Previous work has shown that the bedrock erosion rate E because of collisions of saltating bedload can be expressed by E = βqb(1-Pc), where qb is the sediment transport rate, Pc is the extent of alluvial cover, and β is the abrasion coefficient. However, the dependence of the abrasion coefficient on the physical characteristics of the bedrock material is poorly known, and in particular, the effects of wet-dry weathering on the saltation-abrasion bedrock incision has not been specifically characterized. Observation suggests that the typical wet-dry cycling of exposed bedrock in river beds gives rise to cracks and voids that are likely to alter the incision rate of the material when subjected to impacts of moving sediment. In this study, flume experiments are performed to develop an understanding of how wet-dry cycling affects the rock tensile strength and the bedrock erosion rate. To represent the physical effects of weathering, boring cores taken from natural bedrock channel are exposed to artificial wet-dry cycles. The experimental results suggest the following: (1) the abrasion coefficient for fresh bedrock is estimated by β = 1.0 × 10− 4σT− 2(d/ksb)0.5, where σT is the tensile strength, d is the diameter of colliding gravel, and ksb is the hydraulic roughness height of bedrock; (2) the tensile strength of the bedrock decreases exponentially as a result of repeated wet-dry cycles, σT/σT0 = exp (-CTNWa0/σT0), where σT0 is the initial tensile strength, Wa0 is the initial normalized rate of water absorption., N is the number of wet-dry cycles, and CT is a constant; (3) the erosion rate of fresh bedrock depends on the inverse of the square of tensile strength, but the erosion rate of weathered bedrock depends on the − 1.5 power of tensile strength.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.02.018","usgsCitation":"Inoue, T., Yamaguchi, S., and Nelson, J.M., 2017, The effect of wet-dry weathering on the rate of bedrock river channel erosion by saltating gravel: Geomorphology, v. 285, p. 152-161, https://doi.org/10.1016/j.geomorph.2017.02.018.","productDescription":"10 p.","startPage":"152","endPage":"161","ipdsId":"IP-080142","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":337666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ca52c4e4b0849ce97c8678","contributors":{"authors":[{"text":"Inoue, Takuya","contributorId":173794,"corporation":false,"usgs":false,"family":"Inoue","given":"Takuya","email":"","affiliations":[{"id":27295,"text":"Civil Engineering Research Institute, Sapporo, Japan","active":true,"usgs":false}],"preferred":false,"id":684577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yamaguchi, Satomi","contributorId":189359,"corporation":false,"usgs":false,"family":"Yamaguchi","given":"Satomi","email":"","affiliations":[],"preferred":false,"id":684578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":684576,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70185136,"text":"70185136 - 2017 - Operational shoreline mapping with high spatial resolution radar and geographic processing","interactions":[],"lastModifiedDate":"2017-03-15T16:54:23","indexId":"70185136","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Operational shoreline mapping with high spatial resolution radar and geographic processing","docAbstract":"A comprehensive mapping technology was developed utilizing standard image processing and available GIS procedures to automate shoreline identification and mapping from 2 m synthetic aperture radar (SAR) HH amplitude data. The development used four NASA Uninhabited Aerial Vehicle SAR (UAVSAR) data collections between summer 2009 and 2012 and a fall 2012 collection of wetlands dominantly fronted by vegetated shorelines along the Mississippi River Delta that are beset by severe storms, toxic releases, and relative sea-level rise. In comparison to shorelines interpreted from 0.3 m and 1 m orthophotography, the automated GIS 10 m alongshore sampling found SAR shoreline mapping accuracy to be ±2 m, well within the lower range of reported shoreline mapping accuracies. The high comparability was obtained even though water levels differed between the SAR and photography image pairs and included all shorelines regardless of complexity. The SAR mapping technology is highly repeatable and extendable to other SAR instruments with similar operational functionality.
","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","doi":"10.14358/PERS.83.3.237","usgsCitation":"Rangoonwala, A., Jones, C., Chi, Z., and Ramsey, E.W., 2017, Operational shoreline mapping with high spatial resolution radar and geographic processing: Photogrammetric Engineering and Remote Sensing, v. 83, no. 3, p. 237-246, https://doi.org/10.14358/PERS.83.3.237.","productDescription":"10 p.","startPage":"237","endPage":"246","ipdsId":"IP-074986","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.83.3.237","text":"Publisher Index Page"},{"id":337621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"83","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ca52c6e4b0849ce97c867e","contributors":{"authors":[{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":684481,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Cathleen E","contributorId":189314,"corporation":false,"usgs":false,"family":"Jones","given":"Cathleen E","affiliations":[],"preferred":false,"id":684482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chi, Zhaohui","contributorId":189315,"corporation":false,"usgs":false,"family":"Chi","given":"Zhaohui","email":"","affiliations":[],"preferred":false,"id":684483,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796 ramseye@usgs.gov","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":2883,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah","suffix":"III","email":"ramseye@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":684484,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70185125,"text":"70185125 - 2017 - Toxicity of chromium (VI) to two mussels and an amphipod in water-only exposures with or without a co-stressor of elevated temperature, zinc, or nitrate","interactions":[],"lastModifiedDate":"2017-03-22T14:39:31","indexId":"70185125","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"title":"Toxicity of chromium (VI) to two mussels and an amphipod in water-only exposures with or without a co-stressor of elevated temperature, zinc, or nitrate","docAbstract":"The objectives of the present study were to develop methods for propagating western pearlshell (Margaritifera falcata) for laboratory toxicity testing and evaluate acute and chronic toxicity of chromium VI [Cr(VI)] to the pearlshell and a commonly tested mussel (fatmucket, Lampsilis siliquoidea at 20 °C or in association with a co-stressor of elevated temperature (27 °C), zinc (50 µg Zn/L), or nitrate (35 mg NO3/L). A commonly tested invertebrate (amphipod, Hyalella azteca) also was tested in chronic exposures. Newly transformed pearlshell (~1 week old) were successfully cultured and tested in acute 96 h Cr exposures (control survival 100%). However, the grow-out of juveniles in culture for chronic toxicity testing was less successful and chronic 28-day Cr toxicity tests started with 4 month-old pearlshell failed due to low control survival (39–68%). Acute median effect concentration (EC50) for the pearlshell (919 µg Cr/L) and fatmucket (456 µg Cr/L) tested at 20 °C without a co-stressor decreased by a factor of > 2 at elevated temperature but did not decrease at elevated Zn or elevated NO3. Chronic 28-day Cr tests were completed successfully with the fatmucket and amphipod (control survival 83–98%). Chronic maximum acceptable toxicant concentration (MATC) for fatmucket at 20 °C (26 µg Cr/L) decreased by a factor of 2 at elevated temperature or NO3 but did not decrease at elevated Zn. However, chronic MATC for amphipod at 20 °C (13 µg Cr/L) did not decrease at elevated temperature, Zn, or NO3. Acute EC50s for both mussels tested with or without a co-stressor were above the final acute value used to derive United States Environmental Protection Agency acute water quality criterion (WQC) for Cr(VI); however, chronic MATCs for fatmucket at elevated temperature or NO3 and chronic MATCs for the amphipod at 20 °C with or without elevated Zn or NO3 were about equal to the chronic WQC. The results indicate that (1) the elevated temperature increased the acute Cr toxicity to both mussel species, (2) fatmucket was acutely more sensitive to Cr than the pearlshell, (3) elevated temperature or NO3 increased chronic Cr toxicity to fatmucket, and (4) acute WQC are protective of tested mussels with or without a co-stressor; however, the chronic WQC might not protect fatmucket at elevated temperature or NO3 and might not protect the amphipod at 20 °C with or without elevated Zn or NO3.
