.35061955451964,\n 36.38439262160467\n ],\n [\n -115.34815192222594,\n 36.384409896217875\n ],\n [\n -115.34822702407837,\n 36.3826737784023\n ],\n [\n -115.34197211265564,\n 36.38259604087706\n ],\n [\n -115.3419613838196,\n 36.38066985293378\n ],\n [\n -115.33441901206969,\n 36.38069576609268\n ],\n [\n -115.33445119857788,\n 36.37882135865761\n ],\n [\n -115.33233761787415,\n 36.37880408280295\n ],\n [\n -115.33235907554625,\n 36.37684324836339\n ],\n [\n -115.33114671707153,\n 36.37686916279726\n ],\n [\n -115.33121109008789,\n 36.37496010972552\n ],\n [\n -115.32900094985962,\n 36.37495147136984\n ],\n [\n -115.32896876335144,\n 36.37309420262354\n ],\n [\n -115.32553553581238,\n 36.3731373954236\n ],\n [\n -115.31395912170409,\n 36.366494060977395\n ],\n [\n -115.30202865600585,\n 36.356817448638495\n ],\n [\n -115.30202865600585,\n 36.35419073196714\n ],\n [\n -115.29773712158203,\n 36.35446723263293\n ],\n [\n -115.29790878295898,\n 36.3510109037209\n ],\n [\n -115.29559135437013,\n 36.35094177557743\n ],\n [\n -115.29541969299315,\n 36.349075292502064\n ],\n [\n -115.29361724853517,\n 36.34942094089192\n ],\n [\n -115.29353141784667,\n 36.34727789613907\n ],\n [\n -115.2919864654541,\n 36.34734702753563\n ],\n [\n -115.29215812683104,\n 36.34554959127678\n ],\n [\n -115.29121398925781,\n 36.3456187242078\n ],\n [\n -115.29044151306152,\n 36.34285335909331\n ],\n [\n -115.28915405273438,\n 36.342922494418\n ],\n [\n -115.28881072998045,\n 36.341124956029134\n ],\n [\n -115.28778076171875,\n 36.34133236642219\n ],\n [\n -115.28769493103029,\n 36.33974220595944\n ],\n [\n -115.2868366241455,\n 36.33974220595944\n ],\n [\n -115.28606414794922,\n 36.33614694088851\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"296","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kohn, Matthew J.","contributorId":297342,"corporation":false,"usgs":false,"family":"Kohn","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":854448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":854449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynard, Linda 0000-0001-5732-1532","orcid":"https://orcid.org/0000-0001-5732-1532","contributorId":260328,"corporation":false,"usgs":false,"family":"Reynard","given":"Linda","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":854450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drewicz, Amanda E.","contributorId":297343,"corporation":false,"usgs":false,"family":"Drewicz","given":"Amanda","email":"","middleInitial":"E.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854451,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crevier, Justin","contributorId":297344,"corporation":false,"usgs":false,"family":"Crevier","given":"Justin","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":854452,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Eric","contributorId":127422,"corporation":false,"usgs":false,"family":"Scott","given":"Eric","email":"","affiliations":[],"preferred":false,"id":854453,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237575,"text":"70237575 - 2022 - Lower seismogenic depth model of western U.S. Earthquakes","interactions":[],"lastModifiedDate":"2022-10-31T14:52:24.02545","indexId":"70237575","displayToPublicDate":"2022-10-12T13:25:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Lower seismogenic depth model of western U.S. Earthquakes","docAbstract":"We present a model of the lower seismogenic depth of earthquakes in the western United States (WUS) estimated using the hypocentral depths of events M > 1, a crustal temperature model, and historical earthquake rupture depth models. Locations of earthquakes are from the Advanced National Seismic System Comprehensive Earthquake Catalog from 1980 to 2021 supplemented with seismicity in southern California for event hypocenters that were relocated by Hauksson et al. (2012) to obtain higher precision and better resolution in the model. We calculated the average depth of the deepest 10% of the merged catalog using an adaptive radius of 50 km or more. Along the San Andreas fault, the deepest seismogenic depths are located at 23 km around the Cholame segment, whereas the shallowest depths are located at about 10 km along the Rodgers Creek and Maacama faults. For the WUS outside California, the depth generally varies between 10 and 25 km with an average around 14 km but could extend to 35 km along Cascadia subduction zone. We find good agreement between the small‐magnitude depths and rupture depths derived from coseismic slip of large earthquakes across the region. Our estimates are generally deeper than the previous seismogenic depths determined for the Uniform California Earthquake Rupture Forecast, Version 3 model based on work by Petersen et al. (1996) who used seismicity cross sections along major fault zones in California. Our new seismogenic depth distribution correlates closely with crustal temperature derived from WUS heat flow (Blackwell et al., 2011). This correlation allowed us to develop a map of the brittle–ductile transition that we use to replace seismogenic depths in the model east of the Intermountain West Seismic Belt where the seismicity rate is low. This updated depth model is useful for recalibrating the lower geologic fault rupture depths, and constraining deformation and seismicity source models in updates of the U.S. Geological Survey National Seismic Hazard Model.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220174","usgsCitation":"Zeng, Y., Petersen, M.D., and Boyd, O.S., 2022, Lower seismogenic depth model of western U.S. Earthquakes: Seismological Research Letters, v. 93, no. 6, p. 3186-3204, https://doi.org/10.1785/0220220174.","productDescription":"19 p.","startPage":"3186","endPage":"3204","ipdsId":"IP-142152","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":408265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203125,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 29.84064389983441\n ],\n [\n -103.35937499999999,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 48.69096039092549\n ],\n [\n -125.33203125,\n 29.84064389983441\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Petersen, Mark D. 0000-0001-8542-3990 mpetersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8542-3990","contributorId":1163,"corporation":false,"usgs":true,"family":"Petersen","given":"Mark","email":"mpetersen@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854485,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyd, Oliver S. 0000-0001-9457-0407 olboyd@usgs.gov","orcid":"https://orcid.org/0000-0001-9457-0407","contributorId":140739,"corporation":false,"usgs":true,"family":"Boyd","given":"Oliver","email":"olboyd@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":854486,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237388,"text":"70237388 - 2022 - Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","interactions":[],"lastModifiedDate":"2022-10-17T16:42:25.152014","indexId":"70237388","displayToPublicDate":"2022-10-12T09:07:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats","docAbstract":"This study investigates the applicability of the Landsat Dynamic Surface Water Extent (DSWE) science product for waterbird habitat modeling in multiple non-canopied habitat types. We compare surface water distribution estimates derived from DSWE to two site-specific survey methods: visual surveys and digitized aerial imagery. These site-specific surveys were conducted on Poplar Island, a restoration island project in the Chesapeake Bay, USA. Visual surveys were collected bimonthly from 2006 – 2013, and digitized aerial imagery was collected annually from 2006 – 2015. As a restoration island, Poplar Island presents a unique opportunity to analyze DSWE in a rapidly changing site. We structure our analysis based on the procedural development of individual sub-island cells developed from unconsolidated dredge material into fully restored wetlands that have independent hydrologic connection to the surrounding bay. Each development status is analyzed using our three DSWE classifications: Open Water (OW), a conservative estimate; Wetland Inclusive (WI), an aggressive estimate; and Development Dependent (DD), a landcover adaptive estimate. The OW classification consistently underestimates surface water coverage especially in the more complex, fully developed cells. The WI classification is better able to capture the tidal channels in these cells, but marginally overestimates surface water coverage in more sparsely vegetated cells. The DD classification does not significantly improve upon the estimations of the WI classification. Our data indicate that DSWE can be a capable alternative to our site-specific survey methods. However, the product is limited by Landsat’s 30 m spatial resolution, especially in more structurally complex wetlands. A recommended classification method for characterizing waterbird habitats would depend on the goals and targeted scale of analysis, for which DSWE may be a viable option.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2022.100845","usgsCitation":"Taylor, J., Sullivan, J.D., Teitelbaum, C.S., Reese, J.G., and Prosser, D., 2022, Comparing Landsat Dynamic Surface Water Extent to alternative methods of measuring inundation in developing waterbird habitats: Remote Sensing Applications: Society and Environment, v. 28, 100845, 9 p., https://doi.org/10.1016/j.rsase.2022.100845.","productDescription":"100845, 9 p.","ipdsId":"IP-139932","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2022.100845","text":"Publisher Index Page"},{"id":435658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SW505K","text":"USGS data release","linkHelpText":"Surface water estimates for a complex study site derived from traditional and emerging methods"},{"id":408211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Poplar Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.36236190795898,\n 38.74631848708898\n ],\n [\n -76.36373519897461,\n 38.754886481591335\n ],\n [\n -76.36905670166014,\n 38.7564928660758\n ],\n [\n -76.37231826782227,\n 38.754886481591335\n ],\n [\n -76.37884140014648,\n 38.762114927054405\n ],\n [\n -76.37969970703125,\n 38.76519348705099\n ],\n [\n -76.37712478637695,\n 38.76827191422227\n ],\n [\n -76.36356353759766,\n 38.78165483591876\n ],\n [\n -76.35824203491211,\n 38.78580303175893\n ],\n [\n -76.35772705078125,\n 38.793295935073516\n ],\n [\n -76.36510848999022,\n 38.797577240505625\n ],\n [\n -76.36974334716797,\n 38.79650693825768\n ],\n [\n -76.37042999267578,\n 38.79102138696157\n ],\n [\n -76.37128829956055,\n 38.78807774637309\n ],\n [\n -76.37060165405273,\n 38.784331118923134\n ],\n [\n -76.37334823608398,\n 38.78272536117434\n ],\n [\n -76.37695312499999,\n 38.78339443129763\n ],\n [\n -76.38141632080078,\n 38.78138720209366\n ],\n [\n -76.39291763305664,\n 38.767201172010736\n ],\n [\n -76.39120101928711,\n 38.75582354360097\n ],\n [\n -76.38656616210938,\n 38.7500671112174\n ],\n [\n -76.38364791870117,\n 38.743239112884744\n ],\n [\n -76.37678146362305,\n 38.741766321754575\n ],\n [\n -76.36236190795898,\n 38.74631848708898\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, John B.","contributorId":296294,"corporation":false,"usgs":false,"family":"Taylor","given":"John B.","affiliations":[{"id":64012,"text":"Chesapeake Bay Trust","active":true,"usgs":false}],"preferred":false,"id":854370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":854371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teitelbaum, Claire S. 0000-0001-5646-3184","orcid":"https://orcid.org/0000-0001-5646-3184","contributorId":255382,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire","email":"","middleInitial":"S.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":854372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reese, Jan G.","contributorId":296295,"corporation":false,"usgs":false,"family":"Reese","given":"Jan","email":"","middleInitial":"G.","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":854373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854374,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237375,"text":"70237375 - 2022 - Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","interactions":[],"lastModifiedDate":"2022-10-12T14:07:03.951041","indexId":"70237375","displayToPublicDate":"2022-10-12T08:55:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions","docAbstract":"Aim: Anticipating when and where changes in species' demographic rates will lead to range shifts in response to changing climate remains a major challenge. Despite evidence of increasing mortality in dry forests across the globe in response to drought and warming temperatures, the overall impacts on the distribution of dry forests are largely unknown because we lack comparable large-scale data on tree recruitment rates. Here, our aim was to develop range-wide population models for dry forest tree species (pinyon pine and juniper), quantifying both mortality and recruitment, to better understand where and under what conditions species range contractions are occurring.
