Pingos are circular to elongate ice-cored mounds that form by injection and freezing of pressurized water in near-surface permafrost. Here we use a digital surface model (DSM) derived from an airborne Interferometric Synthetic Aperture Radar (IfSAR) system to assess the distribution and morphometry of pingos within a 40,000 km2 area on the western Arctic Coastal Plain of northern Alaska. We have identified 1247 pingo forms in the study region, ranging in height from 2 to 21 m, with a mean height of 4.6 m. Pingos in this region are of hydrostatic origin, with 98% located within 995 drained lake basins, most of which are underlain by thick eolian sand deposits. The highest pingo density (0.18 km− 2) occurs where streams have reworked these deposits. Morphometric analyses indicate that most pingos are small to medium in size (< 200 m diameter), gently to moderately sloping (< 30°), circular to slightly elongate (mean circularity index of 0.88), and of relatively low height (2 to 5 m). However, 57 pingos stand higher than 10 m, 26 have a maximum slope greater than 30°, and 42 are larger than 200 m in diameter. Comparison with a legacy pingo dataset based on 1950s stereo-pair photography indicates that 66 may have partially or completely collapsed over the last half-century. However, we mapped over 400 pingos not identified in the legacy dataset, and identified only three higher than 2 m to have formed between ca. 1955 and ca. 2005, indicating that caution should be taken when comparing contemporary and legacy datasets derived by different techniques. This comprehensive database of pingo location and morphometry based on an IfSAR DSM may prove useful for land and resource managers as well as aid in the identification of pingo-like features on Mars.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2011.08.007","issn":"0169555X","usgsCitation":"Jones, B.M., Grosse, G., Hinkel, K.M., Arp, C., Walker, S., Beck, R., and Galloway, J., 2012, Assessment of pingo distribution and morphometry using an IfSAR derived digital surface model, western Arctic Coastal Plain, Northern Alaska: Geomorphology, v. 138, no. 1, p. 1-14, https://doi.org/10.1016/j.geomorph.2011.08.007.","productDescription":"14 p.","startPage":"1","endPage":"14","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":241755,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":214067,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geomorph.2011.08.007"}],"volume":"138","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ee48e4b0c8380cd49c89","contributors":{"authors":[{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":436511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grosse, G.","contributorId":82140,"corporation":false,"usgs":true,"family":"Grosse","given":"G.","affiliations":[],"preferred":false,"id":436514,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinkel, Kenneth M.","contributorId":15405,"corporation":false,"usgs":true,"family":"Hinkel","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":436508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arp, C.D.","contributorId":54715,"corporation":false,"usgs":true,"family":"Arp","given":"C.D.","email":"","affiliations":[],"preferred":false,"id":436512,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, S.","contributorId":71777,"corporation":false,"usgs":true,"family":"Walker","given":"S.","email":"","affiliations":[],"preferred":false,"id":436513,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beck, R.A.","contributorId":44246,"corporation":false,"usgs":true,"family":"Beck","given":"R.A.","email":"","affiliations":[],"preferred":false,"id":436510,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Galloway, J. P.","contributorId":19142,"corporation":false,"usgs":true,"family":"Galloway","given":"J. P.","affiliations":[],"preferred":false,"id":436509,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70032604,"text":"70032604 - 2012 - Ocean-atmosphere dynamics during Hurricane Ida and Nor'Ida: An application of the coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system","interactions":[],"lastModifiedDate":"2017-11-05T22:24:26","indexId":"70032604","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2925,"text":"Ocean Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Ocean-atmosphere dynamics during Hurricane Ida and Nor'Ida: An application of the coupled ocean-atmosphere-wave-sediment transport (COAWST) modeling system","docAbstract":"The coupled ocean–atmosphere–wave–sediment transport (COAWST) modeling system was used to investigate atmosphere–ocean–wave interactions in November 2009 during Hurricane Ida and its subsequent evolution to Nor’Ida, which was one of the most costly storm systems of the past two decades. One interesting aspect of this event is that it included two unique atmospheric extreme conditions, a hurricane and a nor’easter storm, which developed in regions with different oceanographic characteristics. Our modeled results were compared with several data sources, including GOES satellite infrared data, JASON-1 and JASON-2 altimeter data, CODAR measurements, and wave and tidal information from the National Data Buoy Center (NDBC) and the National Tidal Database. By performing a series of numerical runs, we were able to isolate the effect of the