","language":"English","publisher":"Springer","doi":"10.1007/s00244-017-0377-x","usgsCitation":"Wang, N., Kunz, J.L., Ivey, C.D., Ingersoll, C.G., Barnhart, M., and Glidewell, E.A., 2017, Toxicity of chromium (VI) to two mussels and an amphipod in water-only exposures with or without a co-stressor of elevated temperature, zinc, or nitrate: Archives of Environmental Contamination and Toxicology, v. 72, no. 3, p. 449-460, https://doi.org/10.1007/s00244-017-0377-x.","productDescription":"12 p.","startPage":"449","endPage":"460","ipdsId":"IP-079222","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":337599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-25","publicationStatus":"PW","scienceBaseUri":"58ca52c8e4b0849ce97c8682","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":684437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":684438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":684439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":684440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, M. Christopher","contributorId":189301,"corporation":false,"usgs":false,"family":"Barnhart","given":"M. Christopher","affiliations":[],"preferred":false,"id":684441,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glidewell, Elizabeth A.","contributorId":189302,"corporation":false,"usgs":false,"family":"Glidewell","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":684442,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70189745,"text":"70189745 - 2017 - Chronic toxicity of azoxystrobin to freshwater amphipods, midges, cladocerans, and mussels in water-only exposures","interactions":[],"lastModifiedDate":"2017-08-27T18:07:22","indexId":"70189745","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Chronic toxicity of azoxystrobin to freshwater amphipods, midges, cladocerans, and mussels in water-only exposures","docAbstract":"Understanding the effects of fungicides on nontarget organisms at realistic concentrations and exposure durations is vital for determining potential impacts on aquatic ecosystems. Environmental concentrations of the fungicide azoxystrobin have been reported up to 4.6 μg/L in the United States and 30 μg/L in Europe. The objective of the present study was to evaluate the chronic toxicity of azoxystrobin in water-only exposures with an amphipod (Hyalella azteca; 42-d exposure), a midge (Chironomus dilutus; 50-d exposure), a cladoceran (Ceriodaphnia dubia; 7-d exposure), and a unionid mussel (Lampsilis siliquoidea; 28-d exposure) at environmentally relevant concentrations. The potential photo-enhanced toxicity of azoxystrobin accumulated by C. dubiaand L. siliquoidea following chronic exposures to azoxystrobin was also evaluated. The 20% effect concentrations (EC20s) based on the most sensitive endpoint were 4.2 μg/L for H. aztecareproduction, 12 μg/L for C. dubia reproduction and C. dilutus emergence, and >28 μg/L for L. siliquoidea. Hyalella azteca was more sensitive to azoxystrobin compared with the other 3 species in the chronic exposures. No photo-enhanced toxicity was observed for either C. dubia or L. siliquoidea exposed to ultraviolet light in control water following azoxystrobin tests. The results of the present study indicate chronic effects of azoxystrobin on 3 of 4 invertebrates tested at environmentally relevant concentrations. The changes noted in biomass and reproduction have the potential to alter the rate of ecological processes driven by aquatic invertebrates. Environ Toxicol Chem 2017;9999:1–8. Published 2017 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
","language":"English","publisher":"SETAC PRESS","doi":"10.1002/etc.3764","usgsCitation":"Kunz, J.L., Ingersoll, C.G., Smalling, K.L., Elskus, A., and Kuivila, K., 2017, Chronic toxicity of azoxystrobin to freshwater amphipods, midges, cladocerans, and mussels in water-only exposures: Environmental Toxicology and Chemistry, v. 36, no. 9, p. 2308-2315, https://doi.org/10.1002/etc.3764.","productDescription":"8 p. ","startPage":"2308","endPage":"2315","ipdsId":"IP-071621","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":344235,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-09","publicationStatus":"PW","scienceBaseUri":"5977074de4b0ec1a48889f5a","contributors":{"authors":[{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":706104,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":706105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smalling, Kelly L. 0000-0002-1214-4920 ksmall@usgs.gov","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":190789,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","email":"ksmall@usgs.gov","middleInitial":"L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":706106,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elskus, Adria 0000-0003-1192-5124 aelskus@usgs.gov","orcid":"https://orcid.org/0000-0003-1192-5124","contributorId":130,"corporation":false,"usgs":true,"family":"Elskus","given":"Adria","email":"aelskus@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":706107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kuivila, Kathryn 0000-0001-7940-489X kkuivila@usgs.gov","orcid":"https://orcid.org/0000-0001-7940-489X","contributorId":190790,"corporation":false,"usgs":true,"family":"Kuivila","given":"Kathryn","email":"kkuivila@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":706108,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184972,"text":"70184972 - 2017 - Identifying small depressional wetlands and using a topographic position index to infer hydroperiod regimes for pond-breeding amphibians","interactions":[],"lastModifiedDate":"2017-04-19T16:08:29","indexId":"70184972","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Identifying small depressional wetlands and using a topographic position index to infer hydroperiod regimes for pond-breeding amphibians","docAbstract":"Small, seasonal pools and temporary ponds (<4.0 ha) are the most numerous and biologically diverse wetlands in many natural landscapes. Thus, accurate determination of their numbers and spatial characteristics is beneficial for conservation and management of biodiversity associated with these freshwater systems. We examined the utility of a topographic position index (TPI) landscape classification to identify and classify depressional wetlands. We also assessed relationships between topographic characteristics and ponded duration of known wetlands to allow hydrological characteristics to be extended to non-monitored locations in similar landscapes. Our results indicate that this approach was successful at identifying wetlands, but did have higher errors of commission (10%) than omission (5%). Additionally, the TPI procedure provided a reasonable means to correlate general ponded duration characteristics (long/short) with wetland topography. Although results varied by hydrologic class, permanent/long ponded duration wetlands were more often classified correctly (80%) than were short ponded duration wetlands (67%). However, classification results were improved to 100 and 75% for permanent/long and short ponded duration wetlands, respectively, by removing wetlands occurring on an abrupt marine terrace that erroneously inflated pond topographic characteristics. Our study presents an approach for evaluating wetland suitability for species or guilds that are associated with key habitat characteristics, such as hydroperiod.","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0872-2","usgsCitation":"Riley, J.W., Calhoun, D.L., Barichivich, W.J., and Walls, S.C., 2017, Identifying small depressional wetlands and using a topographic position index to infer hydroperiod regimes for pond-breeding amphibians: Wetlands, v. 37, no. 2, p. 325-338, https://doi.org/10.1007/s13157-016-0872-2.","productDescription":"14 p.","startPage":"325","endPage":"338","ipdsId":"IP-068981","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":337606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-07","publicationStatus":"PW","scienceBaseUri":"58ca52c9e4b0849ce97c868a","chorus":{"doi":"10.1007/s13157-016-0872-2","url":"http://dx.doi.org/10.1007/s13157-016-0872-2","publisher":"Springer Nature","authors":"Riley Jeffrey W., Calhoun Daniel L., Barichivich William J., Walls Susan C.","journalName":"Wetlands","publicationDate":"1/7/2017","auditedOn":"2/15/2017","publiclyAccessibleDate":"1/7/2017"},"contributors":{"authors":[{"text":"Riley, Jeffrey W. 0000-0001-5525-3134 jriley@usgs.gov","orcid":"https://orcid.org/0000-0001-5525-3134","contributorId":3605,"corporation":false,"usgs":true,"family":"Riley","given":"Jeffrey","email":"jriley@usgs.gov","middleInitial":"W.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":683776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Daniel L. 0000-0003-2371-6936 dcalhoun@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-6936","contributorId":1455,"corporation":false,"usgs":true,"family":"Calhoun","given":"Daniel","email":"dcalhoun@usgs.gov","middleInitial":"L.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":683777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barichivich, William J. 0000-0003-1103-6861 wbarichivich@usgs.gov","orcid":"https://orcid.org/0000-0003-1103-6861","contributorId":3697,"corporation":false,"usgs":true,"family":"Barichivich","given":"William","email":"wbarichivich@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":683778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":2310,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":683779,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70181792,"text":"70181792 - 2017 - Status and trends of dam removal research in the United States","interactions":[],"lastModifiedDate":"2017-11-22T17:01:38","indexId":"70181792","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"Status and trends of dam removal research in the United States","docAbstract":"Aging infrastructure coupled with growing interest in river restoration has driven a dramatic increase in the practice of dam removal. With this increase, there has been a proliferation of studies that assess the physical and ecological responses of rivers to these removals. As more dams are considered for removal, scientific information from these dam-removal studies will increasingly be called upon to inform decisions about whether, and how best, to bring down dams. This raises a critical question: what is the current state of dam-removal science in the United States? To explore the status, trends, and characteristics of dam-removal research in the U.S., we searched the scientific literature and extracted basic information from studies on dam removal. Our literature review illustrates that although over 1200 dams have been removed in the U.S., fewer than 10% have been scientifically evaluated, and most of these studies were short in duration ( < 4 years) and had limited (1–2 years) or no pre-removal monitoring. The majority of studies focused on hydrologic and geomorphic responses to removal rather than biological and water-quality responses, and few studies were published on linkages between physical and ecological components. Our review illustrates the need for long-term, multidisciplinary case studies, with robust study designs, in order to anticipate the effects of dam removal and inform future decision making.","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1164","usgsCitation":"Bellmore, J., Duda, J.J., Craig, L., Greene, S., Torgersen, C.E., Collins, M.J., and Vittum, K., 2017, Status and trends of dam removal research in the United States: WIREs Water, v. 4, no. 2, e1164; 13 p., https://doi.org/10.1002/wat2.1164.","productDescription":"e1164; 13 p.","ipdsId":"IP-067287","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":337663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-05","publicationStatus":"PW","scienceBaseUri":"58ca52c9e4b0849ce97c868e","contributors":{"authors":[{"text":"Bellmore, James jbellmore@usgs.gov","contributorId":181550,"corporation":false,"usgs":true,"family":"Bellmore","given":"James","email":"jbellmore@usgs.gov","affiliations":[],"preferred":true,"id":668570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":668571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craig, Laura","contributorId":173675,"corporation":false,"usgs":false,"family":"Craig","given":"Laura","affiliations":[{"id":27270,"text":"American Rivers","active":true,"usgs":false}],"preferred":false,"id":668572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greene, Samantha L. sgreene@usgs.gov","contributorId":5262,"corporation":false,"usgs":true,"family":"Greene","given":"Samantha L.","email":"sgreene@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":668573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":668576,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Collins, Mathias J.","contributorId":181551,"corporation":false,"usgs":false,"family":"Collins","given":"Mathias","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":668575,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vittum, Katherine kvittum@usgs.gov","contributorId":139893,"corporation":false,"usgs":true,"family":"Vittum","given":"Katherine","email":"kvittum@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":668574,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70189965,"text":"70189965 - 2017 - A synthesis of thermokarst lake water balance in high-latitude regions of North America from isotope tracers","interactions":[],"lastModifiedDate":"2017-07-31T07:38:16","indexId":"70189965","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5363,"text":"Arctic Science","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of thermokarst lake water balance in high-latitude regions of North America from isotope tracers","docAbstract":"Numerous studies utilizing remote sensing imagery and other methods have documented that thermokarst lakes are undergoing varied hydrological transitions in response to recent climate changes, from surface area expansion to drainage and evaporative desiccation. Here, we provide a synthesis of hydrological conditions for 376 lakes of mainly thermokarst origin across high-latitude North America. We assemble surface water isotope compositions measured during the past decade at five lake-rich landscapes including Arctic Coastal Plain (Alaska), Yukon Flats (Alaska), Old Crow Flats (Yukon), northwestern Hudson Bay Lowlands (Manitoba), and Nunavik (Quebec). These landscapes represent the broad range of thermokarst environments by spanning gradients in meteorological, permafrost, and vegetation conditions. An isotope framework was established based on flux-weighted long-term averages of meteorological conditions for each lake to quantify water balance metrics. The isotope composition of source water and evaporation-to-inflow ratio for each lake were determined, and the results demonstrated a substantial array of regional and subregional diversity of lake hydrological conditions. Controls on lake water balance and how these vary among the five landscapes and with differing environmental drivers are assessed. Findings reveal that lakes in the Hudson Bay Lowlands are most vulnerable to evaporative desiccation, whereas those in Nunavik are most resilient. However, we also identify the complexity in predicting hydrological responses of these thermokarst landscapes to future climate change.