The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models.
Long-term research on large ungulate populations typically conjures perceptions of extensive (and expensive) animal capture and telemetry work, and subsequent advanced modeling of resource selection and population dynamics that inform management decisions. In contrast, studies lacking a telemetry component are often limited to animal behavior or natural history. Although compelling from a standpoint of advancing understanding of ecological and evolutionary processes, results from the latter can be unfairly labeled as esoteric because they are not easily transferable to resource managers or may not provide exceptional interest to a general public drawn to these charismatic megafuana. Such dichotomies are not predetermined, however, because study conditions exist where dedicated academic researchers on tight budgets can achieve results relevant to ecology and management.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21599","usgsCitation":"Ricca, M.A., 2019, Population ecology of Roosevelt elk: Conservation and management in Redwood National and State Parks. Butch Weckerly. 2017. University of Nevada Press, Reno, Nevada, USA. 224 pp. $54.95 hardback. ISBN 978- 1943859504.: Journal of Wildlife Management, v. 83, p. 243-244, https://doi.org/10.1002/jwmg.21599.","productDescription":"2 p.","startPage":"243","endPage":"244","ipdsId":"IP-101496","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":414892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"83","noUsgsAuthors":false,"publicationDate":"2018-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867920,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216137,"text":"70216137 - 2019 - Constraining dissolved organic matter sources and temporal variability in a model sub-Arctic lake","interactions":[],"lastModifiedDate":"2020-11-06T13:57:10.563433","indexId":"70216137","displayToPublicDate":"2019-11-18T07:51:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Constraining dissolved organic matter sources and temporal variability in a model sub-Arctic lake","docAbstract":"Circumpolar lakes comprise ~ 1.4 million km2 of arctic and subarctic landscapes and are vulnerable to change in vegetation, permafrost distribution, and hydrological conditions in response to climate warming. However, the composition and cycling of dissolved organic matter (DOM) is poorly understood for these lakes because most are remote and unstudied. The goal of this study was to assess timescale and source controls on DOM composition in Canvasback Lake, a shallow, sub-Arctic lake in interior Alaska with similar hydrologic and geomorphic characteristics to about a quarter of circumpolar lake ecosystems. Lake dissolved organic carbon (DOC) concentration varied by as much as 16% from the mean (3.34 mg L−1 change) through diel cycles in spring 2016 to fall 2017 and was accompanied by minor changes in DOM composition. At the seasonal scale, DOC concentration increased from spring through fall to very high concentrations under ice in winter. Decreases in both condensed aromatic and polyphenolic compound classes and lignin carbon-normalized yield, plus increased relative abundance of aliphatic compounds, suggests that DOM composition shifts from a pulse of allochthonous DOM in the spring to more autochthonous under-ice. These changes highlight the seasonally-dynamic nature of DOM in circumpolar lakes that are poorly captured by single-visit lake surveys and underscores the need to measure DOM properties and fate consistently across multiple timescales (i.e. seasonally) to better constrain the role of DOM in lake processes. To further assess DOM sources, a suite of endmember leachates were compared to bulk lake DOM, indicating solely allochthonous inputs are not well reflected in lake DOM, highlighting the role of degradation processes or mixing with autochthonous sources. Thus, Canvasback Lake appears less well connected to terrestrial inputs compared to past studies of northern high-latitude lakes and does not behave as previous boreal lake models suggest.