interaction terms between the atmosphere (modeled with Weather Research and Forecasting, the WRF model), the ocean (modeled with Regional Ocean Modeling System (ROMS)), and the wave propagation and generation model (modeled with Simulating Waves Nearshore (SWAN)). Special attention was given to the role of the ocean surface roughness. Three different ocean roughness closure models were analyzed: DGHQ (which is based on wave age), TY2001 (which is based on wave steepness), and OOST (which considers both the effects of wave age and steepness). Including the ocean roughness in the atmospheric module improved the wind intensity estimation and therefore also the wind waves, surface currents, and storm surge amplitude. For example, during the passage of Hurricane Ida through the Gulf of Mexico, the wind speeds were reduced due to wave-induced ocean roughness, resulting in better agreement with the measured winds. During Nor’Ida, including the wave-induced surface roughness changed the form and dimension of the main low pressure cell, affecting the intensity and direction of the winds. The combined wave age- and wave steepness-based parameterization (OOST) provided the best results for wind and wave growth prediction. However, the best agreement between the measured (CODAR) and computed surface currents and storm surge values was obtained with the wave steepness-based roughness parameterization (TY2001), although the differences obtained with respect to DGHQ were not significant. The influence of sea surface temperature (SST) fields on the atmospheric boundary layer dynamics was examined; in particular, we evaluated how the SST affects wind wave generation, surface currents and storm surges. The integrated hydrograph and integrated wave height, parameters that are highly correlated with the storm damage potential, were found to be highly sensitive to the ocean surface roughness parameterization.
Metapopulation ecology has historically been rich in theory, yet analytical approaches for inferring demographic relationships among local populations have been few. We show how reverse-time multi-state capture–recapture models can be used to estimate the importance of local recruitment and interpopulation dispersal to metapopulation growth. We use ‘contribution metrics’ to infer demographic connectedness among eight local populations of banner-tailed kangaroo rats, to assess their demographic closure, and to investigate sources of variation in these contributions. Using a 7 year dataset, we show that: (i) local populations are relatively independent demographically, and contributions to local population growth via dispersal within the system decline with distance; (ii) growth contributions via local survival and recruitment are greater for adults than juveniles, while contributions involving dispersal are greater for juveniles; (iii) central populations rely more on local recruitment and survival than peripheral populations; (iv) contributions involving dispersal are not clearly related to overall metapopulation density; and (v) estimated contributions from outside the system are unexpectedly large. Our analytical framework can classify metapopulations on a continuum between demographic independence and panmixia, detect hidden population growth contributions, and make inference about other population linkage forms, including rescue effects and source–sink structures. Finally, we discuss differences between demographic and genetic population linkage patterns for our system.
","language":"English","publisher":"Royal Publishing Society","doi":"10.1098/rspb.2011.0885","issn":"09628452","usgsCitation":"Sanderlin, J., Waser, P., Hines, J., and Nichols, J., 2012, On valuing patches: Estimating contributions to metapopulation growth with reverse-time capture-recapture modelling: Proceedings of the Royal Society B: Biological Sciences, v. 279, no. 1728, p. 480-488, https://doi.org/10.1098/rspb.2011.0885.","productDescription":"9 p.","startPage":"480","endPage":"488","costCenters":[],"links":[{"id":474651,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2011.0885","text":"Publisher Index Page"},{"id":241459,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213800,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1098/rspb.2011.0885"}],"country":"United States","state":"Arizona","county":"Cochise County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-109.0478,32.4251],[-109.0478,32.4059],[-109.0478,32.4016],[-109.0479,32.3866],[-109.0479,32.3576],[-109.048,32.3257],[-109.048,32.3111],[-109.0481,32.266],[-109.0481,32.2514],[-109.0486,32.0745],[-109.0486,32.0391],[-109.0486,31.9747],[-109.0487,31.9592],[-109.0487,31.9408],[-109.0488,31.9112],[-109.0495,31.6101],[-109.0496,31.5944],[-109.0498,31.5717],[-109.0494,31.506],[-109.0497,31.4862],[-109.0498,31.4497],[-109.0499,31.436],[-109.0501,31.3809],[-109.0501,31.3699],[-109.05,31.3319],[-109.2268,31.3323],[-109.2788,31.3322],[-109.3678,31.3321],[-109.3844,31.332],[-109.4021,31.332],[-109.4182,31.3324],[-109.4857,31.3321],[-109.7532,31.3324],[-109.7585,31.3324],[-109.7904,31.3323],[-109.9462,31.3324],[-110.0011,31.3323],[-110.1457,31.3321],[-110.3642,31.3319],[-110.4413,31.3318],[-110.4603,31.3318],[-110.4611,31.3328],[-110.4561,31.3328],[-110.4562,31.4684],[-110.4555,31.5871],[-110.4558,31.6017],[-