{"pageNumber":"254","pageRowStart":"6325","pageSize":"25","recordCount":40783,"records":[{"id":70227148,"text":"70227148 - 2020 - Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling","interactions":[],"lastModifiedDate":"2022-01-03T15:52:27.699945","indexId":"70227148","displayToPublicDate":"2020-11-23T09:25:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5023,"text":"PLoS Neglected Tropical Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling","docAbstract":"Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus’ extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pntd.0008868","usgsCitation":"Leach, C.B., Hoeting, J., Pepin, K.M., Eiras, A.E., Hooten, M., and Colleen T. Webb, C., 2020, Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling: PLoS Neglected Tropical Diseases, v. 14, no. 11, p. 1-20, https://doi.org/10.1371/journal.pntd.0008868.","productDescription":"e0008868, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-107662","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":454766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pntd.0008868","text":"Publisher Index Page"},{"id":393742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Espírito Santo","city":"Vitória","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -40.42968749999999,\n -20.43473423110048\n ],\n [\n -40.222320556640625,\n -20.43473423110048\n ],\n [\n -40.222320556640625,\n -20.17456745043183\n ],\n [\n -40.42968749999999,\n -20.17456745043183\n ],\n [\n -40.42968749999999,\n -20.43473423110048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Leach, Clinton B.","contributorId":270703,"corporation":false,"usgs":false,"family":"Leach","given":"Clinton","email":"","middleInitial":"B.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":829794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoeting, Jennifer A.","contributorId":270704,"corporation":false,"usgs":false,"family":"Hoeting","given":"Jennifer A.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":829795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pepin, Kim M.","contributorId":270705,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim","email":"","middleInitial":"M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":829796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eiras, Alvaro E.","contributorId":270706,"corporation":false,"usgs":false,"family":"Eiras","given":"Alvaro","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":829797,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":829793,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Colleen T. Webb, Colleen T.","contributorId":270707,"corporation":false,"usgs":false,"family":"Colleen T. Webb","given":"Colleen T.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":829798,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70226612,"text":"70226612 - 2020 - Evaluation of Arctic warming in mid-Pliocene climate simulations","interactions":[],"lastModifiedDate":"2021-12-01T13:03:35.899178","indexId":"70226612","displayToPublicDate":"2020-11-23T06:57:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of Arctic warming in mid-Pliocene climate simulations","docAbstract":"Palaeoclimate simulations improve our understanding of the climate, inform us about the performance of climate models in a different climate scenario, and help to identify robust features of the climate system. Here, we analyse Arctic warming in an ensemble of 16 simulations of the mid-Pliocene Warm Period (mPWP), derived from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2).
The PlioMIP2 ensemble simulates Arctic (60–90∘ N) annual mean surface air temperature (SAT) increases of 3.7 to 11.6 ∘C compared to the pre-industrial period, with a multi-model mean (MMM) increase of 7.2 ∘C. The Arctic warming amplification ratio relative to global SAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea ice extent anomalies range from −3.0 to -10.4×106\">−10.4×106 km2, with a MMM anomaly of -5.6×106\">−5.6×106 km2, which constitutes a decrease of 53 % compared to the pre-industrial period. The majority (11 out of 16) of models simulate summer sea-ice-free conditions (≤1×106\">≤1×106 km2) in their mPWP simulation. The ensemble tends to underestimate SAT in the Arctic when compared to available reconstructions, although the degree of underestimation varies strongly between the simulations. The simulations with the highest Arctic SAT anomalies tend to match the proxy dataset in its current form better. The ensemble shows some agreement with reconstructions of sea ice, particularly with regard to seasonal sea ice. Large uncertainties limit the confidence that can be placed in the findings and the compatibility of the different proxy datasets. We show that while reducing uncertainties in the reconstructions could decrease the SAT data–model discord substantially, further improvements are likely to be found in enhanced boundary conditions or model physics. Lastly, we compare the Arctic warming in the mPWP to projections of future Arctic warming and find that the PlioMIP2 ensemble simulates greater Arctic amplification than CMIP5 future climate simulations and an increase instead of a decrease in Atlantic Meridional Overturning Circulation (AMOC) strength compared to pre-industrial period. The results highlight the importance of slow feedbacks in equilibrium climate simulations, and that caution must be taken when using simulations of the mPWP as an analogue for future climate change.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/cp-16-2325-2020","usgsCitation":"de Nooijer, W., Zhang, Q., Li, Q., Zhang, Q., Li, X., Zhang, Z., Guo, C., Nisancioglu, K.H., Haywood, A.M., Tindall, J.C., Dowsett, H.J., Stepanek, C., Lohman, G., Otto-Bliesner, B.L., Feng, R., Sohl, L., Chandler, M., Tan, N., Contoux, C., Ramstein, G., Baatsen, M., von der Heydt, A.S., Chandan, D., Peltier, W.R., Abe-Ouchi, A., Chan, W., Kamae, Y., and Brierley, C.M., 2020, Evaluation of Arctic warming in mid-Pliocene climate simulations: Climate of the Past, v. 16, no. 6, p. 2325-2341, https://doi.org/10.5194/cp-16-2325-2020.","productDescription":"17 p.","startPage":"2325","endPage":"2341","ipdsId":"IP-123682","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":454772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-16-2325-2020","text":"Publisher Index Page"},{"id":392294,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"de Nooijer, Wesley","contributorId":269574,"corporation":false,"usgs":false,"family":"de Nooijer","given":"Wesley","email":"","affiliations":[{"id":55985,"text":"Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden","active":true,"usgs":false}],"preferred":false,"id":827460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Qiong","contributorId":269575,"corporation":false,"usgs":false,"family":"Zhang","given":"Qiong","email":"","affiliations":[{"id":55985,"text":"Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden","active":true,"usgs":false}],"preferred":false,"id":827461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Qiang","contributorId":197310,"corporation":false,"usgs":false,"family":"Li","given":"Qiang","email":"","affiliations":[],"preferred":false,"id":827462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Qiang","contributorId":210479,"corporation":false,"usgs":false,"family":"Zhang","given":"Qiang","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":827463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Xiangyu","contributorId":219286,"corporation":false,"usgs":false,"family":"Li","given":"Xiangyu","email":"","affiliations":[],"preferred":false,"id":827464,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Zhongshi","contributorId":269576,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhongshi","email":"","affiliations":[{"id":55988,"text":"Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":827465,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Guo, Chuncheng","contributorId":269577,"corporation":false,"usgs":false,"family":"Guo","given":"Chuncheng","email":"","affiliations":[{"id":55989,"text":"NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway","active":true,"usgs":false}],"preferred":false,"id":827466,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nisancioglu, Kerim H","contributorId":269578,"corporation":false,"usgs":false,"family":"Nisancioglu","given":"Kerim","email":"","middleInitial":"H","affiliations":[{"id":55989,"text":"NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway","active":true,"usgs":false}],"preferred":false,"id":827467,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haywood, Alan M","contributorId":206288,"corporation":false,"usgs":false,"family":"Haywood","given":"Alan","email":"","middleInitial":"M","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":827468,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tindall, Julia C.","contributorId":147376,"corporation":false,"usgs":false,"family":"Tindall","given":"Julia","email":"","middleInitial":"C.","affiliations":[{"id":13344,"text":"University of Leeds","active":true,"usgs":false}],"preferred":false,"id":827469,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":827470,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stepanek, Christian","contributorId":220691,"corporation":false,"usgs":false,"family":"Stepanek","given":"Christian","email":"","affiliations":[{"id":40240,"text":"Alfred Wegener Institute-Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":827471,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lohman, Gerrit","contributorId":269580,"corporation":false,"usgs":false,"family":"Lohman","given":"Gerrit","email":"","affiliations":[{"id":55990,"text":"Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":827472,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Otto-Bliesner, Bette L.","contributorId":209685,"corporation":false,"usgs":false,"family":"Otto-Bliesner","given":"Bette","email":"","middleInitial":"L.","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":827473,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Feng, Ran","contributorId":269581,"corporation":false,"usgs":false,"family":"Feng","given":"Ran","email":"","affiliations":[{"id":55991,"text":"Department of Geosciences, College of Liberal Arts and Sciences, University of Connecticut, Connecticut, 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CHINA","active":true,"usgs":false}],"preferred":false,"id":827476,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Contoux, Camille","contributorId":269584,"corporation":false,"usgs":false,"family":"Contoux","given":"Camille","email":"","affiliations":[{"id":55994,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":827477,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Ramstein, Gilles","contributorId":269585,"corporation":false,"usgs":false,"family":"Ramstein","given":"Gilles","email":"","affiliations":[{"id":55994,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":827478,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Baatsen, Michiel","contributorId":269586,"corporation":false,"usgs":false,"family":"Baatsen","given":"Michiel","email":"","affiliations":[{"id":55995,"text":"Centre for Complex Systems Science, Utrecht University, Utrecht, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":827479,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"von der Heydt, Anna S","contributorId":269587,"corporation":false,"usgs":false,"family":"von der Heydt","given":"Anna","email":"","middleInitial":"S","affiliations":[{"id":55995,"text":"Centre for Complex Systems Science, Utrecht University, Utrecht, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":827480,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Chandan, Deepak","contributorId":269588,"corporation":false,"usgs":false,"family":"Chandan","given":"Deepak","email":"","affiliations":[{"id":55996,"text":"Department of Physics, University of Toronto, Toronto, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":827481,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Peltier, W. Richard","contributorId":150752,"corporation":false,"usgs":false,"family":"Peltier","given":"W.","email":"","middleInitial":"Richard","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":827572,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Abe-Ouchi, A.","contributorId":173111,"corporation":false,"usgs":false,"family":"Abe-Ouchi","given":"A.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":827482,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Chan, W-L","contributorId":269589,"corporation":false,"usgs":false,"family":"Chan","given":"W-L","affiliations":[{"id":55997,"text":"Centre for Earth Surface System Dynamics (CESD), Atmosphere and Ocean Research Institute (AORI), University of Tokyo, Japan","active":true,"usgs":false}],"preferred":false,"id":827483,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Kamae, Youichi","contributorId":269590,"corporation":false,"usgs":false,"family":"Kamae","given":"Youichi","email":"","affiliations":[{"id":55998,"text":"Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan","active":true,"usgs":false}],"preferred":false,"id":827484,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Brierley, Chris M","contributorId":269591,"corporation":false,"usgs":false,"family":"Brierley","given":"Chris","email":"","middleInitial":"M","affiliations":[{"id":55999,"text":"Department of Geography, University College London, London, UK","active":true,"usgs":false}],"preferred":false,"id":827485,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}}
,{"id":70216491,"text":"70216491 - 2020 - Development of a novel framework for modeling field-scale conservation effects of depressional wetlands in agricultural landscapes","interactions":[],"lastModifiedDate":"2020-11-23T13:47:37.113837","indexId":"70216491","displayToPublicDate":"2020-11-20T07:46:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Development of a novel framework for modeling field-scale conservation effects of depressional wetlands in agricultural landscapes","docAbstract":"The intermixed cropland, grassland, and wetland ecosystems of the upper mid-western United States combine to provide a suite of valuable ecological services. Grassland and wetland losses in the upper midwestern United States have been extensive, but government-funded conservation programs have protected and restored hundreds of thousands of acres of wetland and grassland habitat in the region. The value of restored wetlands in agricultural fields is complex, and the USDA Natural Resource Conservation Service, Conservation Effects Assessment Project (CEAP) has been lacking the methodology to include these conservation practices in their analyses. Our aim is to develop a reproducible methodology for simulating wetlands within the CEAP cropland modeling framework used to evaluate other agricultural conservation practices. Furthermore, we evaluate the effect of using upland conservation practices on the functioning of restored wetlands. By simulating the addition of a depressional wetland that effectively removes 6% of the field from crop production, we obtained a 15% reduction in annual runoff and a 29% and 28% reduction in mean annual nitrogen (N) and phosphorus (P) losses, respectively. The presence of the depressional wetland in the field is estimated to also reduce edge-of-field losses of sediments by 20% and sediment-bound N and P by 19% and 23%, respectively. Additionally, adding a grass filter strip around the wetland greatly decreased sediment inputs to the wetland, increasing the effective life of the wetland, in terms of its ability to perform valued services, by decades to centuries. Our method for modeling depressional wetlands embedded in cropped fields provides a means to quantify the effects of wetland conservation practices on field-level losses for regional assessments, such as the CEAP.