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/AS-2016-0019","usgsCitation":"MacDonald, L.A., Wolfe, B.B., Turner, K.W., Anderson, L., Arp, C.D., Birks, J., Bouchard, F., Edwards, T.W., Farquharson, N., Hall, R.I., McDonald, I., Narancic, B., Ouimet, C., Pienitz, R., Tondu, J., and White, H., 2017, A synthesis of thermokarst lake water balance in high-latitude regions of North America from isotope tracers: Arctic Science, v. 3, no. 2, p. 118-149, https://doi.org/10.1139/AS-2016-0019.","productDescription":"32 p.","startPage":"118","endPage":"149","ipdsId":"IP-076403","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/as-2016-0019","text":"Publisher Index Page"},{"id":344447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5980419ae4b0a38ca2789339","contributors":{"authors":[{"text":"MacDonald, Lauren A.","contributorId":195378,"corporation":false,"usgs":false,"family":"MacDonald","given":"Lauren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":706910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfe, Brent B.","contributorId":172516,"corporation":false,"usgs":false,"family":"Wolfe","given":"Brent","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":706911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Kevin W.","contributorId":195380,"corporation":false,"usgs":false,"family":"Turner","given":"Kevin","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":706912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Lesleigh 0000-0002-5264-089X land@usgs.gov","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":436,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","email":"land@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":706909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":706913,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birks, Jean","contributorId":87856,"corporation":false,"usgs":true,"family":"Birks","given":"Jean","email":"","affiliations":[],"preferred":false,"id":706914,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bouchard, Frederic","contributorId":194639,"corporation":false,"usgs":false,"family":"Bouchard","given":"Frederic","email":"","affiliations":[],"preferred":false,"id":706915,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Edwards, Thomas W.D. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":195384,"corporation":false,"usgs":false,"family":"Edwards","given":"Thomas","email":"tce@usgs.gov","middleInitial":"W.D.","affiliations":[],"preferred":false,"id":706916,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Farquharson, Nicole","contributorId":195385,"corporation":false,"usgs":false,"family":"Farquharson","given":"Nicole","email":"","affiliations":[],"preferred":false,"id":706917,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hall, Roland I.","contributorId":168744,"corporation":false,"usgs":false,"family":"Hall","given":"Roland","email":"","middleInitial":"I.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":706918,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McDonald, Ian","contributorId":195387,"corporation":false,"usgs":false,"family":"McDonald","given":"Ian","email":"","affiliations":[],"preferred":false,"id":706919,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Narancic, Biljana","contributorId":195388,"corporation":false,"usgs":false,"family":"Narancic","given":"Biljana","email":"","affiliations":[],"preferred":false,"id":706920,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ouimet, Chantal","contributorId":195389,"corporation":false,"usgs":false,"family":"Ouimet","given":"Chantal","email":"","affiliations":[],"preferred":false,"id":706921,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pienitz, Reinhard","contributorId":195390,"corporation":false,"usgs":false,"family":"Pienitz","given":"Reinhard","email":"","affiliations":[],"preferred":false,"id":706922,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tondu, Jana","contributorId":195391,"corporation":false,"usgs":false,"family":"Tondu","given":"Jana","email":"","affiliations":[],"preferred":false,"id":706923,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"White, Hilary","contributorId":195392,"corporation":false,"usgs":false,"family":"White","given":"Hilary","email":"","affiliations":[],"preferred":false,"id":706924,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70185013,"text":"70185013 - 2017 - Using maximum entropy to predict suitable habitat for the endangered dwarf wedgemussel in the Maryland Coastal Plain","interactions":[],"lastModifiedDate":"2017-04-19T16:09:34","indexId":"70185013","displayToPublicDate":"2017-03-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Using maximum entropy to predict suitable habitat for the endangered dwarf wedgemussel in the Maryland Coastal Plain","docAbstract":"Carbon capture from stationary sources and geologic storage of carbon dioxide (CO2) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO2 storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference, and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO2, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO2 storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO2 injection will be mitigated by reservoir pressure management, estimates of the costs of CO2 storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO2 storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO2 storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the scarcity of (data from) long-term commercial-scale CO2 storage projects, decision makers may experience considerable difficulty in ascertaining the realistic potential, the likely costs, and the most beneficial pattern of deployment of CCS as an option to reduce CO2 concentrations in the atmosphere.
","language":"English","publisher":"Springer","doi":"10.1007/s11053-016-9310-7","usgsCitation":"Anderson, S.T., 2017, Cost implications of uncertainty in CO2 storage resource estimates: A review: Natural Resources Research, v. 26, no. 2, p. 137-159, https://doi.org/10.1007/s11053-016-9310-7.","productDescription":"23 p.","startPage":"137","endPage":"159","ipdsId":"IP-069500","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":470014,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11053-016-9310-7","text":"Publisher Index Page"},{"id":337513,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-30","publicationStatus":"PW","scienceBaseUri":"58c90122e4b0849ce97abca7","contributors":{"authors":[{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":683988,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70185066,"text":"70185066 - 2017 - Similarities and differences in occurrence and temporal fluctuations in glyphosate and atrazine in small Midwestern streams (USA) during the 2013 growing season","interactions":[],"lastModifiedDate":"2018-09-25T09:12:29","indexId":"70185066","displayToPublicDate":"2017-03-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Similarities and differences in occurrence and temporal fluctuations in glyphosate and atrazine in small Midwestern streams (USA) during the 2013 growing season","docAbstract":"Glyphosate and atrazine are the most intensively used herbicides in the United States. Although there is abundant spatial and temporal information on atrazine occurrence at regional scales, there are far fewer data for glyphosate, and studies that compare the two herbicides are rare. We investigated temporal patterns in glyphosate and atrazine concentrations measured weekly during the 2013 growing season in 100 small streams in the Midwestern United States. Glyphosate was detected in 44% of samples (method reporting level 0.2 μg/L); atrazine was detected above a threshold of 0.2 μg/L in 54% of samples. Glyphosate was detected more frequently in 12 urban streams than in 88 agricultural streams, and at concentrations similar to those in streams with high agricultural land use (> 40% row crop) in the watershed. In contrast, atrazine was detected more frequently and at higher concentrations in agricultural streams than in urban streams. The maximum concentration of glyphosate measured at most urban sites exceeded the maximum atrazine concentration, whereas at agricultural sites the reverse was true. Measurement at a 2-day interval at 8 sites in northern Missouri revealed that transport of both herbicide compounds appeared to be controlled by spring flush, that peak concentration duration was brief, but that peaks in atrazine concentrations were of longer duration than those of glyphosate. The 2-day sampling also indicated that weekly sampling is unlikely to capture peak concentrations of glyphosate and atrazine.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.10.236","usgsCitation":"Mahler, B., Van Metre, P., Burley, T.E., Loftin, K.A., Meyer, M.T., and Nowell, L.H., 2017, Similarities and differences in occurrence and temporal fluctuations in glyphosate and atrazine in small Midwestern streams (USA) during the 2013 growing season: Science of the Total Environment, v. 579, p. 149-158, https://doi.org/10.1016/j.scitotenv.2016.10.236.","productDescription":"10 p.","startPage":"149","endPage":"158","ipdsId":"IP-076521","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":470016,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.10.236","text":"Publisher Index Page"},{"id":438416,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SN073J","text":"USGS data release","linkHelpText":"Concentrations of glyphosate and atrazine compounds in 100 Midwest United States streams in 2013"},{"id":337489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"579","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c90121e4b0849ce97abc9e","contributors":{"authors":[{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Metre, Peter C. 0000-0001-7564-9814 pcvanmet@usgs.gov","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":172246,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter C.","email":"pcvanmet@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":false,"id":684177,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burley, Thomas E. 0000-0002-2235-8092 teburley@usgs.gov","orcid":"https://orcid.org/0000-0002-2235-8092","contributorId":3499,"corporation":false,"usgs":true,"family":"Burley","given":"Thomas","email":"teburley@usgs.gov","middleInitial":"E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":684181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":684178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Michael T. 0000-0001-6006-7985 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6006-7985","contributorId":866,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"T.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":684179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - 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However, while 21st century temperatures are projected to increase in the Caribbean, the low variability and high average temperatures suggest that impacts on ecosystems and water resources are more likely through changes to the availability, timing, and pattern of moisture. The lack of local-scale climate model information that can resolve the complex topography and small scale climate features hinders the development of robust adaptation strategies. The goal of this project was to develop a suite of local-scale climate projections using dynamic downscaling to aid the development of adaptation strategies in Puerto Rico and the U.S. Virgin Islands (USVI). This project began by engaging the ecologists, hydrologists, and conservation biologists in the region to determine the most valuable types of information to aid research and decision making. The final product provides projections of future climate at a 2km horizontal resolution based on three global climate models and two regional climate models for a scenario with high greenhouse gas emissions. Results from the projections suggest that for Puerto Rico, annual temperature would increase between 1°C and 1.3°C by mid-century with larger temperature increases located in the interior portion of the island. Precipitation totals decrease for much of the island with island average decline between 12% and 19%, with some potentially large localized decreases exceeding 30%. The projected changes for the USVI are dominated by the surrounding ocean environment. The resulting projections will be provided to stakeholders in the region via the USGS and the CLCC.","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Terando, A., 2017, Developing multi-model ensemble projections of ecologically relevant climate variables for Puerto Rico and the US Caribbean: Final Project Memorandum 557-271, 20 p.","productDescription":"20 p.","ipdsId":"IP-085236","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":381228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":346905,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/wp-content/uploads/sites/14/2020/01/020-Final-Memo-Terando.pdf"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"US Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.181884765625,\n 18.07275691457901\n ],\n [\n -65.203857421875,\n 18.3858049312974\n ],\n [\n -66.588134765625,\n 18.646245142670608\n ],\n [\n -67.291259765625,\n 18.594188856740413\n ],\n [\n -67.39013671875,\n 18.15629140283545\n ],\n [\n -67.060546875,\n 17.78007412664325\n ],\n [\n -65.599365234375,\n 17.895114303749143\n ],\n [\n -65.181884765625,\n 18.07275691457901\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.43756103515625,\n 17.712060974461494\n ],\n [\n -64.64630126953125,\n 18.367559302479318\n ],\n [\n -64.7479248046875,\n 18.404048629104647\n ],\n [\n -64.9017333984375,\n 18.474399059267128\n ],\n [\n -65.0665283203125,\n 18.432713391700858\n ],\n [\n -65.1214599609375,\n 18.34931174429646\n ],\n [\n -65.07202148437499,\n 17.63616972425169\n ],\n [\n -64.76165771484375,\n 17.589048722297875\n ],\n [\n -64.43756103515625,\n 17.712060974461494\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Terando, Adam 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":197511,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713675,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70185060,"text":"70185060 - 2017 - Geochemistry of host rocks in the Howards Pass district, Yukon-Northwest Territories, Canada: implications for sedimentary environments of Zn-Pb and phosphate mineralization","interactions":[],"lastModifiedDate":"2017-03-22T14:41:40","indexId":"70185060","displayToPublicDate":"2017-03-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of