110.4561,31.6154],[-110.448,31.6157],[-110.4483,31.6536],[-110.4482,31.6883],[-110.4485,31.702],[-110.4485,31.7307],[-110.4482,31.7462],[-110.4484,31.7622],[-110.4481,31.7759],[-110.4467,31.9054],[-110.447,31.9209],[-110.4477,31.993],[-110.4482,32.0518],[-110.4484,32.0668],[-110.4482,32.0801],[-110.446,32.0805],[-110.4444,32.1657],[-110.4443,32.1712],[-110.4437,32.2551],[-110.447,32.2551],[-110.4472,32.2701],[-110.448,32.4274],[-110.3448,32.4268],[-110.3453,32.4255],[-110.2981,32.4252],[-110.2888,32.4255],[-110.2785,32.4254],[-110.2731,32.4258],[-110.2638,32.4256],[-110.2584,32.426],[-110.2296,32.426],[-110.2117,32.4257],[-110.1617,32.4258],[-110.1112,32.4259],[-110.0422,32.4266],[-109.9396,32.4252],[-109.8375,32.4256],[-109.7353,32.4258],[-109.6327,32.4242],[-109.5311,32.4243],[-109.5116,32.4244],[-109.4947,32.4244],[-109.461,32.4241],[-109.4442,32.4242],[-109.4274,32.4248],[-109.3258,32.4238],[-109.3084,32.4234],[-109.2916,32.4234],[-109.2579,32.423],[-109.2405,32.4226],[-109.2242,32.4227],[-109.2242,32.4245],[-109.2068,32.425],[-109.1894,32.4246],[-109.1726,32.4246],[-109.1552,32.4251],[-109.1394,32.4251],[-109.122,32.4252],[-109.1131,32.4251],[-109.0786,32.4249],[-109.0478,32.4251]]]},\"properties\":{\"name\":\"Cochise\",\"state\":\"AZ\"}}]}","volume":"279","issue":"1728","noUsgsAuthors":false,"publicationDate":"2011-06-22","publicationStatus":"PW","scienceBaseUri":"505a6e17e4b0c8380cd754ad","contributors":{"authors":[{"text":"Sanderlin, J.S.","contributorId":98122,"corporation":false,"usgs":true,"family":"Sanderlin","given":"J.S.","affiliations":[],"preferred":false,"id":437635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waser, P.M.","contributorId":66454,"corporation":false,"usgs":true,"family":"Waser","given":"P.M.","affiliations":[],"preferred":false,"id":437634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hines, J.E. 0000-0001-5478-7230","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":36885,"corporation":false,"usgs":true,"family":"Hines","given":"J.E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":437633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nichols, J.D. 0000-0002-7631-2890","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":14332,"corporation":false,"usgs":true,"family":"Nichols","given":"J.D.","affiliations":[],"preferred":false,"id":437632,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70032540,"text":"70032540 - 2012 - A multi-source satellite data approach for modelling Lake Turkana water level: Calibration and validation using satellite altimetry data","interactions":[],"lastModifiedDate":"2020-11-30T21:58:43.196979","indexId":"70032540","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A multi-source satellite data approach for modelling Lake Turkana water level: Calibration and validation using satellite altimetry data","docAbstract":"Lake Turkana is one of the largest desert lakes in the world and is characterized by high degrees of inter- and intra-annual fluctuations. The hydrology and water balance of this lake have not been well understood due to its remote location and unavailability of reliable ground truth datasets. Managing surface water resources is a great challenge in areas where in-situ data are either limited or unavailable. In this study, multi-source satellite-driven data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, and a digital elevation dataset were used to model Lake Turkana water levels from 1998 to 2009. Due to the unavailability of reliable lake level data, an approach is presented to calibrate and validate the water balance model of Lake Turkana using a composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data. Model validation results showed that the satellite-driven water balance model can satisfactorily capture the patterns and seasonal variations of the Lake Turkana water level fluctuations with a Pearson's correlation coefficient of 0.90 and a Nash-Sutcliffe Coefficient of Efficiency (NSCE) of 0.80 during the validation period (2004–2009). Model error estimates were within 10% of the natural variability of the lake. Our analysis indicated that fluctuations in Lake Turkana water levels are mainly driven by lake inflows and over-the-lake evaporation. Over-the-lake rainfall contributes only up to 30% of lake evaporative demand. During the modelling time period, Lake Turkana showed seasonal variations of 1–2 m. The lake level fluctuated in the range up to 4 m between the years 1998–2009. This study demonstrated the usefulness of satellite altimetry data to calibrate and validate the satellite-driven hydrological model for Lake Turkana without using any in-situ data. Furthermore, for Lake Turkana, we identified and outlined opportunities and challenges of using a calibrated satellite-driven water balance model for (i) quantitative assessment of the impact of basin developmental activities on lake levels and for (ii) forecasting lake level changes and their impact on fisheries. From this study, we suggest that globally available satellite altimetry data provide a unique opportunity for calibration and validation of hydrologic models in ungauged basins.