Geysers are rare geologic features that intermittently discharge liquid water and steam driven by heating and decompression boiling. The cause of variability in eruptive styles and the associated seismic signals are not well understood. Data collected from five broadband seismometers at Lone Star Geyser, Yellowstone National Park are used to determine the properties, location, and temporal patterns of hydrothermal tremor. The tremor is harmonic at some stages of the eruption cycle and is caused by near‐periodic repetition of discrete seismic events. Using the polarization of ground motion, we identify the location of tremor sources throughout several eruption cycles. During preplay episodes (smaller eruptions preceding the more vigorous major eruption), tremor occurs at depths of 7–10 m and is laterally offset from the geyser's cone by ~5 m. At the onset of the main eruption, tremor sources migrate laterally and become shallower. As the eruption progresses, tremor sources migrate along the same path but in the opposite direction, ending where preplay tremor originates. The upward and then downward migration of tremor sources during eruptions are consistent with warming of the conduit followed by evacuation of water during the main eruption. We identify systematic relations among the two types of preplays, discharge, and the main eruption. A point‐source moment tensor fit to low‐frequency waveforms of an individual tremor event using half‐space velocity models indicates average VS ≳ 0.8 km/s, source depths ~4–20 m, and moment tensors with primarily positive isotropic and compensated linear vector dipole moments.
We show that seismic attenuation ( ![\"urn:x-wiley:00948276:media:grl61586:grl61586-math-0001\"](\"https://agupubs.onlinelibrary.wiley.com/cms/asset/fe6e1bba-0f11-4326-9d90-0344d44a07b8/grl61586-math-0001.png\")
) along the San Andreas fault (SAF) at Parkfield correlates with the occurrence of moderate‐to‐large earthquakes at local and regional distances. Earthquake‐related ![\"urn:x-wiley:00948276:media:grl61586:grl61586-math-0002\"](\"https://agupubs.onlinelibrary.wiley.com/cms/asset/a89f08da-3eff-4c9e-95a4-b39accaeac3b/grl61586-math-0002.png\")
anomalies are likely caused by changes in permeability from dilatant static stress changes, damage by strong shaking from local sources, and pore unclogging/clogging from mobilization of colloids by dynamic strains. We find that, prior to the 2004 M6 Parkfield earthquake, prefailure conditions for some local events of moderate magnitude correspond to positive anomalies of ![\"urn:x-wiley:00948276:media:grl61586:grl61586-math-0003\"](\"https://agupubs.onlinelibrary.wiley.com/cms/asset/999ff49d-0c63-413d-a3d3-7163e4f59927/grl61586-math-0003.png\")
on the Pacific side, with local and regional earthquakes producing sharp attenuation reversals. After the 2004 Parkfield earthquake, we see higher ![\"urn:x-wiley:00948276:media:grl61586:grl61586-math-0004\"](\"https://agupubs.onlinelibrary.wiley.com/cms/asset/4d674cda-5354-4c8d-a1a4-54b743103370/grl61586-math-0004.png\")
anomalies along the SAF, but low sensitivity to local and regional earthquakes, probably because the mainshock significantly altered the permeability state of the rocks adjacent to the SAF, and its sensitivity to earthquake‐induced stress perturbations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089201","usgsCitation":"Malagnini, L., and Parsons, T.E., 2020, Seismic attenuation monitoring of a critically stressed San Andreas fault: Geophysical Research Letters, v. 47, no. 23, 11 p., https://doi.org/10.1029/2020GL089201.","productDescription":"11 p.","ipdsId":"IP-117715","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":380869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault-Parkfield Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.72302246093749,\n 35.646137228802424\n ],\n [\n -120.64361572265624,\n 35.646137228802424\n ],\n [\n -120.64361572265624,\n 36.61332303966068\n ],\n [\n -121.72302246093749,\n 36.61332303966068\n ],\n [\n -121.72302246093749,\n 35.646137228802424\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"23","noUsgsAuthors":false,"publicationDate":"2020-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Malagnini, Luca 0000-0001-5809-9945","orcid":"https://orcid.org/0000-0001-5809-9945","contributorId":245308,"corporation":false,"usgs":false,"family":"Malagnini","given":"Luca","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":805881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805882,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70216445,"text":"sir20205095 - 2020 - Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015","interactions":[],"lastModifiedDate":"2021-06-14T19:39:33.551007","indexId":"sir20205095","displayToPublicDate":"2020-11-19T07:20:28","publicationYear":"2020","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":"2020-5095","displayTitle":"Landscape and Climatic Influences on Actual Evapotranspiration and Available Water Using the Operational Simplified Surface Energy Balance (SSEBop) Model in Eastern Bernalillo County, New Mexico, 2015","title":"Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the Bernalillo County Public Works Division, conducted a 1-year study in 2015 to assess the spatial and temporal distribution of evapotranspiration (ET) and available water within the East Mountain area in Bernalillo County, New Mexico. ET and available water vary spatiotemporally because of complex interactions among environmental factors, including vegetation characteristics, soil characteristics, topography, and climate.
Precipitation data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (P) were used in conjunction with actual ET (ETa) data from the Operational Simplified Surface Energy Balance (SSEBop) model to estimate available water (P – ETa) at 100-meter (m) resolution in the study area. Maps, descriptive statistics, boxplots, regression analyses (continuous data), and multiple comparison tests (categorical data) were used to characterize P, ETa, and available water and their relations to topographic, soil, and vegetation datasets in the East Mountain area. Five categories of the natural land-cover type (evergreen forest, shrub, herbaceous, deciduous forest, and mixed forest) and four categories of developed land-cover type specific to residential intensity (developed open, developed low, developed medium, and developed high) were analyzed individually and in interaction with multiple elevation, tree canopy, and soil texture classes.
Annual mean P in 2015 in the East Mountain area was 608 millimeters (mm), and annual mean ETa was 543 mm (89 percent of annual P in 2015), indicating that in 2015, a spatial mean of about 65 mm of water was available for runoff, soil moisture replenishment, or groundwater recharge. Monthly ETa was greatest in July and smallest in January. The intervening months did not show smooth temporal or consistent spatial changes from month to month. Months with lower ETa (January to March, October to December) also tended to have greater available water, indicating that soil moisture (water supply) and potential ET (water demand) may have been out of phase.
Regression analyses showed that monthly ETa data had the highest correlation with annual ETa among the atmospheric, topographic, soil, or vegetation datasets, particularly during the early and late growing season (March, April, May, and September). In contrast, monthly P was highly variable and not as highly correlated with annual ETa. Among landscape variables, correlations with annual ETa were highest for tree canopy cover (coefficient of determination [R2] = 0.46). Correlations between ETa and other landscape variables were lower (R2 = 0.06–0.19): available soil water in the top 100 centimeters, soil bulk density of layer 1, slope, sand content of soil layer 1, soil depth, available soil water in the top 25 centimeters, leaf area index, aspect eastness, and elevation. Evergreen forest areas had the highest annual median ETa, followed by mixed forest, open residential areas, and deciduous forest. Available water typically was higher in landcover types with lower ETa: herbaceous cover, followed by deciduous forest, high-intensity developed areas, and shrub. Deciduous forest had the second highest median available water, despite having the fourth highest ETa, because deciduous forest had greater P than most other areas. Annual median ETa typically was greatest in the second highest elevation band (2,401–2,800 m above the North American Vertical Datum of 1988 [NAVD 88]), and lower in the highest elevation band (2,801–3,254 m above NAVD 88), despite having greater P, likely because of decreased tree canopy cover or a shift from evergreen to deciduous trees at the highest elevations.
Annual median ETa increased with tree canopy cover, regardless of landcover type. ETa correlation was higher with tree canopy than with leaf area index or normalized difference vegetation index. This result indicates that it is important to include the thermal band (from satellite multispectral data) in vegetation indices used to describe ETa, perhaps to account for the influence of energy limitation or water limitation on ET. Of all natural landcover types, finer soils had the most available water, whereas coarser soils had the least available water. Relations of soil type with P – ETa were different than with ETa, indicating ET and available water have a complex response to differences in soil type. Further modeling would be useful in determining soils’ infiltration, storage, conductivity, and plant-water availability relations to individual storms for each position in the landscape, as well as the corresponding effects of these processes on ET and available water.