host rocks in the Howards Pass district, Yukon-Northwest Territories, Canada: implications for sedimentary environments of Zn-Pb and phosphate mineralization","docAbstract":"Detailed lithogeochemical data are reported here on early Paleozoic sedimentary rocks that host the large Howards Pass stratiform Zn-Pb deposits in Yukon-Northwest Territories. Redox-sensitive trace elements (Mo, Re, V, U) and Ce anomalies in members of the Duo Lake Formation record significant environmental changes. During the deposition of lower footwall units (Pyritic siliceous and Calcareous mudstone members), bottom waters were anoxic and sulphidic, respectively; these members formed in a marginal basin that may have become increasingly restricted with time. Relative to lower members, a major environmental change is proposed for deposition of the overlying Lower cherty mudstone member, which contains phosphorite beds up to ∼0.8 m thick in the upper part, near the base of the Zn-Pb deposits. The presence of these beds, together with models for modern phosphorite formation, suggests P input from an upwelling system and phosphorite deposition in an upper slope or outer shelf setting. The overlying Active mudstone member contains stratabound to stratiform Zn-Pb deposits within black mudstone and gray calcareous mudstone. Data for unmineralized black mudstone in this member indicate deposition under diverse redox conditions from suboxic to sulphidic. Especially distinctive in this member are uniformly low ratios of light to heavy rare earth elements that are unique within the Duo Lake Formation, attributed here to the dissolution of sedimentary apatite by downward-percolating acidic metalliferous brines. Strata that overlie the Active member (Upper siliceous mudstone member) consist mainly of black mudstone with thin (0.5–1.5 cm) laminae of fine-grained apatite, recording continued deposition on an upper slope or outer shelf under predominantly suboxic bottom waters. Results of this study suggest that exploration for similar stratiform sediment-hosted Zn-Pb deposits should include the outer parts of ancient continental margins, especially at and near stratigraphic transitions from marginal basin facies to overlying slope or shelf facies.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-016-0680-x","usgsCitation":"Slack, J.F., Falck, H., Kelley, K.D., and Xue, G.G., 2017, Geochemistry of host rocks in the Howards Pass district, Yukon-Northwest Territories, Canada: implications for sedimentary environments of Zn-Pb and phosphate mineralization: Mineralium Deposita, v. 52, no. 4, p. 565-593, https://doi.org/10.1007/s00126-016-0680-x.","productDescription":"29 p.","startPage":"565","endPage":"593","ipdsId":"IP-076693","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":337465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-05","publicationStatus":"PW","scienceBaseUri":"58c7af9be4b0849ce9795e72","contributors":{"authors":[{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":684113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falck, Hendrik","contributorId":167705,"corporation":false,"usgs":false,"family":"Falck","given":"Hendrik","email":"","affiliations":[{"id":24811,"text":"NWT Geoscience Office, Yellowknife, Canada","active":true,"usgs":false}],"preferred":false,"id":684114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Karen D. kdkelley@usgs.gov","contributorId":431,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":684115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xue, Gabriel G.","contributorId":189206,"corporation":false,"usgs":false,"family":"Xue","given":"Gabriel","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":684116,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188624,"text":"70188624 - 2017 - Albedo feedbacks to future climate via climate change impacts on dryland biocrusts","interactions":[],"lastModifiedDate":"2017-06-19T11:23:01","indexId":"70188624","displayToPublicDate":"2017-03-10T00:00:00","publicationYear":"2017","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":"Albedo feedbacks to future climate via climate change impacts on dryland biocrusts","docAbstract":"Drylands represent the planet’s largest terrestrial biome and evidence suggests these landscapes have large potential for creating feedbacks to future climate. Recent studies also indicate that dryland ecosystems are responding markedly to climate change. Biological soil crusts (biocrusts) ‒ soil surface communities of lichens, mosses, and/or cyanobacteria ‒ comprise up to 70% of dryland cover and help govern fundamental ecosystem functions, including soil stabilization and carbon uptake. Drylands are expected to experience significant changes in temperature and precipitation regimes, and such alterations may impact biocrust communities by promoting rapid mortality of foundational species. In turn, biocrust community shifts affect land surface cover and roughness—changes that can dramatically alter albedo. We tested this hypothesis in a full-factorial warming (+4 °C above ambient) and altered precipitation (increased frequency of 1.2 mm monsoon-type watering events) experiment on the Colorado Plateau, USA. We quantified changes in shortwave albedo via multi-angle, solar-reflectance measurements. Warming and watering treatments each led to large increases in albedo (>30%). This increase was driven by biophysical factors related to treatment effects on cyanobacteria cover and soil surface roughness following treatment-induced moss and lichen mortality. A rise in dryland surface albedo may represent a previously unidentified feedback to future climate.
","language":"English","publisher":"Springer Nature","doi":"10.1038/srep44188","usgsCitation":"Rutherford, W.A., Painter, T.H., Ferrenberg, S., Belnap, J., Okin, G.S., Flagg, C.B., and Reed, S.C., 2017, Albedo feedbacks to future climate via climate change impacts on dryland biocrusts: Scientific Reports, v. 7, Article 44188: 9 p., https://doi.org/10.1038/srep44188.","productDescription":"Article 44188: 9 p.","ipdsId":"IP-079895","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470019,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/srep44188","text":"Publisher Index Page"},{"id":342637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","city":"Castle Valley","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.393889,\n 38.617778\n ],\n [\n -109.427222,\n 38.617778\n ],\n [\n -109.427222,\n 38.651111\n ],\n [\n -109.393889,\n 38.651111\n ],\n [\n -109.393889,\n 38.617778\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-10","publicationStatus":"PW","scienceBaseUri":"5948e2a6e4b062508e354c6e","contributors":{"authors":[{"text":"Rutherford, William A. wrutherford@usgs.gov","contributorId":5724,"corporation":false,"usgs":true,"family":"Rutherford","given":"William","email":"wrutherford@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Painter, Thomas H.","contributorId":12378,"corporation":false,"usgs":true,"family":"Painter","given":"Thomas","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":698647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrenberg, Scott 0000-0002-3542-0334 sferrenberg@usgs.gov","orcid":"https://orcid.org/0000-0002-3542-0334","contributorId":147684,"corporation":false,"usgs":true,"family":"Ferrenberg","given":"Scott","email":"sferrenberg@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Okin, Gregory S.","contributorId":50025,"corporation":false,"usgs":true,"family":"Okin","given":"Gregory","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":698650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flagg, Cody B. cflagg@usgs.gov","contributorId":4573,"corporation":false,"usgs":true,"family":"Flagg","given":"Cody","email":"cflagg@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698651,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698645,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70184404,"text":"ofr20171025 - 2017 - Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia","interactions":[],"lastModifiedDate":"2017-03-14T09:45:46","indexId":"ofr20171025","displayToPublicDate":"2017-03-10T00:00:00","publicationYear":"2017","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":"2017-1025","title":"Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia","docAbstract":"Between 1997 and 2011, Mongolia established three specially protected areas in the north-central part of the country to protect various high-value resources. These areas are jointly referred to as the Ulaan Taiga Specially Protected Areas. In accordance with the goals of the draft general management plan, this report identifies options for initiating an inventory and monitoring program for the three protected areas. Together, the three areas comprise over 1.5 million hectares of mountainous terrain west of Lake Hovsgol and bordering the Darkhad Valley. The area supports numerous rare ungulates, endangered fish, and over 40 species of threatened plants. Illegal mining, illegal logging, and poaching pose the most immediate threats to resources. As a first step, a review of published literature would inform natural resource management at the Ulaan Taiga Specially Protected Areas because it would inform other inventories.
Vegetation classification and mapping also would inform other inventory efforts because the process incorporates geographic analysis to identify environmental gradients, fine-scale sampling that captures species composition and structure, and landscape-scale results that represent the variety and extent of habitats for various organisms. Mapping using satellite imagery reduces the cost per hectare.
Following a determination of existing knowledge, field surveys of vertebrates and vascular plants would serve to build species lists and fill in gaps in existing knowledge. For abiotic resources, a focus on monitoring air quality, evaluating and monitoring water quality, and assembling and storing weather data would provide information for correlating resource response status with changing environmental conditions.
Finally, we identify datasets that, if incorporated into a geographic information system, would inform resource management. They include political boundaries, infrastructure, topography, surficial geology, hydrology, fire history, and soils.
In terms of tracking high-value resources, vegetation monitoring at the plot scale would provide a basis for detecting change in such characteristics as plant species composition, vegetation structure, and productivity that are associated with landscape-scale factors such as climate change or biotic interactions. Continued population monitoring of rare ungulates, particularly argali or wild sheep (Ovis ammon), would provide information on how populations are responding to natural and anthropogenic stressors. Siberian taimen (Hucho taimen) also is an important monitoring target given ongoing threats of poaching and climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171025","usgsCitation":"Moore, P.E., Meyer, J.B., and Chow, L.S., 2017, Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia: U.S. Geological Survey Open-File Report 2017–1025, 35 p., https://doi.org/10.3133/ofr20171025.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-082861","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":337345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1025/coverthb.jpg"},{"id":337346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1025/ofr20171025.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1025"}],"country":"Mongolia","otherGeospatial":"Ulaan Taiga Specially Protected Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.55859375,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 49.89463439573421\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
This paper presents a conceptual framework to operationalize flow–ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow–ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow–ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.
","language":"English","publisher":"MDPI","doi":"10.3390/w9030196","usgsCitation":"Cartwright, J.M., Caldwell, C., Nebiker, S., and Knight, R., 2017, Putting flow-ecology relationships into practice: A decision-support system to assess fish community response to water-management scenarios: Water, v. 9, no. 3, p. 1-18, https://doi.org/10.3390/w9030196.","productDescription":"Article 196; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-076084","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":470021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w9030196","text":"Publisher Index Page"},{"id":337171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-08","publicationStatus":"PW","scienceBaseUri":"58c277d5e4b014cc3a3e76a9","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Casey","contributorId":187734,"corporation":false,"usgs":false,"family":"Caldwell","given":"Casey","email":"","affiliations":[],"preferred":false,"id":681523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nebiker, Steven","contributorId":187735,"corporation":false,"usgs":false,"family":"Nebiker","given":"Steven","email":"","affiliations":[],"preferred":false,"id":681524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Rodney 0000-0001-9588-0167 rrknight@usgs.gov","orcid":"https://orcid.org/0000-0001-9588-0167","contributorId":152422,"corporation":false,"usgs":true,"family":"Knight","given":"Rodney","email":"rrknight@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":681525,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184443,"text":"ds1038 - 2017 - Groundwater-quality data in 12 GAMA study units: Results from the 2006–10 initial sampling period and the 2008–13 trend sampling period, California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2017-03-10T13:57:25","indexId":"ds1038","displayToPublicDate":"2017-03-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1038","title":"Groundwater-quality data in 12 GAMA study units: Results from the 2006–10 initial sampling period and the 2008–13 trend sampling period, California GAMA Priority Basin Project","docAbstract":"The Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) program was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey in cooperation with the California State Water Resources Control Board. From 2004 through 2012, the GAMA-PBP collected samples and assessed the quality of groundwater resources that supply public drinking water in 35 study units across the State. Selected sites in each study unit were sampled again approximately 3 years after initial sampling as part of an assessment of temporal trends in water quality by the GAMA-PBP. Twelve of the study units, initially sampled during 2006–11 (initial sampling period) and sampled a second time during 2008–13 (trend sampling period) to assess temporal trends, are the subject of this report.