","language":"English","publisher":"European Geosciences Union","publisherLocation":"Munich, Germany","doi":"10.5194/hess-16-1-2012","issn":"10275606","usgsCitation":"Velpuri, N., Senay, G., and Asante, K., 2012, A multi-source satellite data approach for modelling Lake Turkana water level: Calibration and validation using satellite altimetry data: Hydrology and Earth System Sciences, v. 16, no. 1, p. 1-18, https://doi.org/10.5194/hess-16-1-2012.","productDescription":"18 p.","startPage":"1","endPage":"18","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":474744,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-16-1-2012","text":"Publisher Index Page"},{"id":241758,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":214070,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.5194/hess-16-1-2012"}],"country":"Kenya","otherGeospatial":"Lake Turkana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 35.79345703125,\n 2.4162756547063857\n ],\n [\n 36.8701171875,\n 2.4162756547063857\n ],\n [\n 36.8701171875,\n 4.718777551249855\n ],\n [\n 35.79345703125,\n 4.718777551249855\n ],\n [\n 35.79345703125,\n 2.4162756547063857\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2012-01-03","publicationStatus":"PW","scienceBaseUri":"5059e48be4b0c8380cd466ee","contributors":{"authors":[{"text":"Velpuri, N.M. 0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":66495,"corporation":false,"usgs":true,"family":"Velpuri","given":"N.M.","affiliations":[],"preferred":false,"id":436730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":152206,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel B.","email":"senay@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":436729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asante, K.O. 0000-0001-5408-1852","orcid":"https://orcid.org/0000-0001-5408-1852","contributorId":17051,"corporation":false,"usgs":true,"family":"Asante","given":"K.O.","affiliations":[],"preferred":false,"id":436728,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70032565,"text":"70032565 - 2012 - Mapping carbon flux uncertainty and selecting optimal locations for future flux towers in the Great Plains","interactions":[],"lastModifiedDate":"2018-02-23T13:12:35","indexId":"70032565","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping carbon flux uncertainty and selecting optimal locations for future flux towers in the Great Plains","docAbstract":"Flux tower networks (e. g., AmeriFlux, Agriflux) provide continuous observations of ecosystem exchanges of carbon (e. g., net ecosystem exchange), water vapor (e. g., evapotranspiration), and energy between terrestrial ecosystems and the atmosphere. The long-term time series of flux tower data are essential for studying and understanding terrestrial carbon cycles, ecosystem services, and climate changes. Currently, there are 13 flux towers located within the Great Plains (GP). The towers are sparsely distributed and do not adequately represent the varieties of vegetation cover types, climate conditions, and geophysical and biophysical conditions in the GP. This study assessed how well the available flux towers represent the environmental conditions or \"ecological envelopes\" across the GP and identified optimal locations for future flux towers in the GP. Regression-based remote sensing and weather-driven net ecosystem production (NEP) models derived from different extrapolation ranges (10 and 50%) were used to identify areas where ecological conditions were poorly represented by the flux tower sites and years previously used for mapping grassland fluxes. The optimal lands suitable for future flux towers within the GP were mapped. Results from this study provide information to optimize the usefulness of future flux towers in the GP and serve as a proxy for the uncertainty of the NEP map.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Landscape Ecology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1007/s10980-011-9699-7","issn":"09212973","usgsCitation":"Gu, Y., Howard, D., Wylie, B.K., and Zhang, L., 2012, Mapping carbon flux uncertainty and selecting optimal locations for future flux towers in the Great Plains: Landscape Ecology, v. 27, no. 3, p. 319-326, https://doi.org/10.1007/s10980-011-9699-7.","startPage":"319","endPage":"326","numberOfPages":"8","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":241589,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213917,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10980-011-9699-7"}],"volume":"27","issue":"3","noUsgsAuthors":false,"publicationDate":"2011-12-28","publicationStatus":"PW","scienceBaseUri":"505a5053e4b0c8380cd6b5f1","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":436837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Daniel M. 0000-0002-7563-7538 dhoward@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":139585,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel M.","email":"dhoward@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":436836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","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":436838,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Li","contributorId":98139,"corporation":false,"usgs":true,"family":"Zhang","given":"Li","affiliations":[],"preferred":false,"id":436839,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70032573,"text":"70032573 - 2012 - Ecoregional analysis of nearshore sea-surface temperature in the North