Vaccines that autonomously transfer among individuals have been proposed as a strategy to control infectious diseases within inaccessible wildlife populations. However, rates of vaccine spread and epidemiological efficacy in real-world systems remain elusive. Here, we investigate whether topical vaccines that transfer among individuals through social contacts can control vampire bat rabies—a medically and economically important zoonosis in Latin America. Field experiments in three Peruvian bat colonies, which used fluorescent biomarkers as a proxy for the bat-to-bat transfer and ingestion of an oral vaccine, revealed that vaccine transfer would increase population-level immunity up to 2.6 times beyond the same effort using conventional, non-spreadable vaccines. Mathematical models showed that observed levels of vaccine transfer would reduce the probability, size and duration of rabies outbreaks, even at low but realistically achievable levels of vaccine application. Models further predicted that existing vaccines provide substantial advantages over culling bats—the policy currently implemented in North, Central and South America. Linking field studies with biomarkers to mathematical models can inform how spreadable vaccines may combat pathogens of health and conservation concern before costly investments in vaccine design and testing.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41559-019-1032-x","usgsCitation":"Bakker, K.M., Rocke, T.E., Osorio, J., Abbott, R.C., Tello, C., Carerra, J., Valderrama, W., Shiva, C., Falcon, N., and Streicker, D.G., 2019, Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats: Nature Ecology and Evotution, v. 3, p. 1697-1704, https://doi.org/10.1038/s41559-019-1032-x.","productDescription":"8 p.","startPage":"1697","endPage":"1704","ipdsId":"IP-109945","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":459171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41559-019-1032-x","text":"Publisher Index Page"},{"id":380001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2019-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bakker, Kevin M. 0000-0002-7084-9291","orcid":"https://orcid.org/0000-0002-7084-9291","contributorId":244266,"corporation":false,"usgs":false,"family":"Bakker","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":48876,"text":"Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical,","active":true,"usgs":false}],"preferred":false,"id":803632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":803631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Osorio, Jorge E.","contributorId":50392,"corporation":false,"usgs":false,"family":"Osorio","given":"Jorge E.","affiliations":[{"id":13052,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":803633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abbott, Rachel C. 0000-0003-4820-9295 rabbott@usgs.gov","orcid":"https://orcid.org/0000-0003-4820-9295","contributorId":1183,"corporation":false,"usgs":true,"family":"Abbott","given":"Rachel","email":"rabbott@usgs.gov","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":803634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tello, Carlos","contributorId":244267,"corporation":false,"usgs":false,"family":"Tello","given":"Carlos","email":"","affiliations":[{"id":48877,"text":"ILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":803635,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carerra, Jorge","contributorId":244268,"corporation":false,"usgs":false,"family":"Carerra","given":"Jorge","email":"","affiliations":[{"id":48877,"text":"ILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":803636,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Valderrama, William","contributorId":244269,"corporation":false,"usgs":false,"family":"Valderrama","given":"William","email":"","affiliations":[{"id":48878,"text":"eILLARIY, Asociaci´on para el Desarrollo y Conservaci´on de los Recursos Naturales Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":803637,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shiva, Carlos","contributorId":244270,"corporation":false,"usgs":false,"family":"Shiva","given":"Carlos","email":"","affiliations":[{"id":48879,"text":"hFaculty of Veterinary Medicine and Zootechnics, Universidad Peruana Cayetano, Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":803638,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Falcon, Nestor","contributorId":244271,"corporation":false,"usgs":false,"family":"Falcon","given":"Nestor","email":"","affiliations":[{"id":48879,"text":"hFaculty of Veterinary Medicine and Zootechnics, Universidad Peruana Cayetano, Lima, Peru","active":true,"usgs":false}],"preferred":false,"id":803639,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Streicker, Daniel G. 0000-0001-7475-2705","orcid":"https://orcid.org/0000-0001-7475-2705","contributorId":152378,"corporation":false,"usgs":false,"family":"Streicker","given":"Daniel","email":"","middleInitial":"G.","affiliations":[{"id":12473,"text":"University of Glasgow","active":true,"usgs":false}],"preferred":false,"id":803640,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215773,"text":"70215773 - 2019 - Behavioural plasticity modulates temperature-related constraints on foraging time for a montane mammal","interactions":[],"lastModifiedDate":"2020-10-29T22:32:26.824141","indexId":"70215773","displayToPublicDate":"2019-11-17T17:27:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Behavioural plasticity modulates temperature-related constraints on foraging time for a montane mammal","docAbstract":"The California Department of Water Resources (DWR) requested analysis of juvenile Chinook salmon survival in the Sacramento-San Joaquin River Delta (henceforth identified as “the Delta”) as part of an effects analysis that will be included in an Incidental Take Permit (ITP) Application. This application is in compliance with the California Endangered Species Act (CESA) and Environmental Impact Report (EIR), which is itself in compliance with California Environmental Quality Act (CEQA). DWR is seeking an ITP and preparing CEQA compliance documentation for long-term operation of the State Water Project (SWP). DWR requested assistance from the U.S. Geological Survey to aid in determining the effect of two proposed projects on juvenile Chinook salmon (Oncorhynchus tshawytscha) populations migrating through the Delta. Therefore, in this report we analyzed an 82-year time series of simulated river flows and Delta Cross Channel (DCC) gate operations under three scenarios constructed for the ITP: the proposed project (PP), the second proposed project (PP2b) and the existing (EX) scenarios.