Location: Western United States.
Major taxa studied: Two pinyon pine (Pinus spp.) and three juniper (Juniperus spp.) species.
Methods: We developed range-wide demographic models for five species using forest inventory data from across the western United States and estimated population trends and climate vulnerability.
Results: We find that four of the five species are declining in parts of their range, with Pinus edulis having the largest proportion of populations declining (24%). Population vulnerability increases with aridity and temperature, with up to ~50% of populations declining in the warmest and driest conditions. Mortality and recruitment were both essential to explaining where populations are declining.
Main conclusions: Our results suggest that dry forest species are undergoing an active range shift driven by both changing recruitment and mortality, and that increasing temperatures and drought threaten the long-term viability of many of these species in their current range. While four of the five species examined were experiencing some declines, P. edulis is currently most vulnerable. Management actions such as reducing tree density may be able to mitigate some of these impacts. The framework we present to estimate range-wide demographic rates can be applied to other species to determine where range contractions are most likely.
","language":"English","publisher":"Wiley","doi":"10.1111/geb.13582","usgsCitation":"Shriver, R., Yackulic, C., Bell, D.M., and Bradford, J., 2022, Dry forest decline is driven by both declining recruitment and increasing mortality in response to warm, dry conditions: Global Ecology and Biogeography, v. 31, no. 11, p. 2259-2269, https://doi.org/10.1111/geb.13582.","productDescription":"11 p.","startPage":"2259","endPage":"2269","ipdsId":"IP-143036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":435659,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FIGKFM","text":"USGS data release","linkHelpText":"Pinyon-juniper basal area, climate and demographics data from National Forest Inventory plots and projected under future density and climate conditions"},{"id":408210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Idaho, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Utah, Washington, Wyoming","otherGeospatial":"Great Basin, Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.5751953125,\n 49.03786794532644\n ],\n [\n -119.64111328125,\n 48.38544219115483\n ],\n [\n -118.63037109375,\n 47.79839667295524\n ],\n [\n -117.44384765625,\n 47.78363463526376\n ],\n [\n -116.806640625,\n 47.14489748555398\n ],\n [\n -117.02636718749999,\n 46.118941506107056\n ],\n [\n -116.56494140625001,\n 45.69083283645816\n ],\n [\n -116.91650390625,\n 44.91813929958515\n ],\n [\n -117.1142578125,\n 44.69989765840318\n ],\n [\n -117.18017578125,\n 44.29240108529005\n ],\n [\n -116.45507812500001,\n 43.77109381775651\n ],\n [\n -115.59814453125001,\n 43.18114705939968\n ],\n [\n -112.96142578125,\n 42.13082130188811\n ],\n [\n -111.20361328125,\n 42.032974332441405\n ],\n [\n -111.29150390625,\n 40.730608477796636\n ],\n [\n -112.08251953125,\n 40.111688665595956\n ],\n [\n -112.30224609374999,\n 39.01064750994083\n ],\n [\n -112.54394531249999,\n 38.13455657705411\n ],\n [\n -111.46728515624999,\n 37.996162679728116\n ],\n [\n -110.23681640625,\n 38.993572058209466\n ],\n [\n -110.06103515625,\n 40.43022363450862\n ],\n [\n -108.984375,\n 40.38002840251183\n ],\n [\n -108.78662109375,\n 38.94232097947902\n ],\n [\n -108.19335937499999,\n 37.33522435930639\n ],\n [\n -106.72119140625,\n 37.00255267215955\n ],\n [\n -106.67724609375,\n 36.54494944148322\n ],\n [\n -106.94091796875,\n 35.746512259918504\n ],\n [\n -106.61132812499999,\n 34.831841149828655\n ],\n [\n -106.3916015625,\n 33.669496972795535\n ],\n [\n -106.6552734375,\n 33.119150226768866\n ],\n [\n -106.5673828125,\n 31.50362930577303\n ],\n [\n -105.16113281249999,\n 30.675715404167743\n ],\n [\n -104.6337890625,\n 29.458731185355344\n ],\n [\n -103.0517578125,\n 28.806173508854776\n ],\n [\n -102.26074218749999,\n 29.649868677972304\n ],\n [\n -101.42578124999999,\n 29.649868677972304\n ],\n [\n -99.36035156249999,\n 32.65787573695528\n ],\n [\n -97.6025390625,\n 33.7243396617476\n ],\n [\n -97.2509765625,\n 34.84987503195418\n ],\n [\n -96.8994140625,\n 37.23032838760387\n ],\n [\n -96.591796875,\n 38.89103282648846\n ],\n [\n -96.94335937499999,\n 41.27780646738183\n ],\n [\n -96.8994140625,\n 42.4234565179383\n ],\n [\n -98.5693359375,\n 43.54854811091286\n ],\n [\n -98.7890625,\n 44.37098696297173\n ],\n [\n -98.87695312499999,\n 47.39834920035926\n ],\n [\n -100.37109375,\n 47.81315451752768\n ],\n [\n -101.9970703125,\n 49.03786794532644\n ],\n [\n -119.5751953125,\n 49.03786794532644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Shriver, Robert K.","contributorId":297511,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert K.","affiliations":[{"id":64419,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno; Ecology, Evolution, and Conservation Biology Graduate Program, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":854333,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, David M.","contributorId":191003,"corporation":false,"usgs":false,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":854336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269055,"text":"70269055 - 2022 - Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","interactions":[],"lastModifiedDate":"2025-07-15T15:43:20.638903","indexId":"70269055","displayToPublicDate":"2022-10-12T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model","docAbstract":"It has been nearly a decade since updates to seismic and fault sources in the central and eastern United States (CEUS) were last assessed for the 2012 Central and Eastern United States Seismic Source Characterization for nuclear facilities (CEUS-SSCn) and 2014 United States Geological Survey National Seismic Hazard Model (NSHM) for the conterminous U.S. In advance of the 2023 NSHM update, we created 3 related geospatial databases to summarize and characterize new fault source information for the CEUS. These include fault section, fault-zone polygon, and earthquake geology (fault slip rate, earthquake recurrence intervals) databases which document updates to fault parameters used in prior seismic hazard models in this region. The 2012 CEUS-SSCn and 2014 NSHM fault models served as a foundation, as we revised and added fault sources where new published studies documented significant changes to our understanding of fault location, geometry, or activity. We added 9 new fault sections that meet the criteria of (1) a length ≥7 km, (2) evidence of recurrent Quaternary tectonic activity, and (3) documentation that is publicly available in a peer-reviewed source. The prior CEUS models only included 6 fault sections (sources) and 10 fault-zone polygons (previously called repeating large magnitude earthquake (RLME) polygons). The revised databases include 15 fault sections and 10 fault zone polygons. Updates to the faults constitute a 150% increase in fault sections, but no change in the number of fault-zone polygons, although some fault-zone polygons differ from RLME polygons used in prior models. No faults were removed from past models. Several seismic zones and suspected faults were evaluated but not included in this update due to a lack of information about fault location, geometry, or recurrent Quaternary activity. These updates to the fault sections, fault-zone polygons, and earthquake geology databases will inform fault geometry and activity rates of CEUS sources during the 2023 NSHM implementation.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220162","usgsCitation":"Jobe, J.A., Hatem, A.E., Gold, R.D., DuRoss, C., Reitman, N.G., Briggs, R.W., and Collett, C.M., 2022, Revised earthquake geology inputs for the central and eastern United States and southeast Canada for the 2023 National Seismic Hazard Model: Seismological Research Letters, v. 93, no. 6, p. 3100-3120, https://doi.org/10.1785/0220220162.","productDescription":"21 p.","startPage":"3100","endPage":"3120","ipdsId":"IP-138939","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.33762311995545,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 25.297267313035647\n ],\n [\n -66.17641020136095,\n 51.85857884546667\n ],\n [\n -104.33762311995545,\n 51.85857884546667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatem, Alexandra Elise 0000-0001-7584-2235","orcid":"https://orcid.org/0000-0001-7584-2235","contributorId":225597,"corporation":false,"usgs":true,"family":"Hatem","given":"Alexandra","email":"","middleInitial":"Elise","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reitman, Nadine G. 0000-0002-6730-2682 nreitman@usgs.gov","orcid":"https://orcid.org/0000-0002-6730-2682","contributorId":5816,"corporation":false,"usgs":true,"family":"Reitman","given":"Nadine","email":"nreitman@usgs.gov","middleInitial":"G.