Pacific","interactions":[],"lastModifiedDate":"2016-05-03T16:03:45","indexId":"70032573","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Ecoregional analysis of nearshore sea-surface temperature in the North Pacific","docAbstract":"The quantification and description of sea surface temperature (SST) is critically important because it can influence the distribution, migration, and invasion of marine species; furthermore, SSTs are expected to be affected by climate change. To better understand present temperature regimes, we assembled a 29-year nearshore time series of mean monthly SSTs along the North Pacific coastline using remotely-sensed satellite data collected with the Advanced Very High Resolution Radiometer (AVHRR) instrument. We then used the dataset to describe nearshore (<20 km offshore) SST patterns of 16 North Pacific ecoregions delineated by the Marine Ecoregions of the World (MEOW) hierarchical schema. Annual mean temperature varied from 3.8°C along the Kamchatka ecoregion to 24.8°C in the Cortezian ecoregion. There are smaller annual ranges and less variability in SST in the Northeast Pacific relative to the Northwest Pacific. Within the 16 ecoregions, 31–94% of the variance in SST is explained by the annual cycle, with the annual cycle explaining the least variation in the Northern California ecoregion and the most variation in the Yellow Sea ecoregion. Clustering on mean monthly SSTs of each ecoregion showed a clear break between the ecoregions within the Warm and Cold Temperate provinces of the MEOW schema, though several of the ecoregions contained within the provinces did not show a significant difference in mean seasonal temperature patterns. Comparison of these temperature patterns shared some similarities and differences with previous biogeographic classifications and the Large Marine Ecosystems (LMEs). Finally, we provide a web link to the processed data for use by other researchers.
\n\n
We describe and present a new model of global subduction zone geometries, called Slab1.0. An extension of previous efforts to constrain the two-dimensional non-planar geometry of subduction zones around the focus of large earthquakes, Slab1.0 describes the detailed, non-planar, three-dimensional geometry of approximately 85% of subduction zones worldwide. While the model focuses on the detailed form of each slab from their trenches through the seismogenic zone, where it combines data sets from active source and passive seismology, it also continues to the limits of their seismic extent in the upper-mid mantle, providing a uniform approach to the definition of the entire seismically active slab geometry. Examples are shown for two well-constrained global locations; models for many other regions are available and can be freely downloaded in several formats from our new Slab1.0 website, http://on.doi.gov/d9ARbS. We describe improvements in our two-dimensional geometry constraint inversion, including the use of ‘average’ active source seismic data profiles in the shallow trench regions where data are otherwise lacking, derived from the interpolation between other active source seismic data along-strike in the same subduction zone. We include several analyses of the uncertainty and robustness of our three-dimensional interpolation methods. In addition, we use the filtered, subduction-related earthquake data sets compiled to build Slab1.0 in a reassessment of previous analyses of the deep limit of the thrust interface seismogenic zone for all subduction zones included in our global model thus far, concluding that the width of these seismogenic zones is on average 30% larger than previous studies have suggested.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2011JB008524","usgsCitation":"Hayes, G.P., Wald, D.J., and Johnson, R.L., 2012, Slab1.0: A three-dimensional model of global subduction zone geometries: Journal of Geophysical Research B: Solid Earth, v. 117, no. B1, Article B01302; 15 p., https://doi.org/10.1029/2011JB008524.","productDescription":"Article B01302; 15 p.","costCenters":[],"links":[{"id":241645,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"B1","noUsgsAuthors":false,"publicationDate":"2012-01-04","publicationStatus":"PW","scienceBaseUri":"505b912ee4b08c986b3197a2","contributors":{"authors":[{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":842,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":436147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":436146,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Rebecca L. 0000-0002-8771-6161 rljohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-8771-6161","contributorId":178874,"corporation":false,"usgs":true,"family":"Johnson","given":"Rebecca","email":"rljohnson@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":436145,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70155848,"text":"70155848 - 2012 - Groundwater and surface-water exchange and resultingnNitrate dynamics in the Bogue Phalia Basin in northwestern Mississippi","interactions":[],"lastModifiedDate":"2022-11-15T16:09:38.395666","indexId":"70155848","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater and surface-water exchange and resultingnNitrate dynamics in the Bogue Phalia Basin in northwestern Mississippi","docAbstract":"During April 2007 through September 