To evaluate the proposed projects (PP and PP2b), we used the STARS model (Survival, Travel time, And Routing Simulation model), a stochastic, individual-based simulation model designed to predict survival of a cohort of fish that experience variable daily river flows during migration through the Delta. The STARS model uses parameter estimates from a Bayesian mark-recapture model that jointly estimates travel time and survival in eight discrete reaches of the Delta and migration routing at two key river junctions.
By applying the STARS model to the three 82-year scenarios, we found that both proposed projects had negative effects on survival, travel time, and routing in November but slightly positive effects in October, December, and May, and in June for only the PP. In November, there was a high probability that survival for PP and PP2b were less than EX and that travel time and routing to the Interior Delta for PP and PP2b were greater than for EX. We found that the magnitude of the difference in survival between scenarios was large in some years. For example, survival under both the PP and PP2b scenarios were 10 percent lower than EX in 25 percent of the water years in November. During this period, inflow to the Delta tended to be lower under the PP and PP2b scenarios, and the DCC gate was open more frequently under the PP and PP2b scenarios relative to the EX scenario. Lower inflow reduces survival, and more frequent operation of the DCC gate 1) increases the proportion of fish entering the Interior Delta, where survival is low, and thus 2) reduces survival in the Sacramento River in reaches downstream of the DCC. In contrast, during October, December, May (both PP and PP2b), and June (PP only), survival was slightly higher, travel times were lower, and routing to the Interior Delta was lower under the PP and PP2b relative to the EX scenario in the same time period, although the magnitude of the increase was relatively small in most years (less than two percent). This difference between scenarios was driven by higher river flows in some years under the PP and PP2b relative to the EX scenario. Overall, the differences in survival, travel time, and routing distance between the three operational scenarios were primarily driven by the timing and magnitude of the annual high river flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191127","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Perry, R.W., Hansen, A.C., Evans, S.D., and Kock, T.J., 2019, Using the STARS Model to evaluate the effects of two proposed projects for the long-term operation of State Water Project Incidental Take Permit Application and CEQA compliance (ver. 2.0, February 2020): U.S. Geological Survey Open-File Report 2019-1127, 39 p. plus appendixes, https://doi.org/10.3133/ofr20191127.","productDescription":"Report: vii, 31 p.; Appendixes 1-8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112215","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":372670,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix7.pdf","text":"Appendix 7","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 7","linkHelpText":"– Simulated Daily Routing by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"},{"id":369242,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix2.pdf","text":"Appendix 2","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 2","linkHelpText":"– Simulated Daily Travel Time by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":372669,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix6.pdf","text":"Appendix 6","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 6","linkHelpText":"– Simulated Daily Travel Time by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"},{"id":372668,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix5.pdf","text":"Appendix 5","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 5","linkHelpText":"– Simulated Daily Survival by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"},{"id":369244,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix4.pdf","text":"Appendix 4","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 4","linkHelpText":"– Simulated Proportion of Fish Entering the Interior Delta by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":369243,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix3.pdf","text":"Appendix 3","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 3","linkHelpText":"– Simulated Daily Routing by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":369239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1127/coverthb2.jpg"},{"id":369240,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127"},{"id":369241,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix1.pdf","text":"Appendix 1","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 1","linkHelpText":"– Simulated Daily Survival