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Collett, Camille Marie 0000-0003-4836-0243","orcid":"https://orcid.org/0000-0003-4836-0243","contributorId":257819,"corporation":false,"usgs":true,"family":"Collett","given":"Camille","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943167,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237376,"text":"70237376 - 2022 - Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","interactions":[],"lastModifiedDate":"2022-10-11T19:08:25.114099","indexId":"70237376","displayToPublicDate":"2022-10-11T14:04:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","title":"Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering","docAbstract":"Discovery of hidden geothermal resources is challenging. It requires the mining of large datasets with diverse data attributes representing subsurface hydrogeological and geothermal conditions. The commonly used play fairway analysis approach typically incorporates subject-matter expertise to analyze regional data to estimate geothermal characteristics and favorability. We demonstrate an alternative approach based on machine learning (ML) to process a geothermal dataset from southwest New Mexico (SWNM). The study region includes low- and medium-temperature hydrothermal systems. Several of these systems are not well characterized because of insufficient existing data and limited past explorative work. This study discovers hidden patterns and relations in the SWNM geothermal dataset to improve our understanding of the regional hydrothermal conditions and energy-production favorability. This understanding is obtained by applying an unsupervised ML algorithm based on non-negative matrix factorization coupled with customized k-means clustering (NMFk). NMFk can automatically identify (1) hidden signatures characterizing analyzed datasets, (2) the optimal number of these signatures, (3) the dominant data attributes associated with each signature, and (4) the spatial distribution of the extracted signatures. Here, NMFk is applied to analyze 18 geological, geophysical, hydrogeological, and geothermal attributes at 44 locations in SWNM. Using NMFk, we find data patterns and identify the spatial associations of hydrothermal signatures within two physiographic provinces (Colorado Plateau and Basin and Range) and two sub-regions of these provinces (the Mogollon-Datil volcanic field and the Rio Grande rift) in SWNM. The ML algorithm extracted five hydrothermal signatures in the SWNM datasets that differentiate between low (<90) and medium (90-150)-temperature hydrothermal systems. The algorithm also suggests that the Rio Grande rift and northern Mogollon-Datil volcanic field are the most favorable regions for future geothermal resource discovery. NMFk also identified critical attributes to identify medium-temperature hydrothermal systems in the study area. The resulting NMFk model can be applied to predict geothermal conditions and their uncertainties at new SWNM locations based on limited data from unexplored regions. The code to execute the performed analyses as well as the corresponding data can be found at https://github.com/SmartTensors/GeoThermalCloud.jl.","language":"English","publisher":"Elsevier","doi":"10.1016/j.geothermics.2022.102576","usgsCitation":"Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E., Siler, D.L., Karra, S., and Middleton, R.S., 2022, Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering: Geothermics, v. 106, 102576, 15 p., https://doi.org/10.1016/j.geothermics.2022.102576.","productDescription":"102576, 15 p.","ipdsId":"IP-132590","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1890937","text":"Publisher Index Page"},{"id":408181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Colorado Plateau, Gila Hot Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.05029296875,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 32.008075959291055\n ],\n [\n -106.094970703125,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 35.69299463209881\n ],\n [\n -109.05029296875,\n 32.008075959291055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":854338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":854339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karra, Satish","contributorId":297512,"corporation":false,"usgs":false,"family":"Karra","given":"Satish","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":854343,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middleton, Richard S.","contributorId":297513,"corporation":false,"usgs":false,"family":"Middleton","given":"Richard","email":"","middleInitial":"S.","affiliations":[{"id":64420,"text":"Carbon Solutions LLC","active":true,"usgs":false}],"preferred":false,"id":854344,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237354,"text":"70237354 - 2022 - Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling","interactions":[],"lastModifiedDate":"2022-10-12T15:04:06.279175","indexId":"70237354","displayToPublicDate":"2022-10-11T12:34:41","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"17","title":"Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling","docAbstract":"This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. The temperature of water in a lake is a fundamental driver of lake biogeochemical processes, and it controls the growth, survival, and reproduction of fishes in the lake.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-17","usgsCitation":"Daw, A., Thomas, R.Q., Carey, C.C., Read, J., Appling, A.P., and Karpatne, A., 2022, Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling, chap. 17 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 399-416, https://doi.org/10.1201/9781003143376-17.","productDescription":"18 p.","startPage":"399","endPage":"416","ipdsId":"IP-131612","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408172,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, Wisconsin","otherGeospatial":"Falling Creek Reservoir, Lake Mendota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.38390731811523,\n 43.08042388708437\n ],\n [\n -89.37772750854492,\n 43.08594039080513\n ],\n [\n -89.37549591064453,\n 43.086817970597735\n ],\n [\n -89.37240600585938,\n 43.09270996646064\n ],\n [\n -89.37017440795898,\n 43.09358744926521\n ],\n [\n -89.3686294555664,\n 43.09722260123252\n ],\n [\n -89.36931610107422,\n 43.09822536380049\n ],\n [\n -89.36914443969725,\n 43.10023083967166\n ],\n [\n -89.36553955078124,\n 43.10474292021466\n ],\n [\n -89.3679428100586,\n 43.10649752839693\n ],\n [\n -89.37000274658203,\n 43.111761051190655\n ],\n [\n -89.37463760375977,\n 43.11401670811261\n ],\n [\n -89.37875747680664,\n 43.11376608367126\n ],\n [\n -89.3818473815918,\n 43.115019195614124\n ],\n [\n -89.37446594238281,\n 43.1240408441236\n ],\n [\n -89.37721252441406,\n 43.12830060463496\n ],\n [\n -89.38236236572266,\n 43.13055565187361\n ],\n [\n -89.38871383666992,\n 43.13068092983799\n ],\n [\n -89.39918518066406,\n 43.12830060463496\n ],\n [\n -89.40347671508789,\n 43.129302858116375\n ],\n [\n -89.40845489501953,\n 43.13080620754577\n ],\n [\n -89.40742492675781,\n 43.13418860865851\n ],\n [\n -89.40793991088866,\n 43.140451820450636\n ],\n [\n -89.40999984741211,\n 43.14220540479065\n ],\n [\n -89.40605163574219,\n 43.1450861841603\n ],\n [\n -89.40587997436523,\n 43.148968758958794\n ],\n [\n -89.40656661987305,\n 43.149594957605004\n ],\n [\n -89.40570831298828,\n 43.15059686209487\n ],\n [\n -89.4012451171875,\n 43.149469718389014\n ],\n [\n -89.40193176269531,\n 43.15097257204435\n ],\n [\n -89.40845489501953,\n 43.15322678324046\n ],\n [\n -89.41068649291992,\n 43.15172398501466\n ],\n [\n -89.40999984741211,\n 43.147591099354806\n ],\n [\n -89.41343307495117,\n 43.147841585228726\n ],\n [\n -89.41823959350586,\n 43.147591099354806\n ],\n [\n -89.4257926940918,\n 43.1430821780996\n ],\n [\n -89.43008422851562,\n 43.14320743040299\n ],\n [\n -89.43403244018555,\n 43.14345793423997\n ],\n [\n -89.43592071533202,\n 43.14746585603295\n ],\n [\n -89.43815231323242,\n 43.14658914559456\n ],\n [\n -89.43815231323242,\n 43.14082759275761\n ],\n [\n -89.42768096923827,\n 43.139575009419644\n ],\n [\n -89.43334579467773,\n 43.13406333787913\n ],\n [\n -89.43643569946289,\n 43.12779947173576\n ],\n [\n -89.44192886352539,\n 43.12253732841481\n ],\n [\n -89.44244384765625,\n 43.12003138678775\n ],\n [\n -89.4477653503418,\n 43.117149427027904\n ],\n [\n -89.45171356201172,\n 43.113264831709394\n ],\n [\n -89.45428848266602,\n 43.11063319155333\n ],\n [\n -89.45943832397461,\n 43.1111344650692\n ],\n [\n -89.46699142456055,\n 43.10912934637269\n ],\n [\n -89.47162628173828,\n 43.109505311131876\n ],\n [\n -89.47763442993164,\n 43.107374813628546\n ],\n [\n -89.47917938232422,\n 43.10612154516305\n ],\n [\n -89.48347091674805,\n 43.10587088839085\n ],\n [\n -89.48484420776367,\n 43.10211091364412\n ],\n [\n -89.48484420776367,\n 43.09471562296844\n ],\n [\n -89.48209762573242,\n 43.09108032218198\n ],\n [\n -89.47437286376952,\n 43.08656723479702\n ],\n [\n -89.47282791137695,\n 43.084185193502705\n ],\n [\n -89.47128295898438,\n 43.08293145035436\n ],\n [\n -89.4707679748535,\n 43.07992236211469\n ],\n [\n -89.46784973144531,\n 43.08117616684163\n ],\n [\n -89.4613265991211,\n 43.07979698023082\n ],\n [\n -89.4532585144043,\n 43.082304569158524\n ],\n [\n -89.44570541381836,\n 43.086817970597735\n ],\n [\n -89.43849563598633,\n 43.088823820055715\n ],\n [\n -89.4319725036621,\n 43.09158175580929\n ],\n [\n -89.4257926940918,\n 43.0870687053721\n ],\n [\n -89.42253112792969,\n 43.08694333811321\n ],\n [\n -89.42665100097656,\n 43.08594039080513\n ],\n [\n -89.42785263061523,\n 43.081552303256444\n ],\n [\n -89.42235946655273,\n 43.0770385138722\n ],\n [\n -89.41909790039062,\n 43.07841776257431\n ],\n [\n -89.41171646118164,\n 43.077916221183784\n ],\n [\n -89.39626693725586,\n 43.07641157238099\n ],\n [\n -89.38802719116211,\n 43.0796715980904\n ],\n [\n -89.38390731811523,\n 43.08042388708437\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.47116565704346,\n 37.46021149840544\n ],\n [\n -77.4715518951416,\n 37.46075653907328\n ],\n [\n -77.47017860412598,\n 37.46147189892073\n ],\n [\n -77.46747493743896,\n 37.46232350886342\n ],\n [\n -77.46653079986572,\n 37.46228944465196\n ],\n [\n -77.46644496917725,\n 37.461778479617465\n ],\n [\n -77.46532917022705,\n 37.461540028073095\n ],\n [\n -77.46481418609619,\n 37.46266415012469\n ],\n [\n -77.46575832366943,\n 37.46375419173192\n ],\n [\n -77.46670246124268,\n 37.46395857276409\n ],\n [\n -77.4677324295044,\n 37.465252972993774\n ],\n [\n -77.46811866760254,\n 37.46470796510214\n ],\n [\n -77.46798992156982,\n 37.46375419173192\n ],\n [\n -77.47022151947021,\n 37.462970725932955\n ],\n [\n -77.47223854064941,\n 37.462119123361774\n ],\n [\n -77.47288227081299,\n 37.46150596350467\n ],\n [\n -77.4732255935669,\n 37.46062027927878\n ],\n [\n -77.47421264648438,\n 37.460075237617744\n ],\n [\n -77.4744701385498,\n 37.46072247414792\n ],\n [\n -77.47661590576172,\n 37.46116531696699\n ],\n [\n -77.4794054031372,\n 37.45915547080624\n ],\n [\n -77.47944831848145,\n 37.45963238834291\n ],\n [\n -77.48022079467772,\n 37.46062027927878\n ],\n [\n -77.48043537139893,\n 37.46188067290371\n ],\n [\n -77.48095035552979,\n 37.46171035068239\n ],\n [\n -77.48215198516846,\n 37.46239163723983\n ],\n [\n -77.48219490051268,\n 37.46188067290371\n ],\n [\n -77.48185157775877,\n 37.46143783432129\n ],\n [\n -77.48258113861084,\n 37.46119938169058\n ],\n [\n -77.48373985290527,\n 37.46000710713079\n ],\n [\n -77.48369693756104,\n 37.459019208094254\n ],\n [\n -77.48305320739746,\n 37.45786096501728\n ],\n [\n -77.48416900634764,\n 37.45850822071285\n ],\n [\n -77.4845552444458,\n 37.459257667677306\n ],\n [\n -77.48644351959227,\n 37.45912140515152\n ],\n [\n -77.48687267303467,\n 37.45803129600663\n ],\n [\n -77.4869155883789,\n 37.45734996972154\n ],\n [\n -77.48867511749268,\n 37.45612356676599\n ],\n [\n -77.48867511749268,\n 37.4557147613116\n ],\n [\n -77.48957633972168,\n 37.455851030044684\n ],\n [\n -77.49056339263916,\n 37.456362035582444\n ],\n [\n -77.49159336090088,\n 37.456396102494146\n ],\n [\n -77.49253749847412,\n 37.45561255959884\n ],\n [\n -77.4929666519165,\n 37.45581696288469\n ],\n [\n -77.49300956726074,\n 37.45632796865522\n ],\n [\n -77.49193668365479,\n 37.45731590324433\n ],\n [\n -77.49236583709715,\n 37.457452169060026\n ],\n [\n -77.49335289001463,\n 37.45697523761887\n ],\n [\n -77.49378204345703,\n 37.457554368258826\n ],\n [\n -77.49429702758789,\n 37.45721370371964\n ],\n [\n -77.49403953552246,\n 37.456532369985794\n ],\n [\n -77.4940824508667,\n 37.45595323143157\n ],\n [\n -77.49309539794922,\n 37.45476087322793\n ],\n [\n -77.49232292175293,\n 37.45459053478991\n ],\n [\n -77.49210834503174,\n 37.45510154894013\n ],\n [\n -77.49103546142578,\n 37.455578492330226\n ],\n [\n -77.4907350540161,\n 37.455578492330226\n ],\n [\n -77.4907350540161,\n 37.45510154894013\n ],\n [\n -77.49017715454102,\n 37.45448833154089\n ],\n [\n -77.48876094818114,\n 37.45448833154089\n ],\n [\n -77.48816013336182,\n 37.45523781879053\n ],\n [\n -77.4857997894287,\n 37.456532369985794\n ],\n [\n -77.48554229736328,\n 37.457724699946496\n ],\n [\n -77.48507022857666,\n 37.45786096501728\n ],\n [\n -77.48472690582275,\n 37.457452169060026\n ],\n [\n -77.4848985671997,\n 37.45694117097097\n ],\n [\n -77.4848985671997,\n 37.456668637229136\n ],\n [\n -77.4842119216919,\n 37.45680490422421\n ],\n [\n -77.48335361480713,\n 37.45643016939033\n ],\n [\n -77.48146533966064,\n 37.456702704001174\n ],\n [\n -77.48082160949707,\n 37.45734996972154\n ],\n [\n -77.48099327087402,\n 37.45789503124621\n ],\n [\n -77.48180866241455,\n 37.45850822071285\n ],\n [\n -77.4819803237915,\n 37.459973041864046\n ],\n [\n -77.48129367828369,\n 37.45915547080624\n ],\n [\n -77.48090744018555,\n 37.458440088798184\n ],\n [\n -77.48039245605469,\n 37.45775876623748\n ],\n [\n -77.47974872589111,\n 37.45765656731798\n ],\n [\n -77.47880458831787,\n 37.45789503124621\n ],\n [\n -77.47841835021973,\n 37.45833789080978\n ],\n [\n -77.47811794281006,\n 37.45799722983981\n ],\n [\n -77.47747421264648,\n 37.45874668192534\n ],\n [\n -77.47708797454834,\n 37.45915547080624\n ],\n [\n -77.47537136077881,\n 37.45912140515152\n ],\n [\n -77.47464179992676,\n 37.45813349441403\n ],\n [\n -77.47318267822266,\n 37.45809942829375\n ],\n [\n -77.4724531173706,\n 37.458303824782625\n ],\n [\n -77.47253894805908,\n 37.45884887935506\n ],\n [\n -77.47172355651855,\n 37.45939392995478\n ],\n [\n -77.47146606445312,\n 37.45980271529748\n ],\n [\n -77.47116565704346,\n 37.46021149840544\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daw, Arka","contributorId":297446,"corporation":false,"usgs":false,"family":"Daw","given":"Arka","email":"","affiliations":[{"id":64394,"text":"Department of Computer Science, Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":854242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thomas, R. Quinn","contributorId":242825,"corporation":false,"usgs":false,"family":"Thomas","given":"R.","email":"","middleInitial":"Quinn","affiliations":[{"id":48537,"text":"Assistant Professor, Forest Resources & Environmental Conservation, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":854244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854245,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854246,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854247,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237341,"text":"70237341 - 2022 - Physics-guided neural networks (PGNN): An application in lake temperature modeling","interactions":[],"lastModifiedDate":"2022-10-12T14:57:02.942819","indexId":"70237341","displayToPublicDate":"2022-10-11T12:22:13","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"15","title":"Physics-guided neural networks (PGNN): An application in lake temperature modeling","docAbstract":"This chapter introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science has become an indispensable tool for knowledge discovery in the era of big data, as the volume of data continues to explode in practically every research domain. Recent advances in data science such as deep learning have been immensely successful in transforming the state-of-the-art in a number of commercial and industrial applications such as natural language translation and image classification, using billions or even trillions of data samples. Accurate water temperatures are critical to understanding contemporary change, and for predicting future thermal habitat of economically valuable fish.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-15","usgsCitation":"Daw, A., Karpatne, A., Watkins, W., Read, J., and Kumar, V., 2022, Physics-guided neural networks (PGNN): An application in lake temperature modeling, chap. 15 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 353-372, https://doi.org/10.1201/9781003143376-15.","productDescription":"20 p.","startPage":"353","endPage":"372","ipdsId":"IP-132785","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":446159,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1201/9781003143376-15","text":"External Repository"},{"id":408170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daw, Arka","contributorId":297446,"corporation":false,"usgs":false,"family":"Daw","given":"Arka","email":"","affiliations":[{"id":64394,"text":"Department of Computer Science, Virginia Tech.","active":true,"usgs":false}],"preferred":false,"id":854191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watkins, William 0000-0002-7544-0700 wwatkins@usgs.gov","orcid":"https://orcid.org/0000-0002-7544-0700","contributorId":178146,"corporation":false,"usgs":true,"family":"Watkins","given":"William","email":"wwatkins@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854193,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854194,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854195,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237336,"text":"70237336 - 2022 - Physics-guided recurrent neural networks for predicting lake water temperature","interactions":[],"lastModifiedDate":"2022-10-12T15:25:40.706808","indexId":"70237336","displayToPublicDate":"2022-10-11T12:12:46","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"16","title":"Physics-guided recurrent neural networks for predicting lake water temperature","docAbstract":"This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9781003143376-16","usgsCitation":"Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J.A., Steinbach, M., and Kumar, V., 2022, Physics-guided recurrent neural networks for predicting lake water temperature, chap. 16 of Knowledge-guided machine learning: Accelerating discovery using scientific knowledge and data, p. 373-398, https://doi.org/10.1201/9781003143376-16.","productDescription":"26 p.","startPage":"373","endPage":"398","ipdsId":"IP-132700","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":408169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854186,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237346,"text":"70237346 - 2022 - Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)","interactions":[],"lastModifiedDate":"2022-10-11T16:08:12.135327","indexId":"70237346","displayToPublicDate":"2022-10-11T11:00:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12625,"text":"Limnology & Oceanography: Letters","active":true,"publicationSubtype":{"id":10}},"title":"Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020)","docAbstract":"The dataset described here includes estimates of historical (1980–2020) daily surface water temperature, lake metadata, and daily weather conditions for lakes bigger than 4 ha in the conterminous United States (n = 185,549), and also in situ temperature observations for a subset of lakes (n = 12,227). Estimates were generated using a long short-term memory deep learning model and compared to existing process-based and linear regression models. Model training was optimized for prediction on unmonitored lakes through cross-validation that held out lakes to assess generalizability and estimate error. On the held-out lakes with in situ observations, median lake-specific error was 1.24°C, and the overall root mean squared error was 1.61°C. This dataset increases the number of lakes with daily temperature predictions when compared to existing datasets, as well as substantially improves predictive accuracy compared to a prior empirical model and a debiased process-based approach (2.01°C and 1.79°C median error, respectively).