2008, the USGS collected hydrogeologic and water-quality data from a site on the Bogue Phalia to evaluate the role of groundwater and surface-water interaction on the transport of nitrate to the shallow sand and gravel aquifer underlying the Mississippi Alluvial Plain in northwestern Mississippi. A two-dimensional groundwater/surface-water exchange model was developed using temperature and head data and VS2DH, a variably saturated flow and energy transport model. Results from this model showed that groundwater/surface-water exchange at the site occurred regularly and recharge was laterally extensive into the alluvial aquifer. Nitrate was consistently reported in surface-water samples (n = 52, median concentration = 39.8 μmol/L) although never detected in samples collected from in-stream piezometers or shallow monitoring wells adjacent to the stream (n = 46). These two facts, consistent detections of nitrate in surface water and no detections of nitrate in groundwater, coupled with model results that indicate large amounts of surface water moving through an anoxic streambed, support the case for denitrification and nitrate loss through the streambed.
","language":"English","publisher":"Alliance of Crop, Soil, and Environmental Science Societies","doi":"10.2134/jeq2011.0087","usgsCitation":"Barlow, J.R., and Coupe, R.H., 2012, Groundwater and surface-water exchange and resultingnNitrate dynamics in the Bogue Phalia Basin in northwestern Mississippi: Journal of Environmental Quality, v. 41, no. 1, p. 155-169, https://doi.org/10.2134/jeq2011.0087.","productDescription":"15 p.","startPage":"155","endPage":"169","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-024197","costCenters":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true}],"links":[{"id":381802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Bogue Phalia Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.20421710355372,\n 34.156863209912004\n ],\n [\n -90.75054266585917,\n 34.156863209912004\n ],\n [\n -90.8764117905081,\n 34.12139801584064\n ],\n [\n -91.1361842392514,\n 33.60994504300518\n ],\n [\n -91.05316417831278,\n 33.117872488161694\n ],\n [\n -90.22296356892653,\n 33.129086822630626\n ],\n [\n -90.20689517003508,\n 34.156863209912004\n ],\n [\n -90.20421710355372,\n 34.156863209912004\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2012-01-01","publicationStatus":"PW","scienceBaseUri":"55cc6e29e4b08400b1fe0fd2","contributors":{"authors":[{"text":"Barlow, Jeannie R. B. 0000-0002-0799-4656 jbarlow@usgs.gov","orcid":"https://orcid.org/0000-0002-0799-4656","contributorId":3701,"corporation":false,"usgs":true,"family":"Barlow","given":"Jeannie","email":"jbarlow@usgs.gov","middleInitial":"R. B.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coupe, Richard H. 0000-0001-8679-1015 rhcoupe@usgs.gov","orcid":"https://orcid.org/0000-0001-8679-1015","contributorId":551,"corporation":false,"usgs":true,"family":"Coupe","given":"Richard","email":"rhcoupe@usgs.gov","middleInitial":"H.","affiliations":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true}],"preferred":true,"id":566595,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70035398,"text":"70035398 - 2012 - An investigation of element ratios for assessing suspended-sediment sources in small agricultural basins","interactions":[],"lastModifiedDate":"2023-09-25T11:06:08.54295","indexId":"70035398","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3059,"text":"Physical Geography","active":true,"publicationSubtype":{"id":10}},"title":"An investigation of element ratios for assessing suspended-sediment sources in small agricultural basins","docAbstract":"Various sediment properties previously have been investigated for the purpose of determining sources of suspended sediment. A remaining research need is an assessment of element ratios for the determination of suspended-sediment sources in different terrestrial environments. In this study, 253 element ratios were assessed to determine which, if any, were potentially useful for sediment-source determinations in six small agricultural basins in northeastern Kansas, USA. Samples of surface soils (cropland and grassland), channel banks, and reservoir bottom sediments were collected, analyzed for 23 elements, and compared. Of the 253 element ratios assessed, only the Co/Pb and Co/Zn ratios were substantially and consistently different between the channel banks and surface soils for all six basins. For three of four reservoirs for which data were available, sediment-source estimates provided by Co/Pb ratios were in agreement with estimates previously provided using 137Cs. For two of the four reservoirs, sediment-source estimates provided by Co/Zn ratios were consistent with the 137Cs estimates. Thus, the Co/Pb ratio potentially may be more useful. Additional research is needed to ascertain whether or not the use of Co/Pb and Co/Zn ratios as tracers is widely applicable or restricted to specific terrestrial environments.","language":"English","publisher":"Taylor and Francis","doi":"10.2747/0272-3646.33.1.50","issn":"02723646","usgsCitation":"Juracek, K., 2012, An investigation of element ratios for assessing suspended-sediment sources in small agricultural basins: Physical Geography, v. 33, no. 1, p. 50-67, https://doi.org/10.2747/0272-3646.33.1.50.","productDescription":"18 