by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":372672,"rank":11,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1127/versionHist.txt","size":"8 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":372671,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix8.pdf","text":"Appendix 8","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 8","linkHelpText":"– Simulated Proportion of Fish Entering the Interior Delta by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"}],"country":"United States","state":"California ","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.9150390625,\n 38.46219172306828\n ],\n [\n -122.1240234375,\n 38.46219172306828\n ],\n [\n -122.1240234375,\n 38.993572058209466\n ],\n [\n -122.9150390625,\n 38.993572058209466\n ],\n [\n -122.9150390625,\n 38.46219172306828\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: November 2019; Version 2.0: February 2020","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115–5016
Simultaneous autoregressive (SAR) models are useful for accommodating various forms of dependence among data that have discrete support in a space of interest. These models are often specified hierarchically as mixed-effects regression models with first-moment structure controlled by a conventional linear regression term and second-moment structure induced by correlated random effects. In their general form, SAR models resemble conditional autoregressive (CAR) models, and can be made equivalent but are often parameterized differently. Importantly, SAR models can be specified by simultaneously regressing a discrete spatial process on itself. Thus, they allow one to construct statistical models for processes with directional graphical properties that pertain to data generating mechanisms. Most commonly SAR models have been used to account for structure among data with areal spatial support in applications involving ecology, epidemiology, sociology, and environmental science.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wiley StatsRef: Statistics reference online","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781118445112.stat08208","usgsCitation":"Hooten, M., Ver Hoef, J.M., and Hanks, E., 2019, Simultaneous autoregressive (SAR) model, chap. of Wiley StatsRef: Statistics reference online, HTML Document, https://doi.org/10.1002/9781118445112.stat08208.","productDescription":"HTML Document","ipdsId":"IP-105139","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":388871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-11-15","publicationStatus":"PW","contributors":{"authors":[{"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":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ver Hoef, Jay M.","contributorId":265330,"corporation":false,"usgs":false,"family":"Ver Hoef","given":"Jay","email":"","middleInitial":"M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":822561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim M.","contributorId":265331,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim M.","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":822562,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236884,"text":"70236884 - 2019 - On the utilization of synthetic and measured earthquake ground motions for designing building monitoring systems in the near-field of major faults","interactions":[],"lastModifiedDate":"2022-09-21T13:32:08.495923","indexId":"70236884","displayToPublicDate":"2019-11-15T08:25:34","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"On the utilization of synthetic and measured earthquake ground motions for designing building monitoring systems in the near-field of major faults","docAbstract":"Agencies and research groups engaged in studying measures for enhancing the resiliency of communities have recently placed emphasis on the need for extensive implementation of monitoring systems for rapid post-event assessment of structural integrity. Designing a monitoring system for a building requires a thorough knowledge of its potential nonlinear dynamic behavior with an associated localization of interstory drift. Extending this task across a regional scale becomes even more challenging because of the heterogeneity of the buildings inventory and the limited knowledge of the characteristics of the demand especially for sites located in the near-field of a major fault.\nThe existing observational database of near-field ground motion records is in fact too limited to constitute a comprehensive basis for full understanding of the potential range of structural response variability at different locations near a major fault. In addition, current insight into monitoring system design typically relies on linear structural models and sensors deployed on a limited number of floors.