The best multivariate linear model for annual ETa had an R2 value of 0.62. Monthly ETa models had R2 values between 0.16 and 0.65. Models usually, but not always, performed best during the growing season. These results indicate that even the best multivariate linear models cannot explain a notable amount of the variability in ET. The monthly ETa models with the highest correlations (August and September) followed a July having almost twice the mean precipitation for July (1981–2010), which indicates that a soil-moisture variable is needed to more accurately model monthly ETa. Further study is needed to better characterize this system, the variables that affect ET and available water, and the partitioning of available water into runoff, soil moisture storage, and groundwater recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205095","collaboration":"Prepared in cooperation with the Bernalillo County Public Works Division","usgsCitation":"Douglas-Mankin, K.R., McCutcheon, R.J., Mitchell, A.C., and Senay, G.B., 2020, Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5095, 40 p., https://doi.org/10.3133/sir20205095.","productDescription":"x, 40 p.","numberOfPages":"53","onlineOnly":"Y","ipdsId":"IP-101269","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":380594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5095/sir20205095.pdf","text":"Report","size":"3.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5095"},{"id":380593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5095/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Bernalillo County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.65252685546875,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 34.879171662167664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
","tableOfContents":"- Abstract
- Introduction
- Background
- Materials and Methods
- Climate in the East Mountain Area for the Study Period, 2015
- ETa and Available Water in the East Mountain Area
- Spatial and Temporal Variability of ETa and Available Water
- Landscape and Climatic Effects on ETa and Available Water
- Summary and Conclusions
- References Cited
","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-11-19","noUsgsAuthors":false,"publicationDate":"2020-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":203927,"corporation":false,"usgs":true,"family":"Douglas-Mankin","given":"Kyle","email":"","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCutcheon, Ryan J. 0000-0003-3775-006X","orcid":"https://orcid.org/0000-0003-3775-006X","contributorId":245006,"corporation":false,"usgs":true,"family":"McCutcheon","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Aurelia C. 0000-0003-3302-4546","orcid":"https://orcid.org/0000-0003-3302-4546","contributorId":222580,"corporation":false,"usgs":true,"family":"Mitchell","given":"Aurelia C.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":805140,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70216980,"text":"70216980 - 2020 - Ensemble ShakeMaps for magnitude 9 earthquakes on the Cascadia Subduction Zone","interactions":[],"lastModifiedDate":"2021-02-04T14:51:07.249943","indexId":"70216980","displayToPublicDate":"2020-11-18T07:40:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Ensemble ShakeMaps for magnitude 9 earthquakes on the Cascadia Subduction Zone","docAbstract":"We develop ensemble ShakeMaps for various magnitude 9 (M\">MM 9) earthquakes on the Cascadia megathrust. Ground‐shaking estimates are based on 30 M\">MM 9 Cascadia earthquake scenarios, which were selected using a logic‐tree approach that varied the hypocenter location, down‐dip rupture limit, slip distribution, and location of strong‐motion‐generating subevents. In a previous work, Frankel et al. (2018) used a hybrid approach (i.e., 3D deterministic simulations for frequencies <1  Hz\"><1 Hz<1 Hz and stochastic synthetics for frequencies >1  Hz\">>1 Hz>1 Hz) and uniform site amplification factors to create broadband seismograms from this set of 30 earthquake scenarios. Here, we expand on this work by computing site‐specific amplification factors for the Pacific Northwest and applying these factors to the ground‐motion estimates derived from Frankel et al. (2018). In addition, we use empirical ground‐motion models (GMMs) to expand the ground‐shaking estimates beyond the original model extent of Frankel et al. (2018) to cover all of Washington State, Oregon, northern California, and southern British Columbia to facilitate the use of these ensemble ShakeMaps in region‐wide risk assessments and scenario planning exercises. Using this updated set of 30 M\">MM 9 Cascadia earthquake scenarios, we present ensemble ShakeMaps for the median, 2nd, 16th, 84th, and 98th percentile ground‐motion intensity measures. Whereas traditional scenario ShakeMaps are based on a single hypothetical earthquake rupture, our ensemble ShakeMaps take advantage of a logic‐tree approach to estimating ground motions from multiple earthquake rupture scenarios. In addition, 3D earthquake simulations capture important features such as strong ground‐motion amplification in the Pacific Northwest’s sedimentary basins, which are not well represented in the empirical GMMs that compose traditional scenario ShakeMaps. Overall, our results highlight the importance of strong‐motion‐generating subevents for coastal sites, as well as the amplification of long‐period ground shaking in deep sedimentary basins, compared with previous scenario ShakeMaps for Cascadia.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220200240","usgsCitation":"Wirth, E.A., Grant, A.R., Marafi, N.A., and Frankel, A.D., 2020, Ensemble ShakeMaps for magnitude 9 earthquakes on the Cascadia Subduction Zone: Seismological Research Letters, v. 92, no. 1, p. 199-211, https://doi.org/10.1785/0220200240.","productDescription":"13 p.","startPage":"199","endPage":"211","ipdsId":"IP-120218","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":381570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascadia Subduction Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.65039062499999,\n 37.405073750176925\n ],\n [\n -118.95996093749999,\n 37.405073750176925\n ],\n [\n -118.95996093749999,\n 49.095452162534826\n ],\n [\n -126.65039062499999,\n 49.095452162534826\n ],\n [\n -126.65039062499999,\n 37.405073750176925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"92","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wirth, Erin A. 0000-0002-8592-4442","orcid":"https://orcid.org/0000-0002-8592-4442","contributorId":207853,"corporation":false,"usgs":true,"family":"Wirth","given":"Erin","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":807160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":807161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marafi, Nasser A.","contributorId":197874,"corporation":false,"usgs":false,"family":"Marafi","given":"Nasser","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":807162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frankel, Arthur D. 0000-0001-9119-6106 afrankel@usgs.gov","orcid":"https://orcid.org/0000-0001-9119-6106","contributorId":146285,"corporation":false,"usgs":true,"family":"Frankel","given":"Arthur","email":"afrankel@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":807163,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70219111,"text":"70219111 - 2020 - Baseflow age distributions and depth of active groundwater flow in a snow‐dominated mountain headwater basin","interactions":[],"lastModifiedDate":"2021-03-25T11:56:41.937759","indexId":"70219111","displayToPublicDate":"2020-11-18T07:04:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Baseflow age distributions and depth of active groundwater flow in a snow‐dominated mountain headwater basin","docAbstract":"Deeper flows through bedrock in mountain watersheds could be important, but lack of data to characterize bedrock properties limits understanding. To address data scarcity, we combine a previously published integrated hydrologic model of a snow‐dominated, headwater basin of the Colorado River with a new method for dating baseflow age using dissolved gas tracers SF6, CFC‐113, N2, and Ar. The original flow model predicts the majority of groundwater flow through shallow alluvium (<8 m) sitting on top of less permeable bedrock. The water moves too quickly and is unable to reproduce observed SF6 concentrations. To match gas data, bedrock permeability is increased to allow a larger fraction of deeper and older groundwater flow (median 112 m). The updated hydrologic model indicates interannual variability in baseflow age (3–12 years) is controlled by the volume of seasonal interflow and tightly coupled to snow accumulation and monsoon rain. Deeper groundwater flow remains stable (11.7 ± 0.7 years) as a function mean historical recharge to bedrock hydraulic conductivity (R/K). A sensitivity analysis suggests that increasing bedrock K effectively moves this alpine basin away from its original conceptualization of hyperlocalized groundwater flow (high R/K) with groundwater age insensitive to changes in water inputs. Instead, this basin is situated close to the precipitation threshold defining recharge controlled groundwater flow conditions (low R/K) in which groundwater age increases with small reductions in precipitation. Work stresses the need to explore alternative methods characterizing bedrock properties in mountain basins to better quantify deeper groundwater flow and predict their hydrologic response to change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028161","usgsCitation":"Carroll, R.W., Manning, A.H., Niswonger, R.G., Marchetti, D.W., and Williams, K.H., 2020, Baseflow age distributions and depth of active groundwater flow in a snow‐dominated mountain headwater basin: Water Resources Research, v. 56, no. 12, e2020WR028161, 19 p., https://doi.org/10.1029/2020WR028161.","productDescription":"e2020WR028161, 19 p.","ipdsId":"IP-115011","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454804,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028161","text":"Publisher Index Page"},{"id":384624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.039306640625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H. 0000-0002-9302-8074","orcid":"https://orcid.org/0000-0002-9302-8074","contributorId":178784,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":812816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":812818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marchetti, David W 0000-0002-1246-0798","orcid":"https://orcid.org/0000-0002-1246-0798","contributorId":255716,"corporation":false,"usgs":false,"family":"Marchetti","given":"David","email":"","middleInitial":"W","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":812819,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Kenneth H. 0000-0002-3568-1155","orcid":"https://orcid.org/0000-0002-3568-1155","contributorId":176791,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":812820,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70216349,"text":"ofr20201094 - 2020 - Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17","interactions":[],"lastModifiedDate":"2020-11-17T23:20:23.252871","indexId":"ofr20201094","displayToPublicDate":"2020-11-17T08:05:00","publicationYear":"2020","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":"2020-1094","displayTitle":"Measured and Calculated Nitrate and Dissolved Organic Carbon Concentrations and Loads at the W.P. Franklin Lock and Dam, S-79, South Florida, 2014–17","title":"Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17","docAbstract":"The U.S. Geological Survey monitored dissolved nitrate plus nitrite as nitrogen (N) and dissolved organic carbon (DOC) concentrations and calculated loads of these constituents at the W.P. Franklin Lock and Dam (S-79) from April 2014 to December 2017. Flows from Lake Okeechobee controlled by S-77, S-78 and S-79 affect water quality in the downstream Caloosahatchee River Estuary, where increased nutrients and dissolved organic matter are of concern. Numerous algal blooms have occurred in the Caloosahatchee River and downstream estuaries in recent years (2005–18) and are often attributed to eutrophication. Dissolved nitrate plus nitrite as N (hereafter, referred to as nitrate) data were collected at 15-minute intervals using a submersible ultraviolet optical nitrate sensor. The instrument data were corrected for interferences, as determined by the relation between instrument measurements and 20 concurrent laboratory values. A surrogate model, based on 36 concurrent measurements of DOC, fluorescence of chromophoric dissolved organic matter, and specific conductance, was developed to calculate DOC at 15-minute intervals.
Mean and median calculated nitrate concentrations for the study period (2014–17) were both 0.21 milligram per liter (mg/L). Monthly mean nitrate concentrations ranged from 0.04 mg/L in April 2017 to 0.48 mg/L in November 2015. Monthly mean nitrate concentrations and the proportion of water that was attributed to Lake Okeechobee discharge, released through S-79, were weakly correlated and indicate that the nitrate concentrations typically decreased as the percentage of water released from the lake increased. Annual nitrate loads were 278 metric tons in 2015, 782 metric tons in 2016, and 525 metric tons in 2017. Monthly mean nitrate loads ranged from 1.2 metric tons in April 2017 to 171.3 metric tons in February 2016. Nitrate loads increased linearly with an increase in flow and typically increased during the wet season, May to October. Monthly loads of nitrate were strongly correlated with flow at S-77 and S-79.