","language":"English","publisher":"Wiley","doi":"10.1002/lol2.10249","usgsCitation":"Willard, J.D., Read, J., Topp, S.N., Hansen, G., and Kumar, V., 2022, Daily surface temperatures for 185,549 lakes in the conterminous United States estimated using deep learning (1980–2020): Limnology & Oceanography: Letters, v. 7, no. 4, p. 287-301, https://doi.org/10.1002/lol2.10249.","productDescription":"15 p.","startPage":"287","endPage":"301","ipdsId":"IP-127157","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":446163,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10249","text":"Publisher Index Page"},{"id":435660,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CEMS0M","text":"USGS data release","linkHelpText":"Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020)"},{"id":408163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"7","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Jared D. 0000-0003-4434-051X","orcid":"https://orcid.org/0000-0003-4434-051X","contributorId":297456,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","email":"","middleInitial":"D.","affiliations":[{"id":64397,"text":"University of Minnesota Department of Computer Science","active":true,"usgs":false}],"preferred":false,"id":854206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Topp, Simon Nemer 0000-0001-7741-5982","orcid":"https://orcid.org/0000-0001-7741-5982","contributorId":268229,"corporation":false,"usgs":true,"family":"Topp","given":"Simon","email":"","middleInitial":"Nemer","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Gretchen J. A.","contributorId":174557,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":27469,"text":"Wisconsin Department of Natural Resources, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":854209,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854210,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240887,"text":"70240887 - 2022 - Decision support for aquatic restoration based on species-specific responses to disturbance","interactions":[],"lastModifiedDate":"2023-02-28T13:05:03.312497","indexId":"70240887","displayToPublicDate":"2022-10-11T07:02:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Decision support for aquatic restoration based on species-specific responses to disturbance","docAbstract":"Disturbances to aquatic habitats are not uniformly distributed within the Great Lakes and acute effects can be strongest in nearshore areas where both landscape and within lake effects can have strong influence. Furthermore, different fish species respond to disturbances in different ways. A means to identify and evaluate locations and extent of disturbances that affect fish is needed throughout the Great Lakes. We used partial Canonical Correspondence Analysis to separate “natural” effects on nearshore assemblages from disturbance effects. Species-specific quadratic models of fish abundance as functions of in-lake disturbance or watershed-derived disturbance were developed separately for each of 35 species and lakewide predictions mapped for Lake Erie. Most responses were unimodal and more species decreased in abundance with increasing watershed disturbance than increased. However, eight species increased in abundance with current in-lake disturbance conditions. Optimum Yellow Perch (Perca flavescens) abundance occurred at in-lake disturbance values less than the gradient mean, but decreased continuously from minimum watershed disturbance to higher values. Bands of optimum in-lake conditions occurred throughout the eastern and western portions of the Lake Erie nearshore zone; some areas were less disturbed than desirable. However, watershed-derived disturbance conditions were generally poor for Yellow Perch throughout the lake. In contrast, optimum Smallmouth Bass (Micropterus dolomieu) abundance occurred at in-lake disturbance values greater than the gradient mean and continuously increased with increasing watershed disturbance. Smallmouth Bass responses to disturbance indicated that most of the nearshore zone was less disturbed than is desirable and were most abundant in areas that the Yellow Perch response indicated were highly disturbed. Mapping counts of species response models that agreed on the disturbance level in each spatial unit of the nearshore zone showed a fine-scale mosaic of areas in which habitat restoration may benefit many or few species. This tool may assist managers in prioritizing conservation and restoration efforts and evaluating environmental conditions that may be improved.
In April 2022, a seismic swarm near Mt. Edgecumbe in southeast Alaska suggested renewed activity at this transform fault volcano, which was last active ≈800 years ago. Previously, thin rhyolitic tephras were deposited 5 and 4 ka. Satellite radar data from 2014 to 2022 resolves line-of-sight rapid inflation up to 7.1 cm/yr beginning in August 2018. Bayesian modeling suggests a transcrustal system of a deflating (−0.528 km3) dipping sill at 20 km depth recharging a magma chamber at 10 km (0.222 km3). A near-vertical conduit could capture the volume difference without noticeable surface deformation. Reanalyzed seismicity, recorded 25 km away, shows increases since July 2019. Magma ascent through ductile material and brittle strain release in a stressed overburden could explain the time delay. Cloud-native open data and workflows enabled discovery and analysis of this signal within days after going unnoticed for >3 years.
The phylogeography of the American black bear (Ursus americanus) is characterized by isolation into glacial refugia, followed by population expansion and genetic admixture. Anthropogenic activities, including overharvest, habitat loss, and transportation infrastructure, have also influenced their landscape genetic structure. We describe the genetic structure of the American black bear in the American Southwest and northern Mexico and investigate how prehistoric and contemporary forces shaped genetic structure and influenced gene flow. Using a suite of microsatellites and a sample of 550 bears, we identified 14 subpopulations organized hierarchically following the distribution of ecoregions and mountain ranges containing black bear habitat. The pattern of subdivision we observed is more likely a product of postglacial habitat fragmentation during the Pleistocene and Holocene, rather than a consequence of contemporary anthropogenic barriers to movement during the Anthropocene. We used linear mixed-effects models to quantify the relationship between landscape resistance and genetic distance among individuals, which indicated that both isolation by resistance and geographic distance govern gene flow. Gene flow was highest among subpopulations occupying large tracts of contiguous habitat, was reduced among subpopulations in the Madrean Sky Island Archipelago, where montane habitat exists within a lowland matrix of arid lands, and was essentially nonexistent between two isolated subpopulations. We found significant asymmetric gene flow supporting the hypothesis that bears expanded northward from a Pleistocene refugium located in the American Southwest and northern Mexico and that major highways were not yet affecting gene flow. The potential vulnerability of the species to climate change, transportation infrastructure, and the US–Mexico border wall highlights conservation challenges and opportunities for binational collaboration.
Offshore geological sequestration of CO2 offers a viable approach for reducing greenhouse gas emissions into the atmosphere. Strategies include injection of CO2 into the deep-ocean or ocean-floor sediments, whereby depending on pressure–temperature conditions, CO2 can be trapped physically, gravitationally, or converted to CO2 hydrate. Energy-driven research continues to also advance CO2-for-CH4 replacement strategies in the gas hydrate stability zone (GHSZ), producing methane for natural gas needs while sequestering CO2. In all cases, safe storage of CO2 requires reliable monitoring of the targeted CO2 injection sites and the integrity of the repository over time, including possible leakage. Electromagnetic technologies used for oil and gas exploration, sensitive to electrical conductivity, have long been considered an optimal monitoring method, as CO2, similar to hydrocarbons, typically exhibits lower conductivity than the surrounding medium. We apply 3D controlled-source electromagnetic (CSEM) forward modeling code to simulate an evolving CO2 reservoir in deep-ocean sediments, demonstrating sufficient sensitivity and resolution of CSEM data to detect reservoir changes even before sophisticated inversion of data. Laboratory measurements place further constraints on evaluating certain systems within the GHSZ; notably, CO2 hydrate is measurably weaker than methane hydrate, and >1 order of magnitude more conductive, properties that may affect site selection, stability, and modeling considerations.