p.","startPage":"50","endPage":"67","numberOfPages":"18","costCenters":[],"links":[{"id":215286,"rank":2,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2747/0272-3646.33.1.50"},{"id":243078,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-05-15","publicationStatus":"PW","scienceBaseUri":"5059ea8fe4b0c8380cd48942","contributors":{"authors":[{"text":"Juracek, K.","contributorId":19795,"corporation":false,"usgs":true,"family":"Juracek","given":"K.","affiliations":[],"preferred":false,"id":450462,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70032694,"text":"70032694 - 2012 - Incidence of adult brain cancers is higher in countries where the protozoan parasite Toxoplasma gondii is common","interactions":[],"lastModifiedDate":"2014-09-18T13:28:00","indexId":"70032694","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1028,"text":"Biology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Incidence of adult brain cancers is higher in countries where the protozoan parasite Toxoplasma gondii is common","docAbstract":"We explored associations between the common protozoan parasite Toxoplasma gondii and brain cancers in human populations. We predicted that T. gondii could increase the risk of brain cancer because it is a long-lived parasite that encysts in the brain, where it provokes inflammation and inhibits apoptosis. We used a medical geography approach based on the national incidence of brain cancers and seroprevalence of T. gondii. We corrected reports of incidence for national gross domestic product because wealth probably increases the ability to detect cancer. We also included gender, cell phone use and latitude as variables in our initial models. Prevalence of T. gondii explained 19 per cent of the residual variance in brain cancer incidence after controlling for the positive effects of gross domestic product and latitude among nations. Infection with T. gondii was associated with a 1.8-fold increase in the risk of brain cancers across the range of T. gondii prevalence in our dataset (4–67%). These results, though correlational, suggest that T. gondii should be investigated further as a possible oncogenic pathogen of humans.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biology Letters","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Royal Society","publisherLocation":"London","doi":"10.1098/rsbl.2011.0588","issn":"17449561","usgsCitation":"Thomas, F., Lafferty, K.D., Brodeur, J., Elguero, E., Gauthier-Clerc, M., and Misse, D., 2012, Incidence of adult brain cancers is higher in countries where the protozoan parasite Toxoplasma gondii is common: Biology Letters, v. 8, no. 1, p. 101-103, https://doi.org/10.1098/rsbl.2011.0588.","productDescription":"3 p.","startPage":"101","endPage":"103","numberOfPages":"3","costCenters":[],"links":[{"id":474734,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1098/rsbl.2011.0588","text":"External Repository"},{"id":241527,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213862,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1098/rsbl.2011.0588"}],"volume":"8","issue":"1","noUsgsAuthors":false,"publicationDate":"2011-07-27","publicationStatus":"PW","scienceBaseUri":"505a39d5e4b0c8380cd61a64","contributors":{"authors":[{"text":"Thomas, Frederic","contributorId":57275,"corporation":false,"usgs":true,"family":"Thomas","given":"Frederic","email":"","affiliations":[],"preferred":false,"id":437487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":437484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brodeur, Jacques","contributorId":47987,"corporation":false,"usgs":true,"family":"Brodeur","given":"Jacques","email":"","affiliations":[],"preferred":false,"id":437486,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elguero, Eric","contributorId":80909,"corporation":false,"usgs":true,"family":"Elguero","given":"Eric","email":"","affiliations":[],"preferred":false,"id":437489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gauthier-Clerc, Michel","contributorId":59639,"corporation":false,"usgs":true,"family":"Gauthier-Clerc","given":"Michel","email":"","affiliations":[],"preferred":false,"id":437488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Misse, Dorothee","contributorId":29227,"corporation":false,"usgs":true,"family":"Misse","given":"Dorothee","email":"","affiliations":[],"preferred":false,"id":437485,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70032725,"text":"70032725 - 2012 - Beach response dynamics of a littoral cell using a 17-year single-point time series of sand thickness","interactions":[],"lastModifiedDate":"2020-11-23T18:48:34.216397","indexId":"70032725","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Beach response dynamics of a littoral cell using a 17-year single-point time series of sand thickness","docAbstract":"A 17-year time series of near-daily sand thickness measurements at a single intertidal location was compared with 5 years of semi-annual 3-dimensional beach surveys at the same beach, and at two other beaches within the same littoral cell. The daily single point measurements correlated extremely well with the mean beach elevation and shoreline position of ten high-spatial resolution beach surveys. Correlations were statistically significant at all spatial