Mean and median calculated DOC concentrations for the study period were 18.3 mg/L and 18.9 mg/L, respectively. Monthly mean DOC concentrations ranged from 12.6 mg/L in May 2017 to 21.5 mg/L in September 2015. Generally, DOC concentrations were lower during the dry season months (November to April) and higher during the wet season months. Monthly mean DOC concentrations were moderately correlated with monthly mean flow volumes at S-79. There was a strong correlation between monthly mean DOC concentrations and the proportion of water released at S-79 that can be attributed directly to Lake Okeechobee, indicating that contributions between Moore Haven Lock and Dam (S-77) and S-79 have a higher DOC concentration than water released from Lake Okeechobee. Monthly mean nitrate concentrations and monthly mean DOC concentrations were strongly correlated. Annual loads of DOC were 23,960 metric tons in 2015 and 65,610 metric tons in 2016 (2014 and 2017 data were incomplete). Monthly loads of DOC ranged from 284 metric tons in May 2017 to 15,122 metric tons in September 2017, the latter corresponding to the effects from Hurricane Irma. Monthly loads of DOC were strongly correlated with flow at S-77 and S-79.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201094","collaboration":"USGS Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A., 2020, Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17: U.S. Geological Survey Open-File Report 2020-1094, 37 p., https://doi.org/10.3133/ofr20201094.","productDescription":"Report: vi, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091619","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":380478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1094/coverthb.jpg"},{"id":380479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1094/ofr20201094.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1094"},{"id":380480,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V4ZGWU","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, Franklin Lock and Dam (S-79) southern Florida, 2014-2017"}],"country":"United States","state":"Florida","otherGeospatial":"W.P. Franklin Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.7437744140625,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.701452590314368\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Contact Pubs Warehouse
","tableOfContents":"- Abstract
- Introduction and Background
- Methods
- Dissolved Organic Carbon Model
- Nitrate Concentrations and Loads
- Dissolved Organic Carbon Concentrations and Loads
- Summary
- Acknowledgments
- References Cited
- Appendix 1. Model Archive Summary for Dissolved Organic Carbon Concentrations at Station 02292900: Caloosahatchee River at S-79, Nr. Olga, Florida
","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-11-17","noUsgsAuthors":false,"publicationDate":"2020-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Booth, Amanda 0000-0002-2666-2366 acbooth@usgs.gov","orcid":"https://orcid.org/0000-0002-2666-2366","contributorId":5432,"corporation":false,"usgs":true,"family":"Booth","given":"Amanda","email":"acbooth@usgs.gov","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804780,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70216807,"text":"70216807 - 2020 - Water temperature controls for regulated canyon-bound rivers","interactions":[],"lastModifiedDate":"2020-12-30T14:49:31.055876","indexId":"70216807","displayToPublicDate":"2020-11-16T09:20:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Water temperature controls for regulated canyon-bound rivers","docAbstract":"Many canyon‐bound rivers have been dammed and downstream flow and water temperatures modified. Climate change is expected to cause lower storage in reservoirs and warmer release temperatures, which may further alter downstream flow and thermal regimes. To anticipate potential future changes, we first need to understand the dominant heat transfer mechanisms in canyon‐bound river systems. Towards this end, we adapt a dynamic process‐based river routing and temperature model to account for complex shading and radiation characteristics found in canyon‐bound rivers. We apply the model to a 362 km segment of the Colorado River in Grand Canyon National Park, USA to simulate temperature over an 18‐year period. Extensive temperature and flow datasets from within the canyon were used to assess model performance. At the most downstream gaging location, root mean square errors of hourly flow routing and temperature predictions were 11.5 m3/s and 0.93 °C, respectively. We found that heat fluxes controlling temperatures were highly variable over space and time, primarily due to shortwave radiation dynamics and hydropeaking flow conditions. Additionally, the large differences between air and water temperature during summer periods resulted in high sensible and latent heat fluxes. Sensitivity analyses indicate that reservoir release temperatures are most influential above the RM88 gage (141 kilometers below Glen Canyon Dam), while a combination of discharge, shortwave radiation, and air temperature become more important farther downstream. This study illustrates the importance of understanding the spatial and temporal variability of topographic shading when predicting water temperatures in canyon‐bound rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027566","usgsCitation":"Mihalevich, B.A., Neilson, B., Buahin, C.A., Yackulic, C., and Schmidt, J.C., 2020, Water temperature controls for regulated canyon-bound rivers: Water Resources Research, v. 56, e2020WR027566, 24 p., https://doi.org/10.1029/2020WR027566.","productDescription":"e2020WR027566, 24 p.","ipdsId":"IP-117871","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.961181640625,\n 35.639441068973944\n ],\n [\n -111.29150390625,\n 35.639441068973944\n ],\n [\n -111.29150390625,\n 36.923547681089296\n ],\n [\n -113.961181640625,\n 36.923547681089296\n ],\n [\n -113.961181640625,\n 35.639441068973944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","noUsgsAuthors":false,"publicationDate":"2020-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Mihalevich, Bryce A.","contributorId":245512,"corporation":false,"usgs":false,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neilson, Bethany","contributorId":178798,"corporation":false,"usgs":false,"family":"Neilson","given":"Bethany","affiliations":[],"preferred":false,"id":806341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buahin, Caleb A.","contributorId":245514,"corporation":false,"usgs":false,"family":"Buahin","given":"Caleb","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":806343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":806344,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70228678,"text":"70228678 - 2020 - Increased typhoon activity in the Pacific deep tropics driven by Little Ice Age circulation changes","interactions":[],"lastModifiedDate":"2022-02-16T15:34:39.786634","indexId":"70228678","displayToPublicDate":"2020-11-16T08:57:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Increased typhoon activity in the Pacific deep tropics driven by Little Ice Age circulation changes","docAbstract":"The instrumental record reveals that tropical cyclone activity is sensitive to oceanic and atmospheric variability on inter-annual and decadal scales. However, our understanding of the influence of climate on tropical cyclone behaviour is restricted by the short historical record and the sparseness of prehistorical reconstructions, particularly in the western North Pacific, where coastal communities suffer loss of life and livelihood from typhoons annually. Here, to explore past regional typhoon dynamics, we reconstruct three millennia of deep tropical North Pacific cyclogenesis. Combined with existing records, our reconstruction demonstrates that low-baseline typhoon activity prior to 1350 CE was followed by an interval of frequent storms during the Little Ice Age. This pattern, concurrent with hydroclimate proxy variability, suggests a centennial-scale link between Pacific hydroclimate and tropical cyclone climatology. An ensemble of global climate models demonstrates a migration of the Pacific Walker circulation and variability in two Pacific climate modes during the Little Ice Age, which probably contributed to enhanced tropical cyclone activity in the tropical western North Pacific. In the next century, projected changes to the Pacific Walker circulation and expansion of the tropics will invert these Little Ice Age hydroclimate trends, potentially reducing typhoon activity in the deep tropical Pacific.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41561-020-00656-2","usgsCitation":"Bramante, J.F., Ford, M., Kench, P., Ashton, A., Toomey, M., Sullivan, R., Karnauskas, K., Ummenhofer, C.C., and Donnelly, J.P., 2020, Increased typhoon activity in the Pacific deep tropics driven by Little Ice Age circulation changes: Nature Geoscience, v. 13, p. 806-811, https://doi.org/10.1038/s41561-020-00656-2.","productDescription":"6 p.","startPage":"806","endPage":"811","ipdsId":"IP-120405","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467271,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/26505","text":"External Repository"},{"id":396015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"South Pacific Ocean","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bramante, James F","contributorId":245127,"corporation":false,"usgs":false,"family":"Bramante","given":"James","email":"","middleInitial":"F","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":835005,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, Murray","contributorId":224308,"corporation":false,"usgs":false,"family":"Ford","given":"Murray","email":"","affiliations":[{"id":40855,"text":"UA","active":true,"usgs":false}],"preferred":false,"id":835006,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kench, Paul","contributorId":248315,"corporation":false,"usgs":false,"family":"Kench","given":"Paul","email":"","affiliations":[{"id":49849,"text":"Simon Frazier U.","active":true,"usgs":false}],"preferred":false,"id":835007,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ashton, Andrew","contributorId":184098,"corporation":false,"usgs":false,"family":"Ashton","given":"Andrew","affiliations":[],"preferred":false,"id":835008,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Toomey, Michael 0000-0003-0167-9273 mtoomey@usgs.gov","orcid":"https://orcid.org/0000-0003-0167-9273","contributorId":184097,"corporation":false,"usgs":true,"family":"Toomey","given":"Michael","email":"mtoomey@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":835009,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sullivan, Richard","contributorId":211625,"corporation":false,"usgs":false,"family":"Sullivan","given":"Richard","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":835010,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karnauskas, Kristopher","contributorId":279498,"corporation":false,"usgs":false,"family":"Karnauskas","given":"Kristopher","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":835011,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ummenhofer, Caroline C. 0000-0002-9163-3967","orcid":"https://orcid.org/0000-0002-9163-3967","contributorId":223139,"corporation":false,"usgs":false,"family":"Ummenhofer","given":"Caroline","email":"","middleInitial":"C.","affiliations":[{"id":40678,"text":"University of New South Wales; Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":835012,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Donnelly, Jeffrey P.","contributorId":192783,"corporation":false,"usgs":false,"family":"Donnelly","given":"Jeffrey","email":"","middleInitial":"P.","affiliations":[{"id":6706,"text":"Woods Hole Oceanographic Institution,","active":true,"usgs":false}],"preferred":false,"id":835013,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70216704,"text":"70216704 - 2020 - Along-margin variations in breakup volcanism at the Eastern North American Margin","interactions":[],"lastModifiedDate":"2020-12-01T13:29:05.858719","indexId":"70216704","displayToPublicDate":"2020-11-16T07:22:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Along-margin variations in breakup volcanism at the Eastern North American Margin","docAbstract":"We model the magnetic signature of rift-related volcanism to understand the distribution and volumeofmagmatic activity that occurred during the breakup of Pangaea and early Atlantic opening at the Eastern North American Margin (ENAM).Along-strike variations in the amplitude and character of the prominent East Coast Magnetic Anomaly (ECMA) suggest that the emplacement of the volcanic layers producing this anomaly similarly varied along the margin. We use three-dimensional magnetic forward modeling constrained by seismic interpretationsto identify along-margin variations in volcanic thickness and width that can explain the observed amplitude and character of the ECMA. Our model results suggest that the ECMA is produced by a combination of both first-order (~600-1000 km)and second-order (~50-31100 km) magmatic segmentation. The first-order magmatic segmentation could have resulted from preexisting variations in crustal thickness and rheology developed during the tectonic amalgamation of Pangaea. The second-order magmatic segmentation developed during continental breakup and likely influenced the segmentation and transform fault spacing of the initial, and modern, Mid-Atlantic Ridge. These variations in magmatism showhow extension and thermal weakening was distributed at the ENAM during continental breakup and how this breakup magmatism was related to both previous and subsequent Wilson Cycle stages.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB020040","usgsCitation":"Greene, J., Tominaga, M., and Miller, N.C., 2020, Along-margin variations in breakup volcanism at the Eastern North American Margin: Journal of Geophysical Research, v. 125, no. 12, e2020JB020040, https://doi.org/10.1029/2020JB020040.","productDescription":"e2020JB020040","ipdsId":"IP-123067","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454811,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020jb020040","text":"External Repository"},{"id":380905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"East Coast of United States, Atlantic Ocean","geographicExtents":"{ \"type\": \"FeatureCollection\",\n\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5859375,\n 32.24997445586331\n ],\n [\n -67.8515625,\n 32.24997445586331\n ],\n [\n -67.8515625,\n 44.08758502824516\n ],\n [\n -75.5859375,\n 44.08758502824516\n ],\n [\n -75.5859375,\n 32.24997445586331\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Greene, John A. 0000-0002-4310-602X","orcid":"https://orcid.org/0000-0002-4310-602X","contributorId":200999,"corporation":false,"usgs":false,"family":"Greene","given":"John A.","affiliations":[],"preferred":false,"id":805943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tominaga, Masako 0000-0002-1169-4146","orcid":"https://orcid.org/0000-0002-1169-4146","contributorId":200937,"corporation":false,"usgs":false,"family":"Tominaga","given":"Masako","email":"","affiliations":[],"preferred":false,"id":805944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":805945,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227719,"text":"70227719 - 2020 - Effect of stream permanence on predation risk of lotic crayfish by riparian predators","interactions":[],"lastModifiedDate":"2022-01-27T13:35:18.953519","indexId":"70227719","displayToPublicDate":"2020-11-13T07:32:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Effect of stream permanence on predation risk of lotic crayfish by riparian predators","docAbstract":"Given the importance of crayfish in stream ecosystems, gaining insight into the role of stream permanence in maintaining predator–prey interactions is critical. Our objectives were to determine the influence of stream permanence and season on crayfish predation and assess the role of stream permanence and crayfish density on the presence of predators, while accounting for imperfect detection. We conducted surveys of crayfish density, mammalian scat, and environmental variables within 10 intermittent and 10 permanent streams in the Ozark Highlands. We used occupancy modeling to assess the relationship between predator presence, crayfish density, and environmental variables. Stream permanence did not play a role in determining relative frequency of occurrence or percent volume of crayfish prey in mammalian diets. However, percent volume and relative frequency of crayfish prey found in scats differed by season, with both highest in spring and summer. The relative frequency and percent volume of fish prey showed a significant interaction of season by stream permanence, which may be the first instance of this observation. Procyon lotor (Raccoon) had the highest detection probability (p = 0.39), whereas Neovison vison (American Mink; p = 0.15) and Lontra canadensis (River Otter; p = 0.03) had low detection probabilities. Further study into predator–prey interactions in the context of hydrology, particularly when related to imperiled groups like freshwater crayfishes, is needed since climate change is expected to alter hydrologic patterns.