The initial sampling was designed to provide a spatially unbiased assessment of the quality of untreated groundwater used for public water supplies in the 12 study units. In these study units, 550 sampling sites were selected by using a spatially distributed, randomized, grid-based method to provide spatially unbiased representation of the areas assessed (grid sites, also called “status sites”). After the initial sampling period, 76 of the previously sampled status sites (approximately 10 percent in each study unit) were randomly selected for trend sampling (“trend sites”). The 12 study units sampled both during the initial sampling and during the trend sampling period were distributed among 6 hydrogeologic provinces: Coastal (Northern and Southern), Transverse Ranges and Selected Peninsular Ranges, Klamath, Modoc Plateau and Cascades, and Sierra Nevada Hydrogeologic Provinces. For the purposes of this trend report, the six hydrogeologic provinces were grouped into two hydrogeologic regions based on location: Coastal and Mountain.
The groundwater samples were analyzed for a number of synthetic organic constituents (volatile organic compounds, pesticides, and pesticide degradates), constituents of special interest (perchlorate and 1,2,3-trichloropropane), and natural inorganic constituents (nutrients, major and minor ions, and trace elements). Isotopic tracers (tritium, carbon-14, and stable isotopes of hydrogen and oxygen in water) also were measured to help identify processes affecting groundwater quality and the sources and ages of the sampled groundwater. More than 200 constituents and water-quality indicators were measured during the trend sampling period.
Quality-control samples (blanks, replicates, matrix-spikes, and surrogate compounds) were collected at about one-third of the trend sites, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. On the basis of detections in laboratory and field blank samples collected by GAMA-PBP study units, including the 12 study units presented here, reporting levels for some groundwater results were adjusted in this report. Differences between replicate samples were mostly within acceptable ranges, indicating low variability in analytical results. Matrix-spike recoveries were largely within the acceptable range (70 to 130 percent).
This study did not attempt to evaluate the quality of water delivered to consumers. After withdrawal, groundwater used for drinking water typically is treated, disinfected, and blended with other waters to achieve acceptable water quality. The comparison benchmarks used in this report apply to treated water that is served to the consumer, not to untreated groundwater. To provide some context for the results, however, concentrations of constituents measured in these groundwater samples were compared with benchmarks established by the U.S. Environmental Protection Agency and the State of California. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.
Most organic constituents that were detected in groundwater samples from the trend sites were found at concentrations less than health-based benchmarks. One volatile organic compound—perchloroethene—was detected at a concentration greater than the health-based benchmark in samples from one trend site during the initial and trend sampling periods. Chloroform was detected in at least 10 percent of the samples at trend sites in both sampling periods. Methyl tert-butyl ether was detected in samples from more than 10 percent of the trend sites during the initial sampling period. No pesticide or pesticide degradate was detected in greater than 10 percent of the samples from trend sites or at concentrations greater than their health-based benchmarks during either sampling period. Nutrients were not detected at concentrations greater than their health-based benchmarks during either sampling period.
Most detections of major ions and trace elements in samples from trend sites were less than health-based benchmarks during both sampling periods. Arsenic and boron each were detected at concentrations greater than the health-based benchmark in samples from four trend sites during the initial and trend sampling periods. Molybdenum was detected in samples from four trend sites at concentrations greater than the health-based benchmark during both sampling periods. Samples from two of these trend sites had similar molybdenum concentrations, and two had substantially different concentrations during the initial and trend sampling periods. Uranium was detected at a concentration greater than the health-based benchmark only at two trend sites.
","language":"English","publisher":"U.S. Geological Servey","publisherLocation":"Reston, VA","doi":"10.3133/ds1038","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Mathany, T.M., 2017, Groundwater-quality data in 12 GAMA study units: Results from the 2006–10 initial sampling period and the 2008–13 trend sampling period, California GAMA Priority Basin Project: U.S. Geological Survey Data Series Report 1038, 140 p., https://dx.doi.org/10.3133/ds1038.","productDescription":"x, 140 p.","numberOfPages":"154","onlineOnly":"Y","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":337146,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1038/ds1038.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 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\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
http://ca.water.usgs.gov
The Southeast Missouri Barite District and the Valles Mines are located in Washington, Jefferson, and St. Francois Counties, Missouri, where barite and lead ore are present together in surficial and near-surface deposits. Lead mining in the area began in the early 1700’s and extended into the early 1900’s. Hand mining of lead in the residuum resulted in widespread pits (also called shafts or diggings), and there was some underground mining of lead in bedrock. By the 1860’s barite was recovered from the residuum by hand mining, also resulting in widespread diggings, but generally not underground mines in bedrock. Mechanized open-pit mining of the residuum for barite began in the 1920’s. Barite production slowed by the 1980’s, and there has not been any barite mining since 1998. Mechanized barite mining resulted in large mined areas and tailings ponds containing waste from barite mills.
The U.S. Environmental Protection Agency (EPA) has determined that lead is present in surface soils in Washington and Jefferson Counties at concentrations exceeding health-based screening levels. Also, elevated concentrations of barium, arsenic, and cadmium have been identified in surface soils, and lead concentrations exceeding the Federal drinking-water standard of 15 micrograms per liter have been identified in private drinking-water wells. Potential sources of these contaminants are wastes associated with barite mining, wastes associated with lead mining, or unmined natural deposits of barium, lead, and other metals. As a first step in helping EPA determine the source of soil and groundwater contamination, the U.S. Geological Survey (USGS), in cooperation with the EPA, investigated the geology and mining history of the Southeast Missouri Barite District and the Valles Mines.
Ore minerals are barite (barium sulfate), galena (lead sulfide), cerussite (lead carbonate), anglesite (lead sulfate), sphalerite (zinc sulfide), smithsonite (zinc carbonate), and chalcopyrite (copper-iron sulfide). The Cambrian Potosi Dolomite is the most important formation for the ore deposits, followed by the Eminence Dolomite. Because galena, sphalerite, and barite are less soluble than dolomite, chemical weathering of the ore-bearing dolomite bedrock resulted in the concentration of ore minerals in the residuum. Most of the barite and lead mining was in the residuum, which averages 10 to 15 feet thick.
Lead mining by French explorers may have begun in 1719 along Old Mines Creek at Cabanage de Renaudiere, which was followed shortly by the discovery of lead and the development of lead mines at Mine Renault (also called Forche a Renault Mine), Old Mines, and at other places along the Big River, Mineral Fork, and Forche a Renault Creek. Lead mining began sometime between 1775 and 1780 at Mine a Breton, the name of which was later changed to Potosi. Other mining areas were developed in the early part of the 19th century, including Fourche a Courtois (Palmer Mines), the French Diggings, and the Richwoods Mines. Zinc became a valuable resource after the Civil War, and the Valles Mines was an important supplier of zinc as well as lead, with at least some production up until the 1920’s. Lead mining declined in the early part of the 20th century as mining in the Old Lead Belt, Mine La Motte, and the Tri-State District expanded.
The earliest lead mines were diggings in the residuum and were round holes (shafts) about 4 feet in diameter dug with pick and shovel about 15–20 feet deep, with drifts dug a short distance laterally from the bottom of the shafts. This mining process was repeated a short distance away until a large area was covered with pits. Some mining in bedrock began by about 1800, with shafts as deep as 170 feet and as much as several hundred feet of lateral drifts.
Smelting of the lead ore to elemental lead was first done using a log furnace, which was inefficient; estimates have been made that only about 50 percent of the lead was recovered, and the remainder was lost to the ashes (slags) and to volatilization. Starting in 1798, ash furnaces were used to smelt the ashes from the log furnaces. These two furnaces were worked in tandem for many years but were gradually replaced by other furnaces, including the Scotch hearth. Estimates of lead recovery as high as 80–90 percent have been made for the Scotch hearth. By the mid-1870’s the air furnace was being used, also with estimated lead recovery as high as 80–90 percent. Zinc furnaces were built when zinc became a valuable commodity, but much of the zinc ore was shipped out of the area, either to a smelter in St. Louis, Missouri, or to other smelters.
The total lead and zinc production from the Southeast Missouri Barite District and the Valles Mines is estimated at 180,000 tons of lead and 60,000 tons of zinc. An estimated 97,000 tons of lead and an estimated 120,000 tons of zinc were lost during smelting. The estimated losses do not include losses at the mine site during mining and preparation for smelting, such as the loss of fine-grained galena during hand cleaning or the discarding of zinc ore before its value was known, for which no estimates are available.
Hand mining for barite in the residuum was active by at least the 1860’s and peaked from 1905 to the 1930’s when several thousand people were engaged in barite mining. Hand mining (diggings) and cleaning of the ore was done in much the same way as earlier lead mining, with the additional use of a rattle box to further clean the barite. Mechanized open-pit mining of old barite diggings began in 1924 to recover barite left behind by hand mining, and washing plants were used to clean the clay from the barite. Hand mining, however, continued to thrive, and washer plants began to close temporarily in 1931; nearly all of the barite produced before 1937 was by hand mining. By the 1940’s, however, all barite mining was mechanized.
Mechanized mining used shovels powered by steam, gasoline, or electricity (and by the 1950’s draglines and front-end loaders) to mine the residuum. The ore was loaded onto rail cars (and by the 1940’s, trucks) for shipment to washer plants. Clay was removed from the barite using a log washer, and a jig was used to concentrate the barite. Overflow from the log washers was waste and went to a mud (tailings) pond. The coarse jig tailings went to tailings piles or were used as railroad ballast and, later, to create roads within the mine pit. Some barite was ground, depending on its final use, and some ground barite was bleached using a hot solution of sulfuric acid to remove impurities such as iron minerals and lead sulfide (galena). An earlier bleaching process used lead-lined tanks.
Large quantities of water were required for milling the barite; some was recirculated water and the remainder came from dammed streams or was pumped from wells. Tailings and wastewater were impounded behind dikes that were built across small valleys and were increased in height as necessary using washer waste and any overburden that had been stripped. In some cases, dikes were built across valleys that had already been mined for barite.