\nIn this context, this paper presents first results of a study that combines synthetic earthquake ground motions generated from a massively parallel regional-scale geophysics wave propagation model at frequencies of engineering interest (0-5 Hz) with nonlinear tall building models. The objective is to gain new insight into the potential impact of localization of nonlinearities in structures subjected to realistic near-field earthquakes and develop a methodology that optimizes the deployment of sensors at the building and site level. In addition to the large database of synthetic motions, available real records are also employed to compare and contrast with the trends observed using synthetic ground motions.\nPreliminary results confirm a tendency of the demand to localize in specific portions of the structure, especially when nonlinearities occur. The building analyses provide guidance for various sensor deployment configurations associated with different probability of error in measuring structural drifts.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structural health monitoring 2019: Enabling intelligent life-cycle health management for industry internet of things (IIOT)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Structural Health Monitoring 2019","conferenceDate":"September 10-12, 2019","language":"English","publisher":"DEStech Publications Inc.","doi":"10.12783/shm2019/32124","usgsCitation":"Petrone, F., McCallen, D., and Celebi, M., 2019, On the utilization of synthetic and measured earthquake ground motions for designing building monitoring systems in the near-field of major faults, in Structural health monitoring 2019: Enabling intelligent life-cycle health management for industry internet of things (IIOT), September 10-12, 2019, https://doi.org/10.12783/shm2019/32124.","ipdsId":"IP-107991","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-11-15","publicationStatus":"PW","contributors":{"editors":[{"text":"Miah, Mamun","contributorId":296778,"corporation":false,"usgs":false,"family":"Miah","given":"Mamun","email":"","affiliations":[{"id":64169,"text":"Lawrance Berkeley Lab","active":true,"usgs":false}],"preferred":false,"id":852463,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Petrone, Floriana","contributorId":296776,"corporation":false,"usgs":false,"family":"Petrone","given":"Floriana","email":"","affiliations":[{"id":64168,"text":"Larance Berkeley Lab","active":true,"usgs":false}],"preferred":false,"id":852460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCallen, David","contributorId":296777,"corporation":false,"usgs":false,"family":"McCallen","given":"David","affiliations":[{"id":64169,"text":"Lawrance Berkeley Lab","active":true,"usgs":false}],"preferred":false,"id":852461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852462,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206669,"text":"70206669 - 2019 - Response of nitrogen loading to the Chesapeake Bay to source reduction and land use change scenarios: A SPARROW‐informed analysis","interactions":[],"lastModifiedDate":"2021-07-02T13:41:48.840431","indexId":"70206669","displayToPublicDate":"2019-11-14T15:23:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Response of nitrogen loading to the Chesapeake Bay to source reduction and land use change scenarios: A SPARROW‐informed analysis","docAbstract":"In response to concerns regarding the health of streams and receiving waters, the United States Environmental Protection Agency established a total maximum daily load for nitrogen in the Chesapeake Bay watershed for which practices must be in place by 2025 resulting in an expected 25% reduction in load from 2009 levels. The response of total nitrogen (TN) loads delivered to the Bay to nine source reduction and land use change scenarios was estimated using a Spatially Referenced Regression on Watershed Attributes model. The largest predicted reduction in TN load delivered to the Bay was associated with a scenario in which the mass of TN as fertilizer applied to agricultural lands was decreased. A 25% decrease in the mass of TN applied as fertilizer resulted in a predicted reduction in TN loading to the Bay of 11.3%, which was 2.5–5 times greater than the reductions predicted by other scenarios. Eliminating fertilizer application to all agricultural land in the watershed resulted in a predicted reduction in TN load to the Bay of 45%. It was estimated that an approximate 25% reduction in TN loading to the Bay could be achieved by eliminating fertilizer applied to the 7% of subwatersheds contributing the greatest fertilizer‐sourced TN loads to the Bay. These results indicate that management strategies aimed at decreasing loading from a small number of subwatersheds may be effective for reducing TN loads to the Bay, and similar analyses are possible in other watersheds.","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12807","usgsCitation":"Miller, M., Capel, P.D., Garcia, A.M., and Ator, S., 2019, Response of nitrogen loading to the Chesapeake Bay to source reduction and land use change scenarios: A SPARROW‐informed analysis: Journal of the American Water Resources Association, v. 56, no. 1, p. 100-112, https://doi.org/10.1111/1752-1688.12807.","productDescription":"13 p.","startPage":"100","endPage":"112","ipdsId":"IP-099507","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction 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Program","active":true,"usgs":true}],"preferred":true,"id":775321,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Ana M. 