Considerable effort and resources have been placed into conservation programs designed to reduce or alleviate negative environmental effects of crop production and into evaluation of the benefits of these programs. Wetlands are an important source of ecosystem services, but modeling wetland plants is an emerging science. To date, wetland plant growth has not been explicitly accounted for in ecosystem service models that quantify conservation program effects. As part of an effort to more accurately simulate wetland plants within process-based models, we expanded upon plant growth data collected in an earlier effort with additional sampling at two of four previously sampled areas, and included a fifth sampling site. We then used data from the five sites spanning five years as wetland plant parameters at both the species and functional group levels for the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model. In addition to individual species, modelers are interested in functional groups representing a collection of species because it is unrealistic to model every species occurring in an ecosystem. ALMANAC simulations were completed at three sites for both individual wetland plant species and functional groups. At each site, simulated plant yields were within 1 Mg ha–1 (±7%) of measured values (r2 = 0.99). Multisite species simulated yields were within 37% of measured values (r2 = 0.95). Functional groups performed as well as individual species simulations. Functional group simulated yields were within 1 Mg ha–1 (±5%) of measured yields. Plant growth is a major component of these wetland ecosystems, and ALMANAC verified wetland plant parameters support more accurate assessments of conservation programs and practices on the influence of wetland ecosystems embedded within agricultural fields. The improved plant parameters we provide here will be transferred to other process-based models that focus on other ecosystem components such as soil and water effects, facilitating wetland evaluations across the United States and elsewhere.
The relationship between people and wildfire has always been paradoxical: fire is an essential ecological process and management tool, but can also be detrimental to life and property. Consequently, fire regimes have been modified throughout history through both intentional burning to promote benefits and active suppression to reduce risks. Reintroducing fire and its benefits back into the Sky Island mountains of the United States-Mexico borderlands has the potential to reduce adverse effects of altered fire regimes and build resilient ecosystems and human communities. To help guide regional fire restoration, we describe the frequency and severity of recent fires over a 32-year period (1985-2017) across a vast binational region in the United States-Mexico borderlands and assess variation in fire frequency and severity across climate gradients and in relation to vegetation and land tenure classes. We synthesize relevant literature on historical fire regimes within 9 major vegetation types and assess how observed contemporary fire characteristics vary from expectations based on historical patterns. Less than 28% of the study area burned during the observation period, excluding vegetation types in warmer climates that are not adapted to fire (eg, Desertscrub and Thornscrub). Average severity of recent fires was low despite some extreme outliers in cooler, wetter environments. Midway along regional temperature and precipitation gradients, approximately 64% of Pine-Oak Forests burned at least once, with fire frequencies that mainly corresponded to historical expectations on private lands in Mexico but less so on communal lands, suggesting the influence of land management. Fire frequency was higher than historical expectations in extremely cool and wet environments that support forest types such as Spruce-Fir, indicating threats to these systems possibly attributable to drought and other factors. In contrast, fires were absent or infrequent across large areas of Woodlands (~73% unburned) and Grasslands (~88% unburned) due possibly to overgrazing, which reduces abundance and continuity of fine fuels needed to carry fire. Our findings provide a new depiction of fire regimes in the Sky Islands that can help inform fire management, restoration, and regional conservation planning, fostered by local and traditional knowledge and collaboration among landowners and managers.
","language":"English","publisher":"Sage Journals","doi":"10.1177/1178622120969191","usgsCitation":"Villarreal, M.L., Iniguez, J.M., Flesch, A.D., Sanderlin, J.S., Cortes Montano, C., Conrad, C.R., and Haire, S.L., 2020, Contemporary fire regimes provide a critical perspective on restoration needs in the Mexico-United States borderlands: Air, Soil and Water Research, v. 13, p. 1-18, https://doi.org/10.1177/1178622120969191.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-117669","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":454834,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/1178622120969191","text":"Publisher Index Page"},{"id":436720,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99S0I9W","text":"USGS data release","linkHelpText":"Differenced Normalized Burn Ratio (dNBR) data of wildfires in the Sky Island Mountains of the southwestern US and northern Mexico from 2011-2017"},{"id":380988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Sonora","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.796875,\n 29.036960648558267\n ],\n [\n -108.984375,\n 29.036960648558267\n ],\n [\n -108.984375,\n 33.54139466898275\n ],\n [\n -111.796875,\n 33.54139466898275\n ],\n [\n -111.796875,\n 29.036960648558267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":806073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iniguez, Jose M. 0000-0002-4566-1297","orcid":"https://orcid.org/0000-0002-4566-1297","contributorId":213972,"corporation":false,"usgs":false,"family":"Iniguez","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":806074,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flesch, Aaron D. 0000-0003-3434-0778","orcid":"https://orcid.org/0000-0003-3434-0778","contributorId":245372,"corporation":false,"usgs":false,"family":"Flesch","given":"Aaron","email":"","middleInitial":"D.","affiliations":[{"id":49169,"text":"School of Natural Resources and the Environment and The Desert Laboratory on Tumamoc Hill, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":806075,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanderlin, Jamie S. 0000-0001-8651-9804","orcid":"https://orcid.org/0000-0001-8651-9804","contributorId":245373,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":49171,"text":"US Forest Service, Rocky Mountain Research Station, Flagstaff, Arizona","active":true,"usgs":false}],"preferred":false,"id":806076,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cortes Montano, Citlali 0000-0002-1916-1985","orcid":"https://orcid.org/0000-0002-1916-1985","contributorId":213973,"corporation":false,"usgs":false,"family":"Cortes Montano","given":"Citlali","email":"","affiliations":[{"id":38945,"text":"Universidad Juárez del Estado de Durango","active":true,"usgs":false}],"preferred":false,"id":806077,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Conrad, Caroline Rose 0000-0002-0496-8081","orcid":"https://orcid.org/0000-0002-0496-8081","contributorId":236945,"corporation":false,"usgs":true,"family":"Conrad","given":"Caroline","email":"","middleInitial":"Rose","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":806078,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haire, Sandra L. 0000-0002-5356-7567","orcid":"https://orcid.org/0000-0002-5356-7567","contributorId":213971,"corporation":false,"usgs":false,"family":"Haire","given":"Sandra","email":"","middleInitial":"L.","affiliations":[{"id":32362,"text":"Haire Laboratory for Landscape Ecology","active":true,"usgs":false}],"preferred":false,"id":806079,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70216782,"text":"70216782 - 2020 - Recent and projected precipitation and temperature changes in the Grand Canyon area with implications for groundwater resources","interactions":[],"lastModifiedDate":"2020-12-10T13:27:13.701227","indexId":"70216782","displayToPublicDate":"2020-11-12T09:24:34","publicationYear":"2020","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":"Recent and projected precipitation and temperature changes in the Grand Canyon area with implications for groundwater resources","docAbstract":"Groundwater is a critical resource in the Grand Canyon region, supplying nearly all water needs for residents and millions of visitors. Additionally, groundwater discharging at hundreds of spring locations in and near Grand Canyon supports important ecosystems in this mostly arid environment. The security of groundwater supplies is of critical importance for both people and ecosystems in the region and the potential for changes to groundwater systems from projected climate change is a cause for concern. In this study, we analyze recent historical and projected precipitation and temperature data for the Grand Canyon region. Projected climate scenarios are then used in Soil Water Balance groundwater infiltration simulations to understand the state-of-the-science on projected changes to groundwater resources in the area. Historical climate data from 1896 through 2019 indicate multi-decadal cyclical patterns in both precipitation and temperature for most of the time period. Since the 1970s, however, a significant rising trend in temperature is observed in the area. All 10-year periods since 1993 are characterized by both below average precipitation and above average temperature. Downscaled and bias-corrected precipitation and temperature output from 97 CMIP5 global climate models for the water-year 2020–2099 time period indicate projected precipitation patterns similar to recent historical (water-year 1951–2015) data. Projected temperature for the Grand Canyon area, however, is expected to rise by as much as 3.4 °C by the end of the century, relative to the recent historical average. Integrating the effects of projected precipitation and temperature changes on groundwater infiltration, simulation results indicate that > 76% of future decades will experience average potential groundwater infiltration less than that of the recent historical period.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-76743-6","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2020, Recent and projected precipitation and temperature changes in the Grand Canyon area with implications for groundwater resources: Scientific Reports, v. 10, 19740, 11 p., https://doi.org/10.1038/s41598-020-76743-6.","productDescription":"19740, 11 p.","ipdsId":"IP-117188","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":454837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-76743-6","text":"Publisher Index Page"},{"id":381030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Colorado Plateau, Grand Canyon, Kaibab Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.97216796875,\n 35.60371874069731\n ],\n [\n -109.53369140625,\n 35.60371874069731\n ],\n [\n -109.53369140625,\n 38.35888785866677\n ],\n [\n -113.97216796875,\n 38.35888785866677\n ],\n [\n -113.97216796875,\n 35.60371874069731\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":806236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":806237,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70248052,"text":"70248052 - 2020 - Using remote sensing products to predict recovery of vegetation across space and time following energy development","interactions":[],"lastModifiedDate":"2024-05-16T15:37:39.154563","indexId":"70248052","displayToPublicDate":"2020-11-12T09:16:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Using remote sensing products to predict recovery of vegetation across space and time following energy development","docAbstract":"Using localized studies to understand how ecosystems recover can create uncertainty in recovery predictions across landscapes. Large archives of remote sensing data offer opportunities for quantifying the spatial and temporal factors influencing recovery at broad scales and predicting recovery. For example, energy production is a widespread and expanding land use among many semi-arid ecosystems of the Western United States dominated by sagebrush (Artemisia spp.), a keystone species providing a variety of ecological services. With remotely-sensed (Landsat) estimates of vegetation cover collected every 2–5 years from southwestern Wyoming, USA, over nearly three decades (1985–2015), we modeled changes in sagebrush cover on 375 former oil and gas well pads in response to weather and site-level conditions. We then used modeled relationships to predict recovery time across the landscape as an indicator of resilience for vegetation after well pad disturbances, where faster recovery indicates a greater capacity to recover when similarly disturbed. We found the rate of change in sagebrush cover generally increased with moisture and temperature, particularly at higher elevations. Rate of change in sagebrush cover also increased and decreased with greater percent sand and larger well pads, respectively. We predicted 21% of the landscape would recover to pre-disturbance conditions within 60 years, whereas other areas may require >100 years for recovery. These predictions and maps could inform future restoration efforts as they reflect resilience. This approach also is applicable to other disturbance types (e.g., fires and vegetation removal treatments) across landscapes, which can further improve conservation efforts by characterizing past conditions and monitoring trends in subsequent years.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.105872","usgsCitation":"Monroe, A., Aldridge, C.L., O’Donnell, M.S., Manier, D., Homer, C., and Anderson, P.J., 2020, Using remote sensing products to predict recovery of vegetation across space and time following energy development: Ecological Indicators, v. 110, 105872, 15 p.; 2 Data Releases, https://doi.org/10.1016/j.ecolind.2019.105872.","productDescription":"105872, 15 p.; 2 Data Releases","ipdsId":"IP-101503","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":454840,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2019.105872","text":"Publisher Index Page"},{"id":436724,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XV8GH7","text":"USGS data release","linkHelpText":"Predicted (1989-2015) and forecasted (2015-2114) estimates for rate of change and recovery of sagebrush (Artemisia spp.) following energy development in southwestern Wyoming, USA (ver. 2.0, January 2021)"},{"id":436723,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XV8GH7","text":"USGS data release","linkHelpText":"Predicted (1989-2015) and forecasted (2015-2114) estimates for rate of change and recovery of sagebrush (Artemisia spp.) following energy development in southwestern Wyoming, USA (ver. 2.0, January 2021)"},{"id":436722,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DQ5INM","text":"USGS data release","linkHelpText":"Predicted (1989-2015) and forecasted (2015-2114) estimates for rate of change and recovery of sagebrush (Artemisia spp.) following energy development in southwestern Wyoming, USA"},{"id":436721,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DQ5INM","text":"USGS data release","linkHelpText":"Predicted (1989-2015) and forecasted (2015-2114) estimates for rate of change and recovery of sagebrush (Artemisia spp.) following energy development in southwestern Wyoming, USA"},{"id":420410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.03921100550399,\n 42.92284600849075\n ],\n [\n -111.03921100550399,\n 41.03307775348375\n ],\n [\n -106.02701021455793,\n 41.03307775348375\n ],\n [\n -106.02701021455793,\n 42.92284600849075\n ],\n [\n -111.03921100550399,\n 42.92284600849075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":881652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Manier, Daniel 0000-0002-1105-1327","orcid":"https://orcid.org/0000-0002-1105-1327","contributorId":244206,"corporation":false,"usgs":true,"family":"Manier","given":"Daniel","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":238918,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":881655,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881656,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70216229,"text":"sir20205107 - 2020 - Trends in recent historical and projected climate data for the Colorado River Basin and potential effects on groundwater availability","interactions":[],"lastModifiedDate":"2020-11-10T22:06:48.291573","indexId":"sir20205107","displayToPublicDate":"2020-11-10T10:11:25","publicationYear":"2020","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":"2020-5107","displayTitle":"Trends in Recent Historical and Projected Climate Data for the Colorado River Basin and Potential Effects on Groundwater Availability","title":"Trends in recent historical and projected climate data for the Colorado River Basin and potential effects on groundwater availability","docAbstract":"Understanding recent historical and projected trends in precipitation and temperature in the Colorado River Basin, and estimating what the projected changes in these climate parameters may mean for groundwater resources in the region, is important for water managers and policymakers to sustainably manage water resources in the basin. Historical (1896–2019) precipitation and temperature data for the upper and lower Colorado River Basins were analyzed to better understand recent trends in climate data that may affect groundwater resources in the area. Historical data indicate multidecadal-scale cyclical patterns in precipitation in both the upper and lower basins. Although upper basin precipitation had no statistical trend over the recent historical period, the lower basin had a weak negative trend over this period. Multidecadal-scale cyclical patterns in temperature also are observed in historical climate data in both the upper and lower basins, at least until the early 1970s. Beginning at that time, both the upper and lower basins experienced strong, monotonic positive trends in temperature. Basic principles of hydrology indicate that periods of decreasing precipitation as well as increasing temperature would have a negative effect, that is, reduction in groundwater infiltration and hence, reduced recharge of aquifer systems.