The total production of barite from the Southeast Missouri Barite District and the Valles Mines is estimated to have been about 13.1 million tons. Most of the barite production was from Washington County. Hand mining and processing of barite was inefficient. Estimates of barite recovery range from less than one-fourth to about one-half because pillars between the shafts in the residuum needed to be left unmined for stability. With mechanized mining, large amounts of barite were lost during the milling process. It has been estimated that about 30 percent of the barite was lost and that about two-thirds of the lost barite was fine-grained and was discharged to the tailings ponds. Some galena was lost to the tailings ponds.
A 1972 inventory of tailings ponds by the Missouri Geological Survey identified 67 ponds in the Southeast Missouri Barite District (there are more than this currently documented). Results from samples from four ponds that were drilled were used to estimate that the 67 ponds contained almost 39 million tons (or cubic yards) of tailings averaging about 5 percent barite, for a potential reserve of 1.935 million tons of barite.
It is not known how much lead was removed during barite mining, either by hand or mechanized mining and processing, how much lead was recovered, or how much lead went as fines to the tailing ponds or as coarse material to mine roads or was otherwise lost.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165173","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Mugel, D.N., 2017, Geology and mining history of the Southeast Missouri Barite District and the Valles Mines, Washington, Jefferson, and St. Francois Counties, Missouri: U.S. Geological Survey Scientific Investigations Report 2016–5173, 61 p., https://doi.org/10.3133/sir20165173.","productDescription":"vi, 61 p.","numberOfPages":"72","onlineOnly":"N","ipdsId":"IP-076644","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":337151,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5173/coverthb.jpg"},{"id":337152,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5173/sir20165173.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5173"}],"country":"United States","state":"Missouri","county":"Jefferson County, St. Francois County, Washington County","otherGeospatial":"Southeast Missouri Barite District, Valles Mines","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-90.3435,38.3872],[-90.3467,38.3829],[-90.3507,38.3774],[-90.3536,38.372],[-90.3565,38.367],[-90.3566,38.362],[-90.3579,38.357],[-90.3598,38.3516],[-90.3617,38.3471],[-90.3648,38.3422],[-90.3673,38.3372],[-90.3698,38.3319],[-90.3713,38.3274],[-90.3724,38.3224],[-90.3723,38.3179],[-90.3728,38.3116],[-90.3729,38.307],[-90.3727,38.3011],[-90.3731,38.288],[-90.373,38.278],[-90.37,38.269],[-90.3682,38.2622],[-90.3663,38.255],[-90.3645,38.2486],[-90.3624,38.2424],[-90.3603,38.2356],[-90.3564,38.2264],[-90.3524,38.2197],[-90.3498,38.2141],[-90.3453,38.208],[-90.3413,38.2031],[-90.3373,38.1989],[-90.3334,38.1949],[-90.3292,38.1904],[-90.3222,38.1846],[-90.3185,38.1823],[-90.3133,38.1805],[-90.3092,38.1787],[-90.3028,38.177],[-90.297,38.1751],[-90.2904,38.1725],[-90.2839,38.1689],[-90.2792,38.1652],[-90.2745,38.1604],[-90.2709,38.1559],[-90.2673,38.1501],[-90.2634,38.1441],[-90.256,38.1333],[-90.2504,38.1255],[-90.2493,38.1239],[-90.2512,38.123],[-90.2513,38.1198],[-90.2554,38.1195],[-90.259,38.115],[-90.2633,38.1096],[-90.2674,38.1088],[-90.2756,38.1094],[-90.2785,38.109],[-90.281,38.1045],[-90.287,38.0983],[-90.2925,38.0925],[-90.2972,38.0898],[-90.303,38.0922],[-90.31,38.0946],[-90.3163,38.0974],[-90.3227,38.0975],[-90.3315,38.0995],[-90.4137,38.0447],[-90.3254,37.9861],[-90.4574,37.8823],[-90.2396,37.6998],[-90.2203,37.6854],[-90.2027,37.6701],[-90.1535,37.7004],[-90.1114,37.6715],[-90.1489,37.6423],[-90.2204,37.6423],[-90.5349,37.6441],[-90.5907,37.6441],[-90.6483,37.6437],[-90.6454,37.7365],[-90.7687,37.7393],[-90.8159,37.7381],[-90.8747,37.7362],[-90.874,37.7403],[-90.8892,37.7378],[-90.8921,37.7369],[-91.1004,37.7431],[-91.0925,38.005],[-91.0963,38.2072],[-90.999,38.2079],[-90.9702,38.2111],[-90.9703,38.2084],[-90.7788,38.2077],[-90.7386,38.4184],[-90.7358,38.4832],[-90.7282,38.4821],[-90.7194,38.4811],[-90.7165,38.4802],[-90.7172,38.4734],[-90.7161,38.4729],[-90.7125,38.4733],[-90.7096,38.4751],[-90.7037,38.4763],[-90.6967,38.474],[-90.6898,38.4653],[-90.6875,38.4625],[-90.6888,38.4585],[-90.69,38.4567],[-90.6889,38.4557],[-90.6818,38.4574],[-90.68,38.4565],[-90.683,38.4543],[-90.6837,38.4511],[-90.6802,38.4497],[-90.6743,38.4505],[-90.6589,38.4571],[-90.6547,38.4598],[-90.6546,38.4638],[-90.6574,38.4698],[-90.6596,38.4734],[-90.6594,38.4811],[-90.6569,38.4883],[-90.6516,38.4905],[-90.6456,38.4927],[-90.6398,38.4922],[-90.6328,38.4884],[-90.6282,38.4847],[-90.623,38.481],[-90.6189,38.4787],[-90.6143,38.4773],[-90.6084,38.4785],[-90.6041,38.4839],[-90.5998,38.4902],[-90.5967,38.4951],[-90.5918,38.5032],[-90.5906,38.5064],[-90.4089,38.5039],[-90.406,38.5016],[-90.4061,38.4984],[-90.4079,38.4962],[-90.4075,38.4912],[-90.4052,38.4884],[-90.4083,38.4839],[-90.4119,38.4822],[-90.4125,38.4822],[-90.4137,38.4813],[-90.4154,38.4818],[-90.4201,38.4837],[-90.4218,38.4832],[-90.4224,38.4833],[-90.4224,38.4824],[-90.4214,38.4774],[-90.4215,38.4751],[-90.4159,38.4627],[-90.4102,38.4581],[-90.4067,38.4567],[-90.4026,38.4571],[-90.399,38.4593],[-90.3954,38.461],[-90.3919,38.4601],[-90.3903,38.4569],[-90.3868,38.455],[-90.3773,38.4562],[-90.3602,38.46],[-90.356,38.4617],[-90.3466,38.4616],[-90.3443,38.4606],[-90.3402,38.4592],[-90.3368,38.456],[-90.3375,38.4496],[-90.3442,38.4434],[-90.3474,38.4348],[-90.3473,38.4194],[-90.3467,38.4167],[-90.3488,38.4072],[-90.345,38.3972],[-90.3451,38.3935],[-90.3435,38.3872]]]},\"properties\":{\"name\":\"Jefferson\",\"state\":\"MO\"}}]}","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.
Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.
Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3377","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., Gronberg, J.M., 2017, Predicted pH at the domestic and public supply drinking water depths, Central Valley, California: U.S. Geological Survey Scientific Investigations Map 3377, 1 sheet, scale 1:2,400,000, https://doi.org/10.3133/sim3377.","productDescription":"Sheet: 19.00 x 21.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-079912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336887,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77K4","text":"USGS data release","description":"USGS data release","linkHelpText":"Ascii grids of predicted pH in depth zones used by domestic and public drinking water supply depths, Central Valley, California."},{"id":336878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3377/coverthb.jpg"},{"id":336879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3377/sim3377.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3377"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.22290039062499,\n 40.75557964275589\n ],\n [\n -122.958984375,\n 40.38839687388361\n ],\n [\n -122.574462890625,\n 39.32579941789298\n ],\n [\n -122.08007812499999,\n 38.07404145941957\n ],\n [\n -120.7177734375,\n 36.77409249464195\n ],\n [\n -119.83886718750001,\n 35.33529320309328\n ],\n [\n -119.267578125,\n 34.912962495216966\n ],\n [\n -118.740234375,\n 35.110921809704756\n ],\n [\n -118.740234375,\n 35.8356283888737\n ],\n [\n -118.91601562499999,\n 36.359374956015856\n ],\n [\n -119.84985351562499,\n 37.32648861334206\n ],\n [\n -120.82763671875,\n 38.24680876017446\n ],\n [\n -121.39892578125,\n 39.2492708462234\n ],\n [\n -122.1240234375,\n 40.53050177574321\n ],\n [\n -122.22290039062499,\n 40.75557964275589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
http://ca.water.usgs.gov/
Knowledge of how invasive species use invaded habitats can aid in developing management practices to exclude them. Swan Lake, a 1100-ha Illinois River (USA) backwater, was rehabilitated to restore ecosystem functions, but may provide valuable habitat for invasive bigheaded carps [bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix)]. Use (residency and passages) of Swan Lake by invasive bigheaded carps was monitored using acoustic telemetry (n = 50 individuals/species) to evaluate the use of a large, restored habitat from 2004 to 2005. Passages (entrances/exits) by bigheaded carps were highest in winter, and residency was highest in the summer. Bighead carp backwater use was associated with the differences in temperature between the main channel and backwater, and passages primarily occurred between 18:00 h and midnight. Silver carp backwater use was positively correlated with water level and main channel discharge, and fewer passages occurred between 12:00 h and 18:00 h than during any other time of day. Harvest occurring during summer or high main channel discharge could reduce backwater abundances while maintenance of low water levels could reduce overall backwater use. Conclusions from this study regarding the timing of bigheaded carps' use of backwater habitats are critical to integrated pest management plans to control invasive species.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3122","usgsCitation":"Coulter, A.A., Schultz, D., Tristano, E., Brey, M.K., and Garvey, J.E., 2017, Restoration versus invasive species: Bigheaded carps’ use of a rehabilitated backwater: River Research and Applications, v. 33, no. 5, p. 662-669, https://doi.org/10.1002/rra.3122.","productDescription":"8 p.","startPage":"662","endPage":"669","ipdsId":"IP-072124","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":438423,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HH6J1S","text":"USGS data release","linkHelpText":"Remotely sensed variables analyzed and reported in the paper titled &amp;amp;amp;quot;Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate&amp;amp;amp;rsquo;s effects on wetland surface water, amphibians, and landscape conditions&amp;amp;amp;quot;"},{"id":438422,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1VDT","text":"USGS data release","linkHelpText":"Restoration versus invasive species: bigheaded carps use of a rehabilitated backwater: Data"},{"id":337010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-24","publicationStatus":"PW","scienceBaseUri":"58c12634e4b014cc3a3d3450","contributors":{"authors":[{"text":"Coulter, Alison A.","contributorId":187652,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":681168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schultz, Douglas","contributorId":187653,"corporation":false,"usgs":false,"family":"Schultz","given":"Douglas","affiliations":[],"preferred":false,"id":681169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tristano, Elizabeth","contributorId":187654,"corporation":false,"usgs":false,"family":"Tristano","given":"Elizabeth","affiliations":[],"preferred":false,"id":681170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681167,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garvey, James E.","contributorId":178007,"corporation":false,"usgs":false,"family":"Garvey","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":681171,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70179144,"text":"sir20165142 - 2017 - The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010","interactions":[],"lastModifiedDate":"2017-03-14T09:22:51","indexId":"sir20165142","displayToPublicDate":"2017-03-07T15:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5142","title":"The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010","docAbstract":"The U.S. Geological Survey, in cooperation with the Puerto Rico Department of Natural and Environmental Resources, completed a study to determine whether a relation exists between the extent of forest cover and the magnitude of base flow at two sets of paired drainage basins in the highlands of the municipalities of Adjuntas and Utuado within the mountainous interior of Puerto Rico. One set of paired basins includes the Río Guaónica and Río Tanamá, both tributaries of the Río Grande de Arecibo. The other set includes two smaller basins in the drainage basin of the Río Coabey, which is a tributary of the Río Tanamá. The paired basins in each set have similar rainfall patterns, geologic substrate, and aspect; the principal difference identified in the study is the extent of forest cover and related land uses such as the cultivation of shade and sun coffee. Data describing the hydrology, hydrogeology, and streamflow were used in the analysis. The principal objective of the study was to compare base flow per unit area among basins having different areal extents of forest cover and land uses such as shade coffee and sun coffee cultivation.