","language":"English","publisher":"MDPI","doi":"10.3390/en15197411","usgsCitation":"Constable, S., and Stern, L.A., 2022, Monitoring offshore CO2 sequestration using marine CSEM methods; constraints inferred from field- and laboratory-based gas hydrate studies: Energies, v. 15, no. 19, 7411, 16 p., https://doi.org/10.3390/en15197411.","productDescription":"7411, 16 p.","ipdsId":"IP-142378","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/en15197411","text":"Publisher Index Page"},{"id":408604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Constable, Steven","contributorId":9178,"corporation":false,"usgs":false,"family":"Constable","given":"Steven","email":"","affiliations":[{"id":16196,"text":"Scripps Institution of Oceanography, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":855447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stern, Laura A. 0000-0003-3440-5674","orcid":"https://orcid.org/0000-0003-3440-5674","contributorId":212238,"corporation":false,"usgs":true,"family":"Stern","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":855448,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237298,"text":"ofr20221057 - 2022 - Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","interactions":[],"lastModifiedDate":"2026-03-27T20:28:39.65672","indexId":"ofr20221057","displayToPublicDate":"2022-10-07T11:57:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1057","displayTitle":"Channel Mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","title":"Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona","docAbstract":"Bathymetric and topographic data were collected from May 2013 to February 2016 along the 15.84-mile reach of the Colorado River spanning from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona. Channel bathymetry was mapped using multibeam and singlebeam echo sounders; subaerial topography was mapped using a combination of ground-based total stations and aerial photogrammetry. These data were combined to produce a digital elevation model (DEM), spatially variable estimates of DEM uncertainty, and bed-substrate distribution maps. This project is part of a larger effort to monitor the status and trends of sand storage along the Colorado River in Glen Canyon National Recreation Area and Grand Canyon National Park. This report documents the study methodologies (survey methods and post-processing procedures, DEM production and uncertainty assessment, and bed-substrate classification) and presents the resulting datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221057","collaboration":"Prepared in cooperation with Northern Arizona University and Marda Science LLC","usgsCitation":"Kaplinski, M., Hazel, J.E., Jr., Grams, P.E., Gushue, T., Buscombe, D.D., and Kohl, K., 2022, Channel mapping of the Colorado River from Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona: U.S. Geological Survey Open-File Report 2022-1057, 20 p., https://doi.org/10.3133/ofr20221057.","productDescription":"Report: v, 20 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-120853","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":408061,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1057/ofr20221057.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1057"},{"id":408100,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1057/covrthb.jpg"},{"id":408101,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98GFP93","text":"Channel mapping Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area, Arizona - Data","description":"Kaplinski, M., Hazel, J.E., Jr., Grams, P.E., Gushue, T., Buscombe, D.D., and Kohl, K., 2022, Channel mapping Glen Canyon Dam to Lees Ferry in Glen Canyon National Recreation Area—Data: U.S. Geological Survey data release, https://doi.org/10.5066/P98GFP93."},{"id":501782,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113614.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Glen Canyon Dam, Glen Canyon National Recreation Area, Lees Ferry","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.48668289184569,\n 36.93466271122842\n ],\n [\n -111.48376464843749,\n 36.928625118149235\n ],\n [\n -111.49028778076172,\n 36.92148916432789\n ],\n [\n -111.49972915649414,\n 36.92039126599804\n ],\n [\n -111.50522232055664,\n 36.91599951460428\n ],\n [\n -111.50453567504883,\n 36.90446996392354\n ],\n [\n -111.52050018310547,\n 36.90392089423511\n ],\n [\n -111.52616500854491,\n 36.90172457596916\n ],\n [\n -111.52788162231445,\n 36.894036964201895\n ],\n [\n -111.53406143188475,\n 36.89060474464471\n ],\n [\n -111.53200149536133,\n 36.88470096601324\n ],\n [\n -111.5196418762207,\n 36.87975791652459\n ],\n [\n -111.53200149536133,\n 36.881954866912125\n ],\n [\n -111.54642105102539,\n 36.880856399619006\n ],\n [\n -111.55414581298827,\n 36.87605041942542\n ],\n [\n -111.56805038452148,\n 36.87756090293265\n ],\n [\n -111.57096862792969,\n 36.8708321556463\n ],\n [\n -111.55809402465819,\n 36.85613670839554\n ],\n [\n -111.54779434204102,\n 36.84803240254599\n ],\n [\n -111.55036926269531,\n 36.84624659252608\n ],\n [\n -111.56822204589844,\n 36.84638396400846\n ],\n [\n -111.57337188720703,\n 36.8549005138863\n ],\n [\n -111.5742301940918,\n 36.862180038184434\n ],\n [\n -111.57800674438477,\n 36.86753622642931\n ],\n [\n -111.59156799316406,\n 36.86822289007559\n ],\n [\n -111.60221099853516,\n 36.861630664314674\n ],\n [\n -111.61027908325195,\n 36.852840145275046\n ],\n [\n -111.61869049072266,\n 36.851191810400394\n ],\n [\n -111.62160873413086,\n 36.83608041796078\n ],\n [\n -111.62881851196289,\n 36.8308594247793\n ],\n [\n -111.63843154907227,\n 36.811895979233846\n ],\n [\n -111.64735794067383,\n 36.80474911423461\n ],\n [\n -111.64752960205077,\n 36.7952647442859\n ],\n [\n -111.65147781372069,\n 36.78344200140682\n ],\n [\n -111.65834426879881,\n 36.77752994587241\n ],\n [\n -111.66709899902344,\n 36.76515440007755\n ],\n [\n -111.66915893554688,\n 36.756627862079114\n ],\n [\n -111.68100357055664,\n 36.75236423762844\n ],\n [\n -111.6976547241211,\n 36.74191047837866\n ],\n [\n -111.69919967651367,\n 36.731180139463724\n ],\n [\n -111.70675277709961,\n 36.72746544193304\n ],\n [\n -111.7119026184082,\n 36.72361297320252\n ],\n [\n -111.71791076660155,\n 36.69787903078789\n ],\n [\n -111.73559188842773,\n 36.69113448723155\n ],\n [\n -111.74348831176758,\n 36.682875056701356\n ],\n [\n -111.74692153930664,\n 36.66029475441398\n ],\n [\n -111.76185607910156,\n 36.65423549724267\n ],\n [\n -111.7664909362793,\n 36.649139843693824\n ],\n [\n -111.7668342590332,\n 36.63674360313849\n ],\n [\n -111.7690658569336,\n 36.62530974650164\n ],\n [\n -111.76374435424805,\n 36.61552763134925\n ],\n [\n -111.76614761352539,\n 36.61222072017988\n ],\n [\n -111.77095413208008,\n 36.601610089482534\n ],\n [\n -111.79533004760742,\n 36.58093583857703\n ],\n [\n -111.8107795715332,\n 36.5736296124793\n ],\n [\n -111.82863235473633,\n 36.552534175868985\n ],\n [\n -111.83309555053711,\n 36.53612263184686\n ],\n [\n -111.84991836547852,\n 36.52136305889455\n ],\n [\n -111.85129165649414,\n 36.516534549935\n ],\n [\n -111.86073303222656,\n 36.50687662812643\n ],\n [\n -111.86416625976562,\n 36.501495263232684\n ],\n [\n -111.86038970947266,\n 36.49556152992\n ],\n [\n -111.84837341308594,\n 36.49445752937066\n ],\n [\n -111.84442520141602,\n 36.49224948105883\n ],\n [\n -111.85283660888672,\n 36.48051816892598\n ],\n [\n -111.84442520141602,\n 36.46740460028067\n ],\n [\n -111.8418502807617,\n 36.45359844696098\n ],\n [\n -111.85455322265625,\n 36.44628018910229\n ],\n [\n -111.85609817504883,\n 36.43827073840857\n ],\n [\n -111.87395095825195,\n 36.43357515880956\n ],\n [\n -111.87652587890625,\n 36.423906894561256\n ],\n [\n -111.87257766723633,\n 36.41382299361495\n ],\n [\n -111.88339233398438,\n 36.406362827148655\n ],\n [\n -111.88751220703125,\n 36.39917828607653\n ],\n [\n -111.88493728637694,\n 36.39296035862884\n ],\n [\n -111.87549591064453,\n 36.38922936340128\n ],\n [\n -111.85489654541016,\n 36.39268399475275\n ],\n [\n -111.8547248840332,\n 36.38922936340128\n ],\n [\n -111.88871383666992,\n 36.38176683577235\n ],\n [\n -111.89712524414061,\n 36.373888946305556\n ],\n [\n -111.89352035522461,\n 36.36421325359297\n ],\n [\n -111.87652587890625,\n 36.3472087646811\n ],\n [\n -111.86330795288086,\n 36.32937079795403\n ],\n [\n -111.86708450317383,\n 36.31968957141092\n ],\n [\n -111.86897277832031,\n 36.31000714221622\n ],\n [\n -111.8609046936035,\n 36.30419710747953\n ],\n [\n -111.86502456665039,\n 36.29755653772329\n ],\n [\n -111.86382293701172,\n 36.29382096878581\n ],\n [\n -111.83206558227539,\n 36.25880861429488\n ],\n [\n -111.82931900024414,\n 36.23555062950767\n ],\n [\n -111.80545806884766,\n 36.200650676227696\n ],\n [\n -111.80477142333984,\n 36.19663341291529\n ],\n [\n -111.81987762451172,\n 36.181255086867964\n ],\n [\n -111.8162727355957,\n 36.16434929946814\n ],\n [\n -111.8214225769043,\n 36.14910324993565\n ],\n [\n -111.82039260864258,\n 36.14258812489666\n ],\n [\n -111.83429718017577,\n 36.113887411565116\n ],\n [\n -111.8301773071289,\n 36.10598247395818\n ],\n [\n -111.83635711669922,\n 36.10764673746366\n ],\n [\n -111.84545516967772,\n 36.10182166093424\n ],\n [\n -111.84511184692383,\n 36.097799332209796\n ],\n [\n -111.84991836547852,\n 36.09835414841387\n ],\n [\n -111.85867309570312,\n 36.0907250822317\n ],\n [\n -111.86914443969725,\n 36.093083236312616\n ],\n [\n -111.88201904296875,\n 36.08753452585921\n ],\n [\n -111.88253402709961,\n 36.080875556280155\n ],\n [\n -111.8763542175293,\n 36.07685215551436\n ],\n [\n -111.89523696899414,\n 36.06922100242124\n ],\n [\n -111.91566467285156,\n 36.05090321430484\n ],\n [\n -111.94673538208006,\n 36.049931697391784\n ],\n [\n -111.98175430297852,\n 36.05437281968144\n ],\n [\n -112.01488494873047,\n 36.07046978723051\n ],\n [\n -112.03325271606444,\n 36.08642473676437\n ],\n [\n -112.08749771118163,\n 36.10556640257244\n ],\n [\n -112.09573745727538,\n 36.10306992798007\n ],\n [\n -112.10329055786131,\n 36.10542771162082\n ],\n [\n -112.12646484375,\n 36.10112817067241\n ],\n [\n -112.14088439941406,\n 36.110281749234474\n ],\n [\n -112.15564727783203,\n 36.110697795643894\n ],\n [\n -112.17178344726562,\n 36.103486012588036\n ],\n [\n -112.20027923583983,\n 36.101960358252065\n ],\n [\n -112.22345352172852,\n 36.1113912014288\n ],\n [\n -112.22791671752928,\n 36.12414877519126\n ],\n [\n -112.23443984985352,\n 36.13565654678543\n ],\n [\n -112.26190567016602,\n 36.14868740705872\n ],\n [\n -112.29349136352539,\n 36.15201408833908\n ],\n [\n -112.30087280273438,\n 36.15908281751799\n ],\n [\n -112.3048210144043,\n 36.1838876271496\n ],\n [\n -112.32868194580078,\n 36.21560970545108\n ],\n [\n -112.33503341674805,\n 36.2340275438345\n ],\n [\n -112.34155654907227,\n 36.2394274409703\n ],\n [\n -112.35031127929688,\n 