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":207567,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ator, Scott W. 0000-0002-9186-4837","orcid":"https://orcid.org/0000-0002-9186-4837","contributorId":210852,"corporation":false,"usgs":true,"family":"Ator","given":"Scott W.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":775322,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205290,"text":"ofr20191104 - 2019 - Instructions for running the analytical code PAT (Purge Analyzer Tool) for computation of in-well time of travel of groundwater under pumping conditions","interactions":[],"lastModifiedDate":"2019-11-14T10:03:07","indexId":"ofr20191104","displayToPublicDate":"2019-11-14T11:20:00","publicationYear":"2019","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":"2019-1104","displayTitle":"Instructions for Running the Analytical Code PAT (Purge Analyzer Tool) for Computation of In-Well Time of Travel of Groundwater under Pumping Conditions","title":"Instructions for running the analytical code PAT (Purge Analyzer Tool) for computation of in-well time of travel of groundwater under pumping conditions","docAbstract":"Understanding the optimal time needed to purge a well while pumping to collect a representative groundwater sample requires an understanding of groundwater flow in wells (in-well flow). Parameters that affect in-well flow include the hydraulic properties of the aquifer, well construction, drawdown from pumping, and pump rate. The time of travel relative to in-well flow is affected by the pump’s intake location. The Purge Analyzer Tool (PAT) incorporates hydraulic calculations to help assess the optimal purge times required to vertically transport groundwater in the well to the pump intake (Harte, 2017). Harte (2017) includes a discussion on the rationale for determining in-well groundwater flow and time of travel and also discusses the limitations inherent in the PAT; an understanding of the limitations is important to ensure proper use.
The PAT calculates flow by use of the Dupuit-Theim equation (Lohman, 1979) that assumes steady-state radial flow and a total inflow from the well opening or screen equal to the pumping rate (eq. 1). A bulk average hydraulic conductivity (Kavg) is derived from this relationship. Once Kavg is calculated, the program calculates incremental (layered) horizontal radial inflow into the well over user defined increments (layers). These defined increments represent the screen or well opening as a fraction of the total inflow. The amount of inflow per layer is proportional to the user-defined layered distribution of hydraulic conductivity (Klayer) because drawdown is assumed to be uniformly distributed in the well. The water budget equation that guides the solution of the PAT (eq. 1) is specified as:
Qp = Qv + QH + Qw (1)
where
QP is pumping rate,
Qv is vertical flow entering the boundary of the mixing zone (Mz) from the summation of layered radial flow (∑Qhl-n) where l-n denotes number of layers,
QH is horizontal radial flow into the mixing zone (Mz), and
Qw is flow from wellbore storage effects.
The in-well flow is computed from the convergence of incremental (layered) radial inflows (Qhl-n) summed to the total vertical flow (QV) entering the adjacent zone to the pump intake (called mixing zone [Mz]) as shown in figure 1. The Qv is transported as one-dimensional piston flow. Within the Mz, it's assumed that flow to the pump is dominated by horizontal radial flow (QH) when the pump is in the open interval of the well. Flow from the wellbore storage (Qw) is computed from the volume of water pumped from the well at the time of the drawdown (s) measurement(s). Aquifer storage effects are unaccounted for but are likely to be problematic when (1) dewatering within the well opening occurs or (2) when the water table is close to the top of the well screen or open interval where additional flow into the upper portion of the well opening may occur. For fully saturated wells tens of feet below the water table, storage effects are likely to be more uniformly distributed across the well screen or open interval (regardless of confined or unconfined conditions). Therefore, radial inflow from storage will be less prominent under pump rates commonly used in groundwater sampling either for volumetric sampling (<3 gallons per minute) or low-flow sampling (<0.5 liters per minute).