Within the mountainous interior of Puerto Rico, a substantial amount of the annual rainfall (45 to 39 percent in the Rio Guaónica and Rio Tanamá, respectively) can migrate to the subsurface and later emerge as base flow in streams. The magnitude of base flow within the two sets of paired basins varies seasonally. Minimum base flows occur during the annual dry season (generally from January to March), and maximum base flows occur during the wet season (generally from August to October). During the dry season or periods of below-normal rainfall, base flow is either the primary or the sole component of streamflow. Daily mean base flow ranged from 3.2 to 20.5 cubic feet per second (ft3 /s) at the Rio Guaónica Basin, and from 4.2 to 23.0 ft3 /s at the Rio Tanamá Basin. The daily mean base flows during 2010 ranged from 0.28 to 0.98 ft3 /s at Tributary 1 and from 0.22 to 0.58 ft3 /s at Tributary 2 of the Rio Coabey. The normalized daily base flow at the Río Guaónica and Río Tanamá Basin during 2010 ranged from 1.3 to 8.1 cubic feet per second per square mile (ft3 /s)/mi2 and from 1.1 to 6.1 (ft3 /s)/mi2 , respectively. The normalized daily base flow for the basins of Tributary 1 and Tributary 2 of Río Coabey during 2010 ranged from 1.0 to 3.6 (ft3 /s)/mi2 and from 1.5 to 3.9 (ft3 /s)/mi2 , respectively.
The normalized mean annual base flow is similar within the larger paired basins of Río Tanamá (2.74 [ft3 /s]/mi2 ) and Río Guaónica (3.15 [ft3 /s]/mi2 ). The mean annual base flow per unit area for both of these basins is about 79 percent of the mean annual streamflow. In the large paired basins, the proportion of Type I land use (forest patches, shade and mixed shade/sun coffee with associated cash crops) is substantially higher in Rio Guaónica Basin (81 percent) than in the Rio Tanamá Basin (59 percent), and the base flow per unit area is also higher. In the small paired basins of Rio Coabey, the proportion of Type I land use is much higher at Tributary 1 (52 percent) than at Tributary 2 (15 percent), but, in contrast to the large basins, the mean annual base flow per unit area is lower (2.22 and 2.62 [ft3 /s]/mi2 , respectively). There is no consistent relation between land use and normalized base flow between the two sets of paired basins in the study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165142","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Rodríguez-Martínez, Jesús, and Santiago, Marilyn, 2017, The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010: U.S. Geological Survey Scientific Investigations Report 2016–5142, 19 p., https://doi.org/10.3133/sir20165142.","productDescription":"Report: vii, 19 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-061550","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true}],"links":[{"id":438424,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58JG5","text":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7N58JG5","text":"USGS data release ","description":"USGS data release","linkHelpText":"Hydrologic data for the effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico"},{"id":336240,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5142/coverthb.jpg"},{"id":336241,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5142/sir20165142.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5142"}],"otherGeospatial":"Puerto Rico","contact":"Director, Caribbean-Florida Water Science Center
4446 Pet Lane
Suite 108
Lutz, FL 33559
https://pr.water.usgs.gov
Datum conversions from the National Geodetic Vertical Datum of 1929 to the North American Vertical Datum of 1988 among inland and coastal gages throughout the hydrologic regions of New England, the Mid-Atlantic, and the South Atlantic-Gulf have implications among river and storm surge forecasting, general commerce, and water-control operations. The process of data conversions may involve the application of a recovered National Geodetic Vertical Datum of 1929–North American Vertical Datum of 1988 offset, a simplistic datum transformation using VDatum or VERTCON software, or a survey, depending on a gaging network datum evaluation, anticipated uncertainties for data use among the cooperative water community, and methods used to derive the conversion. Datum transformations from National Geodetic Vertical Datum of 1929 to North American Vertical Datum of 1988 using VERTCON purport errors of ± 0.13 foot at the 95 percent confidence level among modeled points, claiming more consistency along the east coast. Survey methods involving differential and trigonometric leveling, along with observations using Global Navigation Satellite System technology, afford a variety of approaches to establish or perpetuate a datum during a survey. Uncertainties among leveling approaches are generally < 0.1 foot, and and Global Navigation Satellite System approaches may be categorized with uncertainties of ≤0.1 foot for a Level I quality category and ≥0.1 foot for Level II or III quality categories (defined by the U.S. Geological Survey) by observation and review of experienced practice. The conversion process is initiated with an evaluation of the inland and coastal gage network datum, beginning with altitude datum components and the history of those components queried through the U.S. Geological Survey Groundwater Site Inventory database. Subsequent edits to the Groundwater Site Inventory database may be required and a consensus reached among the U.S. Geological Survey Water Science Centers to identify the outstanding workload categorized as in-office datum transformations or offset applications versus out-of-office survey efforts. Datum conversions or datum establishment for the inland or coastal gaging network should meet datum uncertainty requirements among other Federal agencies. Datum uncertainty requirements are ±0.25 foot for U.S. Army Corps of Engineers water-control or construction projects and ±0.16 foot for Federal Emergency Management Agency field surveys and checkpoint surveys used for mapping. River level forecasts generally are defined as ± 0.10 foot among the National Oceanic and Atmospheric Administration–National Weather Service. Collaboration and communication among the cooperative water community is necessary during a datum conversion or datum change. Datum notification time-change requirements set by the National Oceanic and Atmospheric Administration–National Weather Service vary from 30 to 120 days, depending on datum conversion or datum-change case scenarios. Notification times associated with these case scenarios may be useful to the National Oceanic and Atmospheric Administration–National Weather Service and U.S. Army Corps of Engineers, because their daily operations are time sensitive, unlike the notification time change requirements of other entities that make up the cooperative water community. At the time of this writing, a future geopotential datum resulting from Gravity for the Redefinition of the American Vertical Datum is anticipated in 2022. A future version of VDatum and VERTCON is anticipated to provide a transformation among North American Vertical Datum of 1988 elevations to the new geopotential datum.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B8","usgsCitation":"Rydlund, P.H., Jr., and Noll, M.L., 2017, Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions (ver. 1.1, July 2017) U.S. Geological Survey Techniques and Methods, book 11, chap. B8, 29 p., https://doi.org/10.3133/tm11B8.","productDescription":"ix, 29 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-078726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":399692,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105491.htm"},{"id":344212,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b08/versionHist.txt"},{"id":336266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b08/coverthb2.jpg"},{"id":336267,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b08/tm11B8.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-8B"}],"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 -89.6484375,\n 30.183121842195515\n ],\n [\n -89.23095703125,\n 30.278044377800153\n ],\n [\n -88.39599609375,\n 30.221101852485987\n ],\n [\n -87.07763671875,\n 30.35391637229704\n ],\n [\n -86.06689453125,\n 30.164126343161097\n ],\n [\n -85.23193359375,\n 29.49698759653577\n ],\n [\n -84.08935546875,\n 30.012030680358613\n ],\n [\n -83.60595703125,\n 29.630771207229\n ],\n [\n -82.77099609375,\n 28.671310915880834\n ],\n [\n -82.99072265625,\n 27.858503954841247\n ],\n [\n -81.93603515625,\n 26.017297563851745\n ],\n [\n -81.32080078125,\n 25.105497373014686\n ],\n [\n -80.15625,\n 25.045792240303445\n ],\n [\n -79.8486328125,\n 26.70635985763354\n ],\n [\n -80.39794921875,\n 28.013801376380712\n ],\n [\n -81.1669921875,\n 30.939924331023445\n ],\n [\n -80.22216796875,\n 32.194208672875384\n ],\n [\n -77.93701171875,\n 33.687781758439364\n ],\n [\n -75.322265625,\n 35.08395557927643\n ],\n [\n -75.1025390625,\n 35.51434313431818\n ],\n [\n -75.7177734375,\n 36.87962060502676\n ],\n [\n -73.6962890625,\n 40.36328834091583\n ],\n [\n -69.85107421874999,\n 41.178653972331674\n ],\n [\n -69.49951171875,\n 41.83682786072714\n ],\n [\n -70.51025390625,\n 42.24478535602799\n ],\n [\n -70.42236328125,\n 43.197167282501276\n ],\n [\n -69.6533203125,\n 43.644025847699496\n ],\n [\n -66.81884765625,\n 44.74673324024678\n ],\n [\n -67.7197265625,\n 45.72152152227954\n ],\n [\n -67.7197265625,\n 47.100044694025215\n ],\n [\n -68.26904296875,\n 47.35371061951363\n ],\n [\n -69.10400390625,\n 47.517200697839414\n ],\n [\n -69.9609375,\n 46.89023157359399\n ],\n [\n -70.51025390625,\n 45.767522962149876\n ],\n [\n -71.25732421875,\n 45.336701909968134\n ],\n [\n -71.71875,\n 45.02695045318546\n ],\n [\n -73.41064453125,\n 45.01141864227728\n ],\n [\n -74.5751953125,\n 44.63739123445585\n ],\n [\n -76.97021484375,\n 43.13306116240612\n ],\n [\n -78.3544921875,\n 42.114523952464246\n ],\n [\n -79.189453125,\n 39.791654835253425\n ],\n [\n -80.26611328125,\n 37.82280243352756\n ],\n [\n -80.88134765625,\n 36.65079252503471\n ],\n [\n -82.41943359375,\n 35.28150065789119\n ],\n [\n -89.49462890625,\n 35.02999636902566\n ],\n [\n -90.41748046874999,\n 32.47269502206151\n ],\n [\n -90.10986328125,\n 31.034108344903512\n ],\n [\n -89.6484375,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 2017; Version 1.1: July 2017","publicComments":"This report is Chapter 8 in Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data.","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS 100
Rolla, MO 65401
https://mo.water.usgs.gov/