36.24067351811116\n ],\n [\n -112.36026763916016,\n 36.24676516486063\n ],\n [\n -112.39717483520508,\n 36.24828800236324\n ],\n [\n -112.41949081420898,\n 36.238319800169904\n ],\n [\n -112.42738723754883,\n 36.22724252902252\n ],\n [\n -112.42876052856445,\n 36.20591436439329\n ],\n [\n -112.42172241210938,\n 36.205637336998635\n ],\n [\n -112.41657257080078,\n 36.22959658044717\n ],\n [\n -112.39580154418945,\n 36.2408119699007\n ],\n [\n -112.3733139038086,\n 36.238873622531884\n ],\n [\n -112.36009597778319,\n 36.238873622531884\n ],\n [\n -112.35254287719725,\n 36.23222749524095\n ],\n [\n -112.3458480834961,\n 36.23195056101004\n ],\n [\n -112.3355484008789,\n 36.207576508170014\n ],\n [\n -112.31597900390625,\n 36.183471968776296\n ],\n [\n -112.31237411499023,\n 36.16033017416435\n ],\n [\n -112.305850982666,\n 36.149241863737984\n ],\n [\n -112.2867965698242,\n 36.1416177408265\n ],\n [\n -112.26276397705077,\n 36.141063230254474\n ],\n [\n -112.24010467529295,\n 36.1280311035974\n ],\n [\n -112.23392486572266,\n 36.110281749234474\n ],\n [\n -112.22465515136717,\n 36.10057337405567\n ],\n [\n -112.20439910888672,\n 36.09336066155288\n ],\n [\n -112.16577529907225,\n 36.09668968804945\n ],\n [\n -112.15599060058594,\n 36.10140556751167\n ],\n [\n -112.1455192565918,\n 36.10223775215315\n ],\n [\n -112.12852478027344,\n 36.09349937380574\n ],\n [\n -112.1048355102539,\n 36.097244512088324\n ],\n [\n -112.09505081176756,\n 36.09391550909547\n ],\n [\n -112.0835494995117,\n 36.097244512088324\n ],\n [\n -112.05951690673828,\n 36.086008561814516\n ],\n [\n -112.03496932983398,\n 36.07671341388517\n ],\n [\n -112.02398300170898,\n 36.066723373326525\n ],\n [\n -111.98535919189453,\n 36.04757224925469\n ],\n [\n -111.94793701171875,\n 36.04021586880111\n ],\n [\n -111.92338943481444,\n 36.043824745098114\n ],\n [\n -111.91858291625977,\n 36.041326309894316\n ],\n [\n -111.90330505371094,\n 36.043824745098114\n ],\n [\n -111.88974380493164,\n 36.063809372490276\n ],\n [\n -111.88116073608398,\n 36.06533576734184\n ],\n [\n -111.8686294555664,\n 36.071996052851304\n ],\n [\n -111.86656951904297,\n 36.07754585998861\n ],\n [\n -111.87326431274414,\n 36.0850374783633\n ],\n [\n -111.8598747253418,\n 36.08365019548198\n ],\n [\n -111.84906005859375,\n 36.088089414531225\n ],\n [\n -111.84614181518553,\n 36.09197352555871\n ],\n [\n -111.8385887145996,\n 36.091002515795886\n ],\n [\n -111.83412551879883,\n 36.10085077285374\n ],\n [\n -111.82760238647461,\n 36.09946376906971\n ],\n [\n -111.82228088378906,\n 36.10598247395818\n ],\n [\n -111.82416915893555,\n 36.11305535033531\n ],\n [\n -111.81798934936523,\n 36.12095957607491\n ],\n [\n -111.80631637573241,\n 36.163517773207886\n ],\n [\n -111.80923461914062,\n 36.17446549576358\n ],\n [\n -111.80992126464844,\n 36.17986950381666\n ],\n [\n -111.79704666137695,\n 36.18984515478542\n ],\n [\n -111.79481506347655,\n 36.202312931782544\n ],\n [\n -111.80614471435547,\n 36.218933545852614\n ],\n [\n -111.81919097900389,\n 36.23915053224153\n ],\n [\n -111.82193756103516,\n 36.26226903225169\n ],\n [\n -111.85541152954102,\n 36.29617301452927\n ],\n [\n -111.8514633178711,\n 36.30129192777897\n ],\n [\n -111.85077667236328,\n 36.306410505090085\n ],\n [\n -111.85970306396484,\n 36.31360361344308\n ],\n [\n -111.85438156127928,\n 36.32812613620453\n ],\n [\n -111.85764312744139,\n 36.33780631461915\n ],\n [\n -111.86931610107422,\n 36.35439810755854\n ],\n [\n -111.88064575195311,\n 36.36227797065827\n ],\n [\n -111.8873405456543,\n 36.37112458545978\n ],\n [\n -111.88613891601562,\n 36.37402716176924\n ],\n [\n -111.85352325439453,\n 36.382181439429935\n ],\n [\n -111.84648513793944,\n 36.38508360313973\n ],\n [\n -111.8437385559082,\n 36.391716713449014\n ],\n [\n -111.84768676757812,\n 36.39765839419146\n ],\n [\n -111.8569564819336,\n 36.39986913619829\n ],\n [\n -111.87463760375977,\n 36.39448034238433\n ],\n [\n -111.8789291381836,\n 36.39738204701894\n ],\n [\n -111.87875747680664,\n 36.40069814823783\n ],\n [\n -111.86777114868164,\n 36.40815885862307\n ],\n [\n -111.86502456665039,\n 36.41271782897365\n ],\n [\n -111.86708450317383,\n 36.4203155183223\n ],\n [\n -111.8686294555664,\n 36.42611688968639\n ],\n [\n -111.86450958251953,\n 36.43095103471328\n ],\n [\n -111.8521499633789,\n 36.432055939882105\n ],\n [\n -111.8488883972168,\n 36.43481813399704\n ],\n [\n -111.8463134765625,\n 36.444346949113836\n ],\n [\n -111.83618545532227,\n 36.447937213608554\n ],\n [\n -111.83292388916016,\n 36.45318422438426\n ],\n [\n -111.83584213256836,\n 36.46699045144776\n ],\n [\n -111.84425354003906,\n 36.47775760202128\n ],\n [\n -111.84425354003906,\n 36.48176039194775\n ],\n [\n -111.83687210083008,\n 36.49252549054039\n ],\n [\n -111.84202194213867,\n 36.49818346814959\n ],\n [\n -111.85403823852538,\n 36.49942540790511\n ],\n [\n -111.85438156127928,\n 36.503151107655604\n ],\n [\n -111.84803009033203,\n 36.50922223422133\n ],\n [\n -111.84442520141602,\n 36.51667251151527\n ],\n [\n -111.84391021728516,\n 36.519707604051945\n ],\n [\n -111.8327522277832,\n 36.529225985680085\n ],\n [\n -111.82451248168945,\n 36.534329563007525\n ],\n [\n -111.82382583618164,\n 36.54784551881367\n ],\n [\n -111.82125091552734,\n 36.55115518861395\n ],\n [\n -111.81421279907225,\n 36.56039393337068\n ],\n [\n -111.80425643920898,\n 36.57169955053531\n ],\n [\n -111.79361343383789,\n 36.575283912895365\n ],\n [\n -111.76734924316406,\n 36.596097497458956\n ],\n [\n -111.76340103149414,\n 36.60560647239554\n ],\n [\n -111.75859451293945,\n 36.61042941741603\n ],\n [\n -111.75516128540039,\n 36.6164921203925\n ],\n [\n -111.76202774047852,\n 36.62696293911277\n ],\n [\n -111.75859451293945,\n 36.634126363996586\n ],\n [\n -111.75962448120116,\n 36.64252876365686\n ],\n [\n -111.7584228515625,\n 36.64776258214948\n ],\n [\n -111.74280166625977,\n 36.6551995018735\n ],\n [\n -111.7390251159668,\n 36.66291110439972\n ],\n [\n -111.73816680908203,\n 36.680672392083736\n ],\n [\n -111.72958374023438,\n 36.68755550957869\n ],\n [\n -111.71121597290039,\n 36.69498858456907\n ],\n [\n -111.7064094543457,\n 36.71301768775457\n ],\n [\n -111.70486450195312,\n 36.722237044678316\n ],\n [\n -111.6928482055664,\n 36.72870369441065\n ],\n [\n -111.69095993041992,\n 36.739571942595916\n ],\n [\n -111.67551040649414,\n 36.74865056520353\n ],\n [\n -111.66349411010742,\n 36.754152238021554\n ],\n [\n -111.66143417358398,\n 36.76102877424881\n ],\n [\n -111.65319442749023,\n 36.774229995650714\n ],\n [\n -111.64581298828125,\n 36.780004815311294\n ],\n [\n -111.64461135864258,\n 36.7846793506642\n ],\n [\n -111.64066314697266,\n 36.79430249620881\n ],\n [\n -111.64083480834961,\n 36.8021375933119\n ],\n [\n -111.63225173950195,\n 36.80722310453566\n ],\n [\n -111.62813186645506,\n 36.817942805467766\n ],\n [\n -111.62469863891602,\n 36.827012149156175\n ],\n [\n -111.61834716796875,\n 36.83182121345616\n ],\n [\n -111.61474227905273,\n 36.83662997546104\n ],\n [\n -111.61508560180664,\n 36.84349911104911\n ],\n [\n -111.61405563354492,\n 36.847620296243555\n ],\n [\n -111.60907745361328,\n 36.84775766525785\n ],\n [\n -111.60375595092772,\n 36.8509170844658\n ],\n [\n -111.59963607788086,\n 36.856823474483754\n ],\n [\n -111.5939712524414,\n 36.85874638670163\n ],\n [\n -111.588134765625,\n 36.8631414329529\n ],\n [\n -111.58315658569336,\n 36.8631414329529\n ],\n [\n -111.57989501953125,\n 36.861630664314674\n ],\n [\n -111.5797233581543,\n 36.8553125809442\n ],\n [\n -111.57508850097656,\n 36.84789503402531\n ],\n [\n -111.57302856445312,\n 36.842674847355426\n ],\n [\n -111.56633377075195,\n 36.84075153085174\n ],\n [\n -111.55740737915038,\n 36.841850574776956\n ],\n [\n -111.55071258544922,\n 36.84130105478872\n ],\n [\n -111.54247283935547,\n 36.84404861524222\n ],\n [\n -111.54075622558594,\n 36.84940607417791\n ],\n [\n -111.55088424682617,\n 36.857784936640186\n ],\n [\n -111.56375885009766,\n 36.87042017227047\n ],\n [\n -111.56375885009766,\n 36.87289203919785\n ],\n [\n -111.56118392944336,\n 36.87302936279296\n ],\n [\n -111.55431747436523,\n 36.87110680999585\n ],\n [\n -111.55105590820312,\n 36.87151878966862\n ],\n [\n -111.54401779174805,\n 36.87618773734233\n ],\n [\n -111.54058456420898,\n 36.87673700654107\n ],\n [\n -111.53234481811523,\n 36.87852210415612\n ],\n [\n -111.52547836303711,\n 36.875775782851\n ],\n [\n -111.51723861694336,\n 36.875363826137715\n ],\n [\n -111.5115737915039,\n 36.879483293282064\n ],\n [\n -111.51311874389648,\n 36.883465233639356\n ],\n [\n -111.51792526245116,\n 36.8868977741857\n ],\n [\n -111.52650833129881,\n 36.886485877468054\n ],\n [\n -111.5280532836914,\n 36.887996154568654\n ],\n [\n -111.52204513549805,\n 36.89280138293983\n ],\n [\n -111.52204513549805,\n 36.89870453512981\n ],\n [\n -111.51947021484375,\n 36.899528194484695\n ],\n [\n -111.50522232055664,\n 36.89897908923586\n ],\n [\n -111.50007247924805,\n 36.90199911920969\n ],\n [\n -111.49784088134766,\n 36.90886237918357\n ],\n [\n -111.49818420410156,\n 36.915450527897036\n ],\n [\n -111.4918327331543,\n 36.91599951460428\n ],\n [\n -111.48805618286133,\n 36.91723472024699\n ],\n [\n -111.48324966430664,\n 36.92121469122741\n ],\n [\n -111.4779281616211,\n 36.924371072232816\n ],\n [\n -111.47655487060547,\n 36.92821344666079\n ],\n [\n -111.47998809814453,\n 36.9367208722872\n ],\n [\n -111.48668289184569,\n 36.93466271122842\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Ecosystem modeling is a critical process for understanding complex systems at spatiotemporal scales needed to conserve, manage, and restore ecosystem services (ESs). Although mangrove wetlands are sources of ESs worth billions of dollars, there is a lack of modeling tools. This is reflected in our lack of understanding of mangroves’ functional and structural attributes. Here, we discuss the “state of the art” of mangrove models used in the planning and monitoring of R/R projects during the last 30 years. The main objectives were to characterize the most frequent modeling approach, their spatiotemporal resolution, and their current utility/application in management decisions. We identified 281 studies in six broad model categories: conceptual, agent-based (ABM), process-based (PBM), spatial, statistical, and socioeconomic/management (ScoEco). The most widely used models are spatial and statistical, followed by PBM, ScoEco, and conceptual categories, while the ABMs were the least frequently used. Yet, the application of mangrove models in R/R projects since the early 1990s has been extremely limited, especially in the mechanistic model category. We discuss several approaches to help advance model development and applications, including the targeted allocation of potential revenue from global carbon markets to R/R projects using a multi-model and integrated approach.