Projected climate data from 97 Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble members across the full range of Representative Concentration Pathway (RCPs) from water years 1951 through 2099 were evaluated to understand what current global climate models are projecting about future conditions in the Colorado River Basin, and what this might mean for groundwater systems in the region. Precipitation in the upper basin is projected to increase throughout the rest of the century, rising to 6 percent above the 1951–2015 historical period by mid-century and to 9 percent above the historical period by the end of the century. Temperature in the upper basin also is projected to be above the recent historical median throughout the rest of the century, with steady warming in decadal average temperatures expected until the last quarter of this century. In contrast to projected precipitation in the upper basin, precipitation in the lower basin is projected to be the same as, or slightly less than, the historical period throughout most of the rest of this century. Like projected temperature in the upper basin, temperature in the lower basin also is projected to be above the recent historical median throughout the rest of the century. Comparing median projections for all future decades with median results from all historical decades, future precipitation is expected to be greater than that of the past in the upper basin, though no significant difference is projected for precipitation in the lower basin. Significant increases (p-value<0.05) are expected in temperature in both the upper and lower basins.
To estimate the effects of projected precipitation and temperature on groundwater systems in the region, results from the 97 member CMIP5 climate projection ensemble were used as input in a Soil-Water Balance (SWB) groundwater infiltration model for the Colorado River Basin. SWB simulation results indicate that the upper Colorado River Basin is expected to experience decades of above-historical-average groundwater infiltration through the end of the century. For the lower Colorado River Basin, simulated groundwater infiltration is projected to be consistently less than the recent (1951–2015) historical period for most of the remaining century. A comparison of the distribution of all median simulated groundwater infiltration results between recent historical and future periods indicates projected groundwater infiltration in the upper basin is significantly (p-value<0.05) greater over the combined 2020–2099 future period than the recent (1951–2015) historical period. Moreover, in 41 of 71 (58 percent) possible future decades in this century, groundwater infiltration is projected to be greater than the 75th percentile of historical simulated groundwater infiltration. Projected groundwater infiltration in the lower Colorado River Basin across all future decades is significantly less than in the historical period. Of the 71 future decades in the century, projected groundwater infiltration in the lower basin is expected to be less than the 25th percentile of historical infiltration in 55 (77 percent) of the 10-year periods. Important differences in projected precipitation between the upper (increasing precipitation) and lower (decreasing precipitation) basins largely drive the different responses of simulated groundwater infiltration in the upper (increasing infiltration) and lower (decreasing infiltration) basins. It will be useful to revisit projections in groundwater infiltration in the Colorado River Basin when more up-to-date projections of precipitation become available from the next Coupled Model Intercomparison Project phases or by using climate input developments through Regional Climate Modeling efforts and stochastic weather generators.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205107","collaboration":"Prepared in cooperation with Bureau of Reclamation","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2020, Trends in recent historical and projected climate data for the Colorado River Basin and potential effects on groundwater availability: U.S. Geological Survey Scientific Investigations Report 2020–5107, 24 p., https://doi.org/10.3133/sir20205107.","productDescription":"Report: vii, 24 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-117191","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":380358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5107/coverthb.jpg"},{"id":380361,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ST7MX7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-water balance groundwater recharge model results for the Upper Colorado River Basin (ver. 2.0, April 2017)"},{"id":380359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5107/sir20205107.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5107"},{"id":380360,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VLU0O6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-water balance groundwater infiltration model results for the Lower Colorado River Basin"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","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 -112.5,\n 30.088107753367257\n ],\n [\n -108.984375,\n 30.221101852485987\n ],\n [\n -108.21533203125,\n 31.39115752282472\n ],\n [\n -107.16064453125,\n 35.08395557927643\n ],\n [\n -105.35888671875,\n 36.12012758978146\n ],\n [\n -104.6337890625,\n 36.40359962073253\n ],\n [\n -104.96337890625,\n 37.735969208590504\n ],\n [\n -105.3369140625,\n 38.58252615935333\n ],\n [\n -104.83154296875,\n 39.027718840211605\n ],\n [\n -104.87548828125,\n 41.19518982948959\n ],\n [\n -105.05126953124999,\n 41.934976500546604\n ],\n [\n -106.3916015625,\n 43.16512263158296\n ],\n [\n -110.91796875,\n 43.100982876188546\n ],\n [\n -111.09374999999999,\n 40.64730356252251\n ],\n [\n -111.533203125,\n 39.07890809706475\n ],\n [\n -113.04931640625,\n 37.405073750176925\n ],\n [\n -114.14794921875,\n 37.10776507118514\n ],\n [\n -114.54345703125,\n 37.54457732085582\n ],\n [\n -115.46630859375,\n 38.839707613545144\n ],\n [\n -116.806640625,\n 38.805470223177466\n ],\n [\n -116.19140625,\n 37.3002752813443\n ],\n [\n -115.3564453125,\n 36.421282443649496\n ],\n [\n -116.1474609375,\n 35.55010533588552\n ],\n [\n -115.4443359375,\n 32.491230287947594\n ],\n [\n -114.93896484374999,\n 31.3348710339506\n ],\n [\n -114.2578125,\n 31.57853542647338\n ],\n [\n -113.291015625,\n 31.316101383495624\n ],\n [\n -113.04931640625,\n 30.732392734006083\n ],\n [\n -112.7197265625,\n 30.012030680358613\n ],\n [\n -112.47802734375,\n 30.012030680358613\n ],\n [\n -112.5,\n 30.088107753367257\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Arizona Water Science Center
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520 N. Park Avenue
Tucson, AZ 85719
","tableOfContents":"- Acknowledgments
- Abstract
- Introduction
- Data and Methods
- Analyses of Recent Historical Climate Data for the Colorado River Basin
- Analyses of Projected Climate Data for the Colorado River Basin
- Projected Groundwater Infiltration for the Colorado River Basin
- Summary and Conclusions
- References Cited
- Appendix 1. Computational Details and Limitations of the Soil-Water Balance Groundwater Infiltration Model
","publishedDate":"2020-11-10","noUsgsAuthors":false,"publicationDate":"2020-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":1629,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred D.","email":"ftillman@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":false,"id":804512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":804513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":804514,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70216117,"text":"sir20105070R - 2020 - Alkalic-type epithermal gold deposit model","interactions":[],"lastModifiedDate":"2024-04-16T16:38:25.784028","indexId":"sir20105070R","displayToPublicDate":"2020-11-10T09:50:00","publicationYear":"2020","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":"2010-5070","chapter":"R","title":"Alkalic-type epithermal gold deposit model","docAbstract":"This report summarizes the primary characteristics of alkalic-type epithermal gold (Au) deposits and provides an updated descriptive model. These deposits, primarily of Mesozoic to Neogene age, are among the largest epithermal gold deposits in the world. Considered a subset of low-sulfidation epithermal deposits, they are spatially and genetically linked to small stocks or clusters of intrusions containing high alkali-element contents. Deposits occur as disseminations, breccia-fillings, and veins and may be spatially and genetically related to skarns and low-grade porphyry copper (Cu) or molybdenum (Mo) systems. Gold commonly occurs as native gold, precious metal tellurides, and as sub-micron gold in arsenian pyrite. Quartz, carbonate, fluorite, adularia, and vanadian muscovite/roscoelite are the most common gangue minerals. Alkalic-type gold deposits form in a variety of geological settings including continent-arc collision zones and back-arc or post-subduction rifts that are invariably characterized by a transition from convergent to extensional or transpressive tectonics.
The geochemical compositions of alkaline igneous rocks spatially linked with these deposits span the alkaline-subalkaline transition. Their alkali enrichment may be masked by potassic alteration, but the unaltered or least altered rocks (1) have chondrite normalized patterns that are commonly light rare earth element (LREE) enriched, (2) are heavy rare earth element (HREE) depleted, and (3) have high large ion lithophile contents and variable enrichment of high-field strength elements. Radiogenic isotopes suggest a mantle derivation for the alkalic magmas but allow crustal contamination.
Oxygen and hydrogen isotope compositions show that the fluids responsible for deposit formation are dominantly magmatic, although meteoric or other external fluids (seawater, evolved groundwater) also contributed to the ore-forming fluids responsible for these deposits. Carbon and sulfur isotope compositions in vein-hosted carbonates and sulfide gangue minerals, respectively, coincide with magmatic values, although a sedimentary source of carbon and sulfur is evident in several deposits.
Deep-seated structures are critical for the upwelling of hydrous alkalic magmas and for focusing magmatic-hydrothermal fluids to the site of precious metal deposition. The source of gold, silver (Ag), tellurium (Te), vanadium (V), and fluorine (F) was probably the alkalic igneous rocks themselves, and the coexistence of native gold, gold tellurides, and roscoelite in several deposits is primarily a function of similar physicochemical conditions during deposition (for example, overlapping pH and oxygen fugacity (fO2).
Potential environmental impacts related to the mining and processing of alkalic-type epithermal gold deposits include acid mine drainage with high levels of metals, especially zinc (Zn), copper, lead (Pb), and arsenic. However, because alkalic-type gold deposits typically contain carbonates, which contribute calcium and magnesium ions that increase water hardness, aquatic life may be afforded some protection. Impacts vary widely as a function of host rocks, climate, topography, and mining methods.