scales, even for beach surveys 10s of kilometers downcoast, and therefore variability at the single point monitoring site was representative of regional coastal behavior, allowing us to examine nearly two decades of continuous coastal evolution. The annual cycle of beach oscillations dominated the signal, typical of this region, with additional, less intense spectral peaks associated with seasonal wave energy fluctuations (~ 45 to 90 days), as well as full lunar (~ 29 days) and semi-lunar (~ 13 days; spring-neap cycle) tidal cycles. Sand thickness variability was statistically linked to wave energy with a 2 month peak lag, as well as the average of the previous 7–8 months of wave energy. Longer term anomalies in sand thickness were also apparent on time scales up to 15 months. Our analyses suggest that spatially-limited morphological data sets can be extremely valuable (with robust validation) for understanding the details of beach response to wave energy over timescales that are not resolved by typical survey intervals, as well as the regional behavior of coastal systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2011.12.023","issn":"0169555X","usgsCitation":"Barnard, P., Hubbard, D., and Dugan, J., 2012, Beach response dynamics of a littoral cell using a 17-year single-point time series of sand thickness: Geomorphology, v. 139-140, p. 588-598, https://doi.org/10.1016/j.geomorph.2011.12.023.","productDescription":"11 p.","startPage":"588","endPage":"598","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":241492,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213831,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geomorph.2011.12.023"}],"country":"United States","state":"California","otherGeospatial":"Santa Barbara Channel","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.91714477539064,\n 34.37857917718959\n ],\n [\n -119.674072265625,\n 34.37857917718959\n ],\n [\n -119.674072265625,\n 34.43523054493987\n ],\n [\n -119.91714477539064,\n 34.43523054493987\n ],\n [\n -119.91714477539064,\n 34.37857917718959\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"139-140","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059f031e4b0c8380cd4a63e","contributors":{"authors":[{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":437640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, D.M.","contributorId":50994,"corporation":false,"usgs":true,"family":"Hubbard","given":"D.M.","email":"","affiliations":[],"preferred":false,"id":437641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugan, J.E.","contributorId":13584,"corporation":false,"usgs":true,"family":"Dugan","given":"J.E.","affiliations":[],"preferred":false,"id":437639,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70032249,"text":"70032249 - 2012 - Geometry and subsidence history of the Dead Sea basin: A case for fluid-induced mid-crustal shear zone?","interactions":[],"lastModifiedDate":"2020-12-04T14:04:20.924152","indexId":"70032249","displayToPublicDate":"2012-01-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Geometry and subsidence history of the Dead Sea basin: A case for fluid-induced mid-crustal shear zone?","docAbstract":"Pull‐apart basins are narrow zones of crustal extension bounded by strike‐slip faults that can serve as analogs to the early stages of crustal rifting. We use seismic tomography, 2‐D ray tracing, gravity modeling, and subsidence analysis to study crustal extension of the Dead Sea basin (DSB), a large and long‐lived pull‐apart basin along the Dead Sea transform (DST). The basin gradually shallows southward for 50 km from the only significant transverse normal fault. Stratigraphic relationships there indicate basin elongation with time. The basin is deepest (8–8.5 km) and widest (∼15 km) under the Lisan about 40 km north of the transverse fault. Farther north, basin depth is ambiguous, but is 3 km deep immediately north of the lake. The underlying pre‐basin sedimentary layer thickens gradually from 2 to 3 km under the southern edge of the DSB to 3–4 km under the northern end of the lake and 5–6 km farther north. Crystalline basement is ∼11 km deep under the deepest part of the basin. The upper crust under the basin has lowerPwave velocity than in the surrounding regions, which is interpreted to reflect elevated pore fluids there. Within data resolution, the lower crust below ∼18 km and the Moho are not affected by basin development. The subsidence rate was several hundreds of m/m.y. since the development of the DST ∼17 Ma, similar to other basins along the DST, but subsidence rate has accelerated by an order of magnitude during the Pleistocene, which allowed the accumulation of 4 km of sediment. We propose that the rapid subsidence and perhaps elongation of the DSB are due to the development of inter‐connected mid‐crustal ductile shear zones caused by alteration of feldspar to muscovite in the presence of pore fluids. This alteration resulted in a significant strength decrease and viscous creep. We propose a similar cause to the enigmatic rapid subsidence of the North Sea at the onset the North Atlantic mantle plume. Thus, we propose that aqueous fluid flux into a slowly extending continental crust can cause rapid basin subsidence that may be erroneously interpreted as an increased rate of tectonic activity.