","language":"English","publisher":"MDPI","doi":"10.3390/f13101638","usgsCitation":"Rivera-Monroy, V.H., Zhao, X., Wang, H., and Xue, Z.G., 2022, Are existing modeling tools useful to evaluate outcomes in mangrove restoration and rehabilitation projects? A minireview: Forests, v. 13, no. 10, 1638, 21 p., https://doi.org/10.3390/f13101638.","productDescription":"1638, 21 p.","ipdsId":"IP-144365","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f13101638","text":"Publisher Index Page"},{"id":408477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rivera-Monroy, Victor H. 0000-0003-2804-4139","orcid":"https://orcid.org/0000-0003-2804-4139","contributorId":200322,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"Victor","email":"","middleInitial":"H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":854879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Xiaochen","contributorId":219696,"corporation":false,"usgs":false,"family":"Zhao","given":"Xiaochen","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":854880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":222813,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xue, Zuo G.","contributorId":298021,"corporation":false,"usgs":false,"family":"Xue","given":"Zuo","email":"","middleInitial":"G.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":854882,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255211,"text":"70255211 - 2022 - Industrial energy development decouples ungulate migration from the green wave","interactions":[],"lastModifiedDate":"2024-06-13T16:04:59.632829","indexId":"70255211","displayToPublicDate":"2022-10-06T10:58:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6505,"text":"Nature Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Industrial energy development decouples ungulate migration from the green wave","docAbstract":"The ability to freely move across the landscape to track the emergence of nutritious spring green-up (termed ‘green-wave surfing’) is key to the foraging strategy of migratory ungulates. Across the vast landscapes traversed by many migratory herds, habitats are being altered by development with unknown consequences for surfing. Using a unique long-term tracking dataset, we found that when energy development occurs within mule deer (Odocoileus hemionus) migration corridors, migrating animals become decoupled from the green wave. During the early phases of a coalbed natural gas development, deer synchronized their movements with peak green-up. But faced with increasing disturbance as development expanded, deer altered their movements by holding up at the edge of the gas field and letting the green wave pass them by. Development often modified only a small portion of the migration corridor but had far-reaching effects on behaviour before and after migrating deer encountered it, thus reducing surfing along the entire route by 38.65% over the 14-year study period. Our study suggests that industrial development within migratory corridors can change the behaviour of migrating ungulates and diminish the benefits of migration. Such disruptions to migratory behaviour present a common mechanism whereby corridors become unprofitable and could ultimately be lost on highly developed landscapes.
","language":"English","publisher":"Nature","doi":"10.1038/s41559-022-01887-9","collaboration":"Western EcoSystems, INC","usgsCitation":"Aikens, E.O., Wyckoff, T., Sawyer, H., and Kauffman, M., 2022, Industrial energy development decouples ungulate migration from the green wave: Nature Ecology and Evolution, v. 6, p. 1733-1741, https://doi.org/10.1038/s41559-022-01887-9.","productDescription":"9 p.","startPage":"1733","endPage":"1741","ipdsId":"IP-136329","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.50057122965278,\n 41.908308585415085\n ],\n [\n -108.50057122965278,\n 40.99058578879266\n ],\n [\n -107.30804228444518,\n 40.99058578879266\n ],\n [\n -107.30804228444518,\n 41.908308585415085\n ],\n [\n -108.50057122965278,\n 41.908308585415085\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2022-10-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Aikens, Ellen O.","contributorId":272241,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen","email":"","middleInitial":"O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":903738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wyckoff, Teal B.","contributorId":339010,"corporation":false,"usgs":false,"family":"Wyckoff","given":"Teal B.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":903739,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawyer, Hall","contributorId":287880,"corporation":false,"usgs":false,"family":"Sawyer","given":"Hall","affiliations":[{"id":61660,"text":"Western Ecosystems Technology, Inc., Laramie, WY","active":true,"usgs":false}],"preferred":false,"id":903740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903741,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237277,"text":"70237277 - 2022 - Absolute accuracy assessment of lidar point cloud using amorphous objects","interactions":[],"lastModifiedDate":"2022-10-06T14:30:04.492861","indexId":"70237277","displayToPublicDate":"2022-10-06T09:26:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Absolute accuracy assessment of lidar point cloud using amorphous objects","docAbstract":"The accuracy assessment of airborne lidar point cloud typically estimates vertical accuracy by computing RMSEz (root mean square error of the z coordinate) from ground check points (GCPs). Due to the low point density of the airborne lidar point cloud, there is often not enough accurate semantic context to find an accurate conjugate point. To advance the accuracy assessment in full three-dimensional (3D) context, geometric features, such as the three-plane intersection point or two-line intersection point, are often used. Although the point density is still low, geometric features are mathematically modeled from many points. Thus, geometric features provide a robust determination of the intersection point, and the point is considered as a GCP. When no regular built objects are available, we describe the process of utilizing features of irregular shape called amorphous natural objects, such as a tree or a rock. When scanned to a high-density point cloud, an amorphous natural object can be used as ground truth reference data to estimate 3D georeferencing errors of the airborne lidar point cloud. The algorithm to estimate 3D accuracy is the optimization that minimizes the sum of the distance between the airborne lidar points to the ground scanned data. The search volume partitioning was the most important procedure to improve the computational efficiency. We also performed an extensive study to address the external uncertainty associated with the amorphous object method. We describe an accuracy assessment using amorphous objects (108 trees) spread over the project area. The accuracy results for ∆x, ∆y, and ∆z obtained using the amorphous object method were 3.1 cm, 3.6 cm, and 1.7 cm RMSE, along with a mean error of 0.1 cm, 0.1 cm, and 4.5 cm, respectively, satisfying the accuracy requirement of U.S. Geological Survey lidar base specification. This approach shows strong promise as an alternative to geometric feature methods when artificial targets are scarce. The relative convenience and advantages of using amorphous targets, along with its good performance shown here, make this amorphous object method a practical way to perform 3D accuracy assessment.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14194767","usgsCitation":"Kim, M., Stoker, J.M., Irwin, J., Danielson, J.J., and Park, S., 2022, Absolute accuracy assessment of lidar point cloud using amorphous objects: Remote Sensing, v. 14, no. 19, 4767, 18 p., https://doi.org/10.3390/rs14194767.","productDescription":"4767, 18 p.","ipdsId":"IP-145321","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":446201,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14194767","text":"Publisher Index Page"},{"id":408035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kim, Minsu 0000-0003-4472-0926","orcid":"https://orcid.org/0000-0003-4472-0926","contributorId":297371,"corporation":false,"usgs":false,"family":"Kim","given":"Minsu","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":853945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":853946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irwin, Jeffrey 0000-0001-5828-0787 jrirwin@usgs.gov","orcid":"https://orcid.org/0000-0001-5828-0787","contributorId":222485,"corporation":false,"usgs":true,"family":"Irwin","given":"Jeffrey","email":"jrirwin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":853948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Park, Seonkyung 0000-0003-3203-1998 seonkyungpark@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-3203-1998","contributorId":222488,"corporation":false,"usgs":false,"family":"Park","given":"Seonkyung","email":"seonkyungpark@contractor.usgs.gov","affiliations":[{"id":40547,"text":"United Support Services, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":853949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}