Geologic mapping to (1) highlight the distribution of potassic alteration; (2) define fault density and orientation of structures; (3) determine the distribution of alkaline rocks and hydrothermal breccias; and (4) identify uniquely colored gangue minerals, such as fluorite and roscoelite, will be critical to exploration and future discoveries. Geophysical techniques that identify potassium (K) anomalies (for example, radiometric and spectroscopic surveys), as well as magnetic, resistivity, aeromagnetic, and gravity surveys, may help locate zones of high-permeability that control advecting hydrothermal fluids. Geochemical surveys that include analyses for Au, Ag, barium, Te, K, F, V, Mo, and mercury, which are key elements in these deposits, should be undertaken along with the measurement of other pathfinder elements such as arsenic, bismuth, Cu, iron, nickel, Pb, antimony, selenium, and Zn.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070R","issn":"2328-0328","usgsCitation":"Kelley, K.D., Spry, P.G., McLemore, V.T., Fey, D.L., and Anderson, E.D., 2020, Alkalic-type epithermal gold deposit model: U.S. Geological Survey Scientific Investigations Report 2010–5070–R, 74 p., https://doi.org/ 10.3133/ sir20105070R.","productDescription":"x, 74 p.","onlineOnly":"Y","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":380198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/r/sir20105070r.pdf","text":"Report","size":"11.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010–5070–R"},{"id":380197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5070/r/coverthb.jpg"}],"contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS–973
Denver, CO 80225
","tableOfContents":"- Acknowledgments
- Abstract
- Introduction
- Deposit Type and Associated Commodities
- Regional Environment
- Physical Description of Deposit
- Geophysical Characteristics
- Hypogene and Supergene Ore Characteristics
- Hypogene and Supergene Gangue Characteristics
- Geochemical Characteristics
- Stable Isotope Geochemistry
- Hydrothermal Alteration
- Petrology of Associated Igneous Rocks
- Exploration/Resource Assessment Guides
- Geoenvironmental Features and Anthropogenic Mining Effects
- Metal Mobility from Solid Mine Waste
- Past and Present Mining Methods and Ore Treatment
- Volume and Footprint of Mine Waste and Tailings
- Smelter Signatures
- Climate Effects on Geoenvironmental Signatures
- Potential Ecosystem Impacts
- References Cited
","publishedDate":"2020-11-10","noUsgsAuthors":false,"publicationDate":"2020-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelley, Karen D. 0000-0002-3232-5809 kdkelley@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":179012,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":804190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spry, Paul G.","contributorId":127351,"corporation":false,"usgs":false,"family":"Spry","given":"Paul","email":"","middleInitial":"G.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":804185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLemore, Virginia T.","contributorId":113338,"corporation":false,"usgs":true,"family":"McLemore","given":"Virginia","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":804186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fey, David L. dfey@usgs.gov","contributorId":713,"corporation":false,"usgs":true,"family":"Fey","given":"David","email":"dfey@usgs.gov","middleInitial":"L.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":804191,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":1733,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":804189,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70216387,"text":"70216387 - 2020 - Spatial variability in seasonal snowpack trends across the Rio Grande headwaters (1984 - 2017)","interactions":[],"lastModifiedDate":"2020-11-13T14:47:03.495167","indexId":"70216387","displayToPublicDate":"2020-11-10T08:42:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variability in seasonal snowpack trends across the Rio Grande headwaters (1984 - 2017)","docAbstract":"This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-20-0077.1","usgsCitation":"Sexstone, G., Penn, C.A., Liston, G., Gleason, K., Moeser, C.D., and Clow, D.W., 2020, Spatial variability in seasonal snowpack trends across the Rio Grande headwaters (1984 - 2017): Journal of Hydrometeorology, v. 21, no. 11, p. 2713-2733, https://doi.org/10.1175/JHM-D-20-0077.1.","productDescription":"21 p.","startPage":"2713","endPage":"2733","ipdsId":"IP-114071","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":454846,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-20-0077.1","text":"Publisher Index Page"},{"id":436725,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q8PYX1","text":"USGS data release","linkHelpText":"SnowModel simulations and supporting observations for the Rio Grande Headwaters, southwestern Colorado, United States, 1984 - 2017"},{"id":380501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rio Grande headwaters","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.65777587890625,\n 37.267495764381856\n ],\n [\n -105.83404541015625,\n 37.267495764381856\n ],\n [\n -105.83404541015625,\n 37.91603433975963\n ],\n [\n -107.65777587890625,\n 37.91603433975963\n ],\n [\n -107.65777587890625,\n 37.267495764381856\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Penn, Colin A. 0000-0002-5195-2744","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":203851,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liston, Glen","contributorId":244889,"corporation":false,"usgs":false,"family":"Liston","given":"Glen","affiliations":[{"id":36729,"text":"Cooperative Institute for Research in the Atmosphere","active":true,"usgs":false}],"preferred":false,"id":804853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gleason, Kelly","contributorId":244890,"corporation":false,"usgs":false,"family":"Gleason","given":"Kelly","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":804854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804855,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804856,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70216204,"text":"70216204 - 2020 - The firn meltwater Retention Model Intercomparison Project (RetMIP): Evaluation of nine firn models at four weather station sites on the Greenland ice sheet","interactions":[],"lastModifiedDate":"2020-11-10T13:04:01.696787","indexId":"70216204","displayToPublicDate":"2020-11-06T06:51:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"The firn meltwater Retention Model Intercomparison Project (RetMIP): Evaluation of nine firn models at four weather station sites on the Greenland ice sheet","docAbstract":"Perennial snow, or firn, covers 80 % of the Greenland ice sheet and has the capacity to retain surface meltwater, influencing the ice sheet mass balance and contribution to sea-level rise. Multilayer firn models are traditionally used to simulate firn processes and estimate meltwater retention. We present, intercompare and evaluate outputs from nine firn models at four sites that represent the ice sheet's dry snow, percolation, ice slab and firn aquifer areas. The models are forced by mass and energy fluxes derived from automatic weather stations and compared to firn density, temperature and meltwater percolation depth observations. Models agree relatively well at the dry-snow site while elsewhere their meltwater infiltration schemes lead to marked differences in simulated firn characteristics. Models accounting for deep meltwater percolation overestimate percolation depth and firn temperature at the percolation and ice slab sites but accurately simulate recharge of the firn aquifer. Models using Darcy's law and bucket schemes compare favorably to observed firn temperature and meltwater percolation depth at the percolation site, but only the Darcy models accurately simulate firn temperature and percolation at the ice slab site. Despite good performance at certain locations, no single model currently simulates meltwater infiltration adequately at all sites. The model spread in estimated meltwater retention and runoff increases with increasing meltwater input. The highest runoff was calculated at the KAN_U site in 2012, when average total runoff across models (±2σ) was 353±610 mm w.e. (water equivalent), about 27±48 % of the surface meltwater input. We identify potential causes for the model spread and the mismatch with observations and provide recommendations for future model development and firn investigation.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-14-3785-2020","usgsCitation":"Vandecrux, B., Mottram, R., Langen, P., Fausto, R., Olesen, M., Stevens, C.M., Verjans, V., Lee, A., Ligtenberg, S., Kuipers Munneke, P., Marchenko, S., van Pelt, W., Meyer, C.R., Simonsen, S.B., Heilig, A., Samimi, S., Marshall, S.J., Machguth, H., MacFerrin, M.J., Niwano, M., Miller, O.L., Voss, C.I., and Box, J.E., 2020, The firn meltwater Retention Model Intercomparison Project (RetMIP): Evaluation of nine firn models at four weather station sites on the Greenland ice sheet: The Cryosphere, v. 14, p. 3785-3810, https://doi.org/10.5194/tc-14-3785-2020.","productDescription":"26 p.","startPage":"3785","endPage":"3810","ipdsId":"IP-118335","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":454868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-14-3785-2020","text":"Publisher Index Page"},{"id":380331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -42.1875,\n 61.438767493682825\n ],\n [\n -22.8515625,\n 69.41124235697256\n ],\n [\n -18.984375,\n 76.01609366420995\n ],\n [\n -14.0625,\n 81.56996820323275\n ],\n [\n -66.796875,\n 80.70399666821143\n ],\n [\n -71.015625,\n 78.83606545333527\n ],\n [\n -70.3125,\n 76.9206135182968\n ],\n [\n -58.35937499999999,\n 73.92246884621463\n ],\n [\n -57.30468749999999,\n 69.16255790810501\n ],\n [\n -50.2734375,\n 60.23981116999893\n ],\n [\n -41.8359375,\n 59.17592824927136\n ],\n [\n -42.1875,\n 61.438767493682825\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2020-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Vandecrux, Baptiste","contributorId":244723,"corporation":false,"usgs":false,"family":"Vandecrux","given":"Baptiste","email":"","affiliations":[{"id":48961,"text":"Geological Survey of Denmark and Greenland, Copenhagen, Denmark.","active":true,"usgs":false}],"preferred":false,"id":804456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mottram, Ruth","contributorId":244738,"corporation":false,"usgs":false,"family":"Mottram","given":"Ruth","email":"","affiliations":[],"preferred":false,"id":804486,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langen, Peter","contributorId":244739,"corporation":false,"usgs":false,"family":"Langen","given":"Peter","email":"","affiliations":[],"preferred":false,"id":804487,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fausto, Robert","contributorId":220400,"corporation":false,"usgs":false,"family":"Fausto","given":"Robert","email":"","affiliations":[{"id":40164,"text":"Geological Survey of Denmark and Greenland","active":true,"usgs":false}],"preferred":false,"id":804488,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olesen, Martin","contributorId":244740,"corporation":false,"usgs":false,"family":"Olesen","given":"Martin","email":"","affiliations":[],"preferred":false,"id":804489,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stevens, C. Max","contributorId":244741,"corporation":false,"usgs":false,"family":"Stevens","given":"C.","email":"","middleInitial":"Max","affiliations":[],"preferred":false,"id":804490,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Verjans, Vincent","contributorId":244742,"corporation":false,"usgs":false,"family":"Verjans","given":"Vincent","email":"","affiliations":[],"preferred":false,"id":804491,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lee, Amber","contributorId":244743,"corporation":false,"usgs":false,"family":"Lee","given":"Amber","email":"","affiliations":[],"preferred":false,"id":804492,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ligtenberg, Stefan","contributorId":244744,"corporation":false,"usgs":false,"family":"Ligtenberg","given":"Stefan","email":"","affiliations":[],"preferred":false,"id":804493,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kuipers Munneke, Peter","contributorId":220418,"corporation":false,"usgs":false,"family":"Kuipers Munneke","given":"Peter","email":"","affiliations":[{"id":40168,"text":"IMAU, Utrecht University","active":true,"usgs":false}],"preferred":false,"id":804494,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Marchenko, Sergey S.","contributorId":93368,"corporation":false,"usgs":true,"family":"Marchenko","given":"Sergey S.","affiliations":[],"preferred":false,"id":804495,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"van Pelt, Ward","contributorId":244745,"corporation":false,"usgs":false,"family":"van Pelt","given":"Ward","email":"","affiliations":[],"preferred":false,"id":804496,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Meyer, Colin R.","contributorId":244746,"corporation":false,"usgs":false,"family":"Meyer","given":"Colin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":804497,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Simonsen, Sebastian B.","contributorId":244747,"corporation":false,"usgs":false,"family":"Simonsen","given":"Sebastian","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":804498,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Heilig, Achim","contributorId":244748,"corporation":false,"usgs":false,"family":"Heilig","given":"Achim","email":"","affiliations":[],"preferred":false,"id":804499,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Samimi, Samira","contributorId":244749,"corporation":false,"usgs":false,"family":"Samimi","given":"Samira","email":"","affiliations":[],"preferred":false,"id":804500,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Marshall, Shawn J.","contributorId":75368,"corporation":false,"usgs":true,"family":"Marshall","given":"Shawn","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":804501,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Machguth, Horst","contributorId":220463,"corporation":false,"usgs":false,"family":"Machguth","given":"Horst","email":"","affiliations":[],"preferred":false,"id":804502,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"MacFerrin, Michael J.","contributorId":220462,"corporation":false,"usgs":false,"family":"MacFerrin","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":804503,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Niwano, Masashi","contributorId":244750,"corporation":false,"usgs":false,"family":"Niwano","given":"Masashi","email":"","affiliations":[],"preferred":false,"id":804504,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":219231,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804505,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":804506,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Box, Jason E.","contributorId":198809,"corporation":false,"usgs":false,"family":"Box","given":"Jason","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":804507,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}}
,{"id":70217299,"text":"70217299 - 2020 - Short-term impact of sediment addition on plants and invertebrates in a southern California salt marsh","interactions":[],"lastModifiedDate":"2021-01-18T13:48:26.23386","indexId":"70217299","displayToPublicDate":"2020-11-05T07:44:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Short-term impact of sediment addition on plants and invertebrates in a southern California salt marsh","docAbstract":"The implementation and monitoring of management strategies is integral to protect coastal marshes from increased inundation and submergence under sea-level rise. Sediment addition is one such strategy in which sediment is added to marshes to raise relative elevations, decrease tidal inundation, and enhance ecosystem processes. This study looked at the plant and invertebrate community responses over 12 months following a sediment addition project on a salt marsh located in an urbanized estuary in southern California, USA. This salt marsh is experiencing local subsidence, is sediment-limited from landscape modifications, has resident protected species, and is at-risk of submergence from sea-level rise. Abiotic measurements, invertebrate cores, and plant parameters were analyzed before and after sediment application in a before-after-control-impact (BACI) design. Immediately following the sediment application, plant cover and invertebrate abundance decreased significantly, with smothering of existing vegetation communities without regrowth, presumably creating resulting harsh abiotic conditions. At six months after the sediment application treatment, Salicornia bigelovii minimally colonized the sediment application area, and Spartina foliosa spread vegetatively from the edges of the marsh; however, at 12 months following sediment application overall plant recovery was still minimal. Community composition of infaunal invertebrates shifted from a dominance of marsh-associated groups like oligochaetes and polychaetes to more terrestrial and more mobile dispersers like insect larvae. In contrast to other studies, such as those with high organic deposition, that showed vegetation and invertebrate community recovery within one year of sediment application, our results indicated a much slower recovery following a sediment addition of 32 cm which resulted in a supratidal elevation with an average of 1.62 m (NAVD88) at our sampling locations. Our results indicate that the site did not recover after one year and that recovery may take longer which illustrates the importance of long-term monitoring to fully understand restoration trajectories and inform adaptive management. Testing and monitoring sea-level rise adaptation strategies like sediment addition for salt marshes is important to prevent the loss of important coastal ecosystems.