A major benefit of the use of the PAT is the understanding of time-varying, vertical integration of captured pump water. The analytical model computes aquifer (formation) capture intervals relative to the open interval of the well. This information is displayed graphically (called aquifer fraction graphs) and can be used to assess the likely formation intervals contributing water to the sample at any time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191104","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Harte, P.T., Huffman, B.J., Perina, T., Levine, H., and Rojas-Mickelson, D., 2019, Instructions for running the analytical code PAT (Purge Analyzer Tool) for computation of in-well time of travel of groundwater under pumping conditions: U.S. Geological Survey Open-File Report 2019–1104, 23 p., https://doi.org/10.3133/ofr20191104.","productDescription":"Report: vii, 23 p.; Application Site","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102617","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437282,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93EF0GM","text":"USGS data release","linkHelpText":"Purge Analyzer Tool - For computation of in-well time of travel of groundwater under pumping conditions"},{"id":368709,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1104/ofr20191104.pdf","text":"Report","size":"1.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1104"},{"id":368708,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/ptharte/pat","text":"USGS Official Source Code Archive","linkFileType":{"id":5,"text":"html"}},{"id":368706,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1104/coverthb_3.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
In 2016, the U.S. Bureau of Reclamation (USBR) and California Department of Water Resources requested a reinitiation of consultation under Section 7 of the Endangered Species Act on the coordinated long-term operations of the Central Valley and State Water Projects. This resulted in a Biological Assessment released by USBR in 2019. In its analysis of the Biological Assessment for its Biological Opinion on the proposed action, the National Marine Fisheries Service (NMFS) requested assistance from the U.S. Geological Survey to describe the effect of the proposed action on juvenile Chinook salmon (Oncorhynchus tshawytscha) populations migrating through the Sacramento-San Joaquin River Delta (henceforth called “the Delta”). Therefore, in this report we analyzed an 82-year time series of simulated river flows and Delta Cross Channel (DCC) gate operations under two scenarios constructed for the Biological Assessment: the proposed-action (PA) scenario and the continuing-operations scenario (COS).
To evaluate the proposed action, we used the STARS model (Survival, Travel time, And Routing Simulation model), a stochastic, individual-based simulation model designed to predict survival of a cohort of fish that experiences variable daily river flows as the fish migrate through the Delta. The STARS model uses parameter estimates from a Bayesian mark-recapture model that jointly estimates travel time and survival in eight discrete reaches of the Delta and migration routing at two key river junctions.
By applying the STARS model to the two 82-year scenarios, we found that the proposed action had negative effects on survival, travel time, and routing in October–December but positive effects in April–June. In October–December, there was a high probability that survival in the PA scenario was less than that in the COS, and that travel time and routing to the Interior Delta for the PA scenario was greater than that for the COS. The magnitude of the difference in survival between scenarios was larger in some years than in others. For example, we quantified that survival under the PA scenario was 10 percent lower than under the COS in 25 percent of the water years from October through December. During this period, inflow to the Delta tended to be lower under the PA scenario, and the DCC gate was open more frequently under the PA scenario than during the COS. Lower inflow reduces survival, and more frequent operation of the DCC gate 1) increases the proportion of fish entering the Interior Delta, where survival is low, and thus 2) reduces survival in the Sacramento River in reaches downstream of the DCC. In contrast, during the period April–June, survival was higher, travel times were lower, and routing to the Interior Delta was lower under the PA scenario relative to the COS, although the magnitude of the increase in survival was relatively small in most years (less than a 3-percent difference in survival). This difference between scenarios was driven by higher river flows in some years under the PA scenario relative to the COS. Overall, the differences in survival, travel time, and routing distance between the two operational scenarios were primarily driven by the timing and magnitude of the annual high river flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191125","collaboration":"Prepared in cooperation with National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Perry, R.W., Pope, A.C., and Sridharan, V.K., 2019, Using the STARS model to evaluate the effects of the proposed action for the reinitiation of consultation on the coordinated long-term operation of the Central Valley and State Water Project: U.S. Geological Survey Open-File Report 2019–1125, 31 p. plus appendixes, https://doi.org/10.3133/ofr20191125.","productDescription":"Report: vii, 31 p.; Appendixes 1–4","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108833","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":369157,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix3.pdf","text":"Appendix 3","size":"1.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 3","linkHelpText":"– Simulated Daily Routing by Year, Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"},{"id":369158,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix4.pdf","text":"Appendix 4","size":"1.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 4","linkHelpText":"– Simulated Proportion of Fish Entering the Interior Delta by Year Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"},{"id":369153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1125/coverthb.jpg"},{"id":369154,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125.pdf","text":"Report","size":"3.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125"},{"id":369155,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix1.pdf","text":"Appendix 1","size":"1.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 1","linkHelpText":"– Simulated Daily Survival by Year, Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"},{"id":369156,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix2.pdf","text":"Appendix 2","size":"1.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 2","linkHelpText":"– Simulated Daily Travel Time by Year, Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.684326171875,\n 37.56199695314352\n ],\n [\n -119.59716796875,\n 37.56199695314352\n ],\n [\n -119.59716796875,\n 39.41922073655956\n ],\n [\n -122.684326171875,\n 39.41922073655956\n ],\n [\n -122.684326171875,\n 37.56199695314352\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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6505 NE 65th Street
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Conservation of at‐risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two‐step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently developed statistical PVAs differ because forecasts are made conditional on models fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 yr of data on the federally threatened Chiricahua leopard frog (Lithobates chiricahuensis) to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50‐yr time horizon indicated that metapopulation extinction risk was <4% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 3.9% (95% CI 0–37%) by year 2066. Restoring six ponds by increasing their hydroperiod reduced extinction risk to <1% and greatly reduced uncertainty (95% CI 0–2%). Our results suggest that managers can mitigate the impacts of drought and environmental stochasticity on metapopulation viability by maintaining ponds that hold water throughout the year and keeping them free of invasive predators. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.
","language":"English","publisher":"Wiley","doi":"10.1029/2019WR024922","usgsCitation":"Read, J.S., Jia, X., Willard, J., Appling, A.P., Zwart, J.A., Oliver, S.K., Karpatne, A., Hansen, G., Hanson, P.C., Watkins, W., Steinbach, M., and Kumar, V., 2019, Process-guided deep learning predictions of lake water temperature: Water Resources Research, v. 55, no. 11, p. 9173-9190, https://doi.org/10.1029/2019WR024922.","productDescription":"28 p.","startPage":"9173","endPage":"9190","ipdsId":"IP-104941","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":459230,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019wr024922","text":"External Repository"},{"id":377267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","geographicExtents":"{\n \"type\": 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The FEQ Model is a computer program for the simulation of one-dimensional, unsteady flow in open channels and through control structures using the full, dynamic equations of motion. The input to FEQ Model includes the output from the FEQUTL Model, which computes tables relating the hydraulic properties of channel cross sections and control structures to depth, flow, and (or) other specified parameters. FEQ–GDI can be used to help users quickly detect anomalies in the data that may indicate errors in the input files.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191113","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Ern, J.L., Ortel, T., Ishii, A.L., and Bera, M., 2019, Full Equations Model Graphical Data Inspector (FEQ–GDI) user guide: U.S. Geological Survey Open-File Report 2019–1113, 11 p., https://doi.org/10.3133/ofr20191113.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-111050","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369032,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1113/coverthb.jpg"},{"id":369033,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1113/ofr20191113.pdf","text":"Report","size":"4.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1113"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Deposits of calcite coating the lower passages of Wind Cave in the southern Black Hills of South Dakota were precipitated under phreatic conditions. Data from samples associated with a new cave survey and hydrologic studies indicate that past water tables within Wind Cave reached a maximum height of 45 m above modern levels but were mostly confined to 25 m or less. Uranium