Intensification of the global hydrological cycle, ranging from larger individual precipitation events to more extreme multiyear droughts, has the potential to cause widespread alterations in ecosystem structure and function. With evidence that the incidence of extreme precipitation years (defined statistically from historical precipitation records) is increasing, there is a clear need to identify ecosystems that are most vulnerable to these changes and understand why some ecosystems are more sensitive to extremes than others. To date, opportunistic studies of naturally occurring extreme precipitation years, combined with results from a relatively small number of experiments, have provided limited mechanistic understanding of differences in ecosystem sensitivity, suggesting that new approaches are needed. Coordinated distributed experiments (CDEs) arrayed across multiple ecosystem types and focused on water can enhance our understanding of differential ecosystem sensitivity to precipitation extremes, but there are many design challenges to overcome (e.g., cost, comparability, standardization). Here, we evaluate contemporary experimental approaches for manipulating precipitation under field conditions to inform the design of ‘Drought-Net’, a relatively low-cost CDE that simulates extreme precipitation years. A common method for imposing both dry and wet years is to alter each ambient precipitation event. We endorse this approach for imposing extreme precipitation years because it simultaneously alters other precipitation characteristics (i.e., event size) consistent with natural precipitation patterns. However, we do not advocate applying identical treatment levels at all sites – a common approach to standardization in CDEs. This is because precipitation variability varies >fivefold globally resulting in a wide range of ecosystem-specific thresholds for defining extreme precipitation years. For CDEs focused on precipitation extremes, treatments should be based on each site's past climatic characteristics. This approach, though not often used by ecologists, allows ecological responses to be directly compared across disparate ecosystems and climates, facilitating process-level understanding of ecosystem sensitivity to precipitation extremes.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13504","usgsCitation":"Knapp, A., Avolio, M.L., Beier, C., Carroll, C.J., Collins, S., Dukes, J.S., Fraser, L.H., Griffin-Nolan, R.J., Hoover, D.L., Jentsch, A., Loik, M.E., Phillips, R.P., Post, A.K., Sala, O.E., Slette, I.J., Yahdjian, L., and Smith, M.D., 2017, Pushing precipitation to the extremes in distributed experiments: Recommendations for simulating wet and dry years: Global Change Biology, v. 23, no. 5, p. 1774-1782, https://doi.org/10.1111/gcb.13504.","productDescription":"9 p.","startPage":"1774","endPage":"1782","ipdsId":"IP-079614","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470023,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.13504","text":"External Repository"},{"id":336943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47a","contributors":{"authors":[{"text":"Knapp, Alan K.","contributorId":139807,"corporation":false,"usgs":false,"family":"Knapp","given":"Alan K.","affiliations":[{"id":13277,"text":"Graduate Degree Program in Ecology and Department of Biology, Colorado State University, Ft. Collins, CO","active":true,"usgs":false}],"preferred":false,"id":680953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avolio, Meghan L.","contributorId":187573,"corporation":false,"usgs":false,"family":"Avolio","given":"Meghan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":680954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beier, Claus","contributorId":187574,"corporation":false,"usgs":false,"family":"Beier","given":"Claus","email":"","affiliations":[],"preferred":false,"id":680955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Charles J. W.","contributorId":187575,"corporation":false,"usgs":false,"family":"Carroll","given":"Charles","email":"","middleInitial":"J. W.","affiliations":[],"preferred":false,"id":680956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott L.","contributorId":71307,"corporation":false,"usgs":false,"family":"Collins","given":"Scott L.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":680957,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dukes, Jeffrey S.","contributorId":187576,"corporation":false,"usgs":false,"family":"Dukes","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":680958,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fraser, Lauchlan H.","contributorId":187577,"corporation":false,"usgs":false,"family":"Fraser","given":"Lauchlan","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":680959,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Griffin-Nolan, Robert J.","contributorId":187578,"corporation":false,"usgs":false,"family":"Griffin-Nolan","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680960,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hoover, David L. dlhoover@usgs.gov","contributorId":5843,"corporation":false,"usgs":true,"family":"Hoover","given":"David","email":"dlhoover@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":680952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jentsch, Anke","contributorId":187579,"corporation":false,"usgs":false,"family":"Jentsch","given":"Anke","email":"","affiliations":[],"preferred":false,"id":680961,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loik, Michael E.","contributorId":187580,"corporation":false,"usgs":false,"family":"Loik","given":"Michael","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680962,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Phillips, Richard P.","contributorId":187581,"corporation":false,"usgs":false,"family":"Phillips","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":680963,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Post, Alison K.","contributorId":187582,"corporation":false,"usgs":false,"family":"Post","given":"Alison","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":680964,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sala, Osvaldo E.","contributorId":139047,"corporation":false,"usgs":false,"family":"Sala","given":"Osvaldo","email":"","middleInitial":"E.","affiliations":[{"id":12629,"text":"Arizona State University, Tempe, AZ (DETAIL TO BE ADDED)","active":true,"usgs":false}],"preferred":false,"id":680965,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Slette, Ingrid J.","contributorId":187583,"corporation":false,"usgs":false,"family":"Slette","given":"Ingrid","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680966,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Yahdjian, Laura","contributorId":187584,"corporation":false,"usgs":false,"family":"Yahdjian","given":"Laura","email":"","affiliations":[],"preferred":false,"id":680967,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smith, Melinda D.","contributorId":187585,"corporation":false,"usgs":false,"family":"Smith","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":680968,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70184286,"text":"70184286 - 2017 - Prediction and visualization of redox conditions in the groundwater of Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-25T11:31:39","indexId":"70184286","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Prediction and visualization of redox conditions in the groundwater of Central Valley, California","docAbstract":"Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182091,"text":"sir20175009 - 2017 - Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2017-03-08T09:08:36","indexId":"sir20175009","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5009","title":"Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin","docAbstract":"Approximately 6.4 million tons of dissolved solids are discharged from the Upper Colorado River Basin (UCRB) to the Lower Colorado River Basin each year. This results in substantial economic damages, and tens of millions of dollars are spent annually on salinity control projects designed to reduce salinity loads in surface waters of the UCRB. Dissolved solids in surface water and groundwater have been studied extensively over the past century, and these studies have contributed to a conceptual understanding of sources and transport of dissolved solids. This conceptual understanding was incorporated into a Spatially Referenced Regressions on Watershed Attributes (SPARROW) model to examine sources and transport of dissolved solids in the UCRB. The results of this model were published in 2009. The present report documents the methods and data used to develop an updated dissolved-solids SPARROW model for the UCRB, and incorporates data defining current basin attributes not available in the previous model, including delineation of irrigated lands by irrigation type (sprinkler or flood irrigation), and calibration data from additional monitoring sites.
Dissolved-solids loads estimated for 312 monitoring sites were used to calibrate the SPARROW model, which predicted loads for each of 10,789 stream reaches in the UCRB. The calibrated model provided a good fit to the calibration data as evidenced by R2 and yield R2 values of 0.96 and 0.73, respectively, and a root-mean-square error of 0.47. The model included seven geologic sources that have estimated dissolved-solids yields ranging from approximately 1 to 45 tons per square mile (tons/mi2). Yields generated from irrigated agricultural lands are substantially greater than those from geologic sources, with sprinkler irrigated lands generating an average of approximately 150 tons/mi2 and flood irrigated lands generating between 770 and 2,300 tons/mi2 depending on underlying lithology. The coefficients estimated for six landscape transport characteristics that influence the delivery of dissolved solids from sources to streams, are consistent with the process understanding of dissolved-solids loading to streams in the UCRB.
Dissolved-solids loads and the proportion of those loads among sources in the entire UCRB as well as in major tributaries in the basin are reported, as are loads generated from irrigated lands, rangelands, Bureau of Land Management (BLM) lands, and grazing allotments on BLM lands. Model-predicted loads also are compared with load estimates from 1957 and 1991 at selected locations in three divisions of the UCRB. At the basin scale, the model estimates that 32 percent of the dissolved-solids loads are from irrigated agricultural land sources that compose less than 2 percent of the land area in the UCRB. This estimate is less than previously reported estimates of 40 to 45 percent of basin-scale dissolved-solids loads from irrigated agricultural land sources. This discrepancy could be a result of the implementation of salinity control projects in the basin. Notably, results indicate that the conversion of flood irrigated agricultural lands to sprinkler irrigated agricultural lands is a likely process contributing to the temporal decrease in dissolved-solids loads from irrigated lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175009","collaboration":"Prepared in cooperation with the Colorado River Basin Salinity Control Forum","usgsCitation":"Miller, M.P., Buto, S.G., Lambert, P.M., and Rumsey, C.A., 2017, Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2017–5009, 23 p., https://doi.org/10.3133/sir20175009.","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-076357","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":438425,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LO3JV2","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions of total dissolved loads in streams of the Upper Colorado River Basin watershed"},{"id":336947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5009/coverthb.jpg"},{"id":336948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5009/sir20175009.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5009"},{"id":336949,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0JT4","text":"USGS data release","description":"USGS data release","linkHelpText":"Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin"}],"country":"United States","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.500244140625,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"