GRACE satellite data are widely used to estimate groundwater storage (GWS) changes in aquifers globally; however, comparisons with GW monitoring and modeling data are limited. Here we compared GWS changes from GRACE over 15 yr (2002–2017) in 14 major U.S. aquifers with groundwater-level (GWL) monitoring data in ~23,000 wells and with regional and global hydrologic and land surface models. Results show declining GWS trends from GRACE data in the six southwestern and south-central U.S. aquifers, totaling −90 km3 over 15 yr, related to long-term (5–15 yr) droughts, and exceeding Lake Mead volume by ~2.5×. GWS trends in most remaining aquifers were stable or slightly rising. GRACE-derived GWS changes agree with GWL monitoring data in most aquifers (correlation coefficients, R = 0.52–0.95), showing that GRACE satellites capture groundwater (GW) dynamics. Regional GW models (eight models) generally show similar or greater GWS trends than those from GRACE. Large discrepancies in the Mississippi Embayment aquifer, with modeled GWS decline approximately four times that of GRACE, may reflect uncertainties in model storage parameters, stream capture, pumpage, and/or recharge rates. Global hydrologic models (2003–2014), which include GW pumping, generally overestimate GRACE GWS depletion (total: approximately −172 to −186 km3) in heavily exploited aquifers in southwestern and south-central U.S. by ~2.4× (GRACE: −74 km3), underscoring needed modeling improvements relative to anthropogenic impacts. Global land surface models tend to track GRACE GWS dynamics better than global hydrologic models. Intercomparing remote sensing, monitoring, and modeling data underscores the importance of considering all data sources to constrain GWS uncertainties.
Blue Lake crater (<3 ka) is monogenetic volcano that produced one of the youngest eruptions in the central Oregon Cascades. Understanding monogenetic volcano behavior – from storage through eruption – is imperative in planning for future eruptions. Here we combine physical volcanology and geochemistry to determine the pre-eruptive storage conditions, ascent rate, eruption style, and deposit distribution of this young eruption. We find that the eruption of Blue Lake was initially phreatomagmatic, producing lithic-rich fall deposits and thin surge deposits and excavating the maar crater, before transitioning rapidly to a final voluminous magmatic-volatile driven explosive eruption. The mapped fall deposit has an estimated volume of 3.9 × 107 m3 (2.2 × 107 m3 DRE) which suggests a VEI of 3. Although similar in magnitude (as measured by fall deposit volume) to many other recent cinder cone eruptions in the Cascades, the Blue Lake crater eruption lacks an effusive phase. The absence of lava flows may reflect the lack of evidence for syn-eruptive magma storage at shallow levels. Indeed, corrected volatile contents of olivine-hosted melt inclusions (2.9–4.2 wt% H2O, 910–1330 ppm CO2) are strikingly uniform and indicate storage and crystallization at a restricted pressure range (average ~ 235 MPa), equating to a depth of ~8.6 km. Melt inclusion geochemistry indicates that the basaltic andesite magma cooled and crystallized ~25% during storage at this pressure. Crystals in the Blue Lake magma show evidence of mixing with, or entrainment in, a more evolved magma. Feldspar crystals have large An-rich cores (An80–85) and abrupt An-poor rims (An60–70); olivine crystals have large, broad cores (~Fo82–84) and thin rims with lower Fo and NiO contents. Diffusion modeling of olivine zoning suggests that an intrusion event occurred ~10–60 days prior to eruption. Diffusive loss of H+ from melt inclusions was minimal (<1.3 wt% H2O) during magma ascent, from which we calculate minimum ascent times from 235 MPa of <1 day. Many inclusions indicate ascent times of <3 h, corresponding to ascent rates of ~1 to >13 m/s. This study illustrates the pre-eruptive and eruptive complexities of monogenetic volcanoes and highlights the minimal warning that may precede future eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.107103","usgsCitation":"Johnson, E.R., and Cashman, K., 2020, Understanding the storage conditions and fluctuating eruption style of a young monogenetic volcano: Blue Lake crater (<3 ka), High Cascades, Oregon: Journal of Volcanology and Geothermal Research, v. 408, 107103, 13 p., https://doi.org/10.1016/j.jvolgeores.2020.107103.","productDescription":"107103, 13 p.","ipdsId":"IP-123764","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":392386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.79752349853516,\n 44.40202390088682\n ],\n [\n -121.7233657836914,\n 44.40202390088682\n ],\n [\n -121.7233657836914,\n 44.451183121531336\n ],\n [\n -121.79752349853516,\n 44.451183121531336\n ],\n [\n -121.79752349853516,\n 44.40202390088682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"408","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Emily Renee 0000-0002-7967-6913","orcid":"https://orcid.org/0000-0002-7967-6913","contributorId":269628,"corporation":false,"usgs":true,"family":"Johnson","given":"Emily","email":"","middleInitial":"Renee","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":827605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cashman, Katharine V.","contributorId":40097,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine V.","affiliations":[],"preferred":false,"id":827606,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70216900,"text":"70216900 - 2020 - Evidence for an established population of tegu lizards Salvator merianae in southeastern Georgia, USA","interactions":[],"lastModifiedDate":"2020-12-16T13:05:58.261555","indexId":"70216900","displayToPublicDate":"2020-11-04T07:37:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evidence for an established population of tegu lizards (Salvator merianae) in southeastern Georgia, USA","title":"Evidence for an established population of tegu lizards Salvator merianae in southeastern Georgia, USA","docAbstract":"Documenting emergence of invasive species in new areas is vital to understanding spatiotemporal patterns of invasions, propagule pressure, and the risk of establishment. Salvator merianae (Argentine Giant Tegu) has established multiple unconnected populations in southern and Central Florida, and additional sightings have been reported elsewhere in the state. In 2018, land managers in Georgia received >20 reports of this species in the wild. To evaluate the probability of establishment, we assembled verified records of the non-native Argentine Giant Tegu in Georgia over the past nine years. We report on 47 tegu observations throughout Georgia, with a concentration of sightings (n = 38) in Toombs and Tattnall counties. In 2019, we used modified Havahart traps and captured adult male and female tegus at one of our three locations during 3085 corrected trap nights. While we did not find evidence of a well-established population (i.e., varied size structure of tegus captured) with our limited trapping effort, we suspect tegus are breeding in Toombs and Tattnall counties due to the concentration of captures and reports of adult males and females, the consistent reports of adults across years, the confirmed presence of tegus in 2018, 2019 and 2020, and the reproductive capacity (i.e., turgid testes and secondary follicles) of tegus captured. Ongoing tegu introductions from captivity are likely to maintain high propagule pressure in the southeastern United States. Effective early detection, funded rapid response networks, and public outreach to solicit reports of tegu sightings are critical to prevent establishment and associated ecological impacts of this invasive species elsewhere in the southeastern US.","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.019.0404","usgsCitation":"Haro, D., McBrayer, L., Jenson, J.B., Gillis, J., Bonewell, L.R., Nafus, M., Greiman, S.E., Reed, R., and Yackel Adams, A.A., 2020, Evidence for an established population of tegu lizards Salvator merianae in southeastern Georgia, USA: Southeastern Naturalist, v. 19, no. 4, p. 649-662, https://doi.org/10.1656/058.019.0404.","productDescription":"14 p.","startPage":"649","endPage":"662","ipdsId":"IP-120395","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436729,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GW87JH","text":"USGS data release","linkHelpText":"Salvator merianae trapping in Georgia, USA"},{"id":436728,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GW87JH","text":"USGS data release","linkHelpText":"Salvator merianae trapping in Georgia, USA"},{"id":381321,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haro, Daniel","contributorId":245712,"corporation":false,"usgs":false,"family":"Haro","given":"Daniel","email":"","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":806867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McBrayer, Lance","contributorId":245713,"corporation":false,"usgs":false,"family":"McBrayer","given":"Lance","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":806868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jenson, John B","contributorId":245714,"corporation":false,"usgs":false,"family":"Jenson","given":"John","email":"","middleInitial":"B","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":806869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillis, James","contributorId":245715,"corporation":false,"usgs":false,"family":"Gillis","given":"James","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":806870,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonewell, Lea R. 0000-0002-0606-6954","orcid":"https://orcid.org/0000-0002-0606-6954","contributorId":245716,"corporation":false,"usgs":true,"family":"Bonewell","given":"Lea","email":"","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":806871,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nafus, Melia Gail 0000-0002-7325-3055","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":245717,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia Gail","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":806872,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Greiman, Stephen E.","contributorId":190336,"corporation":false,"usgs":false,"family":"Greiman","given":"Stephen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":806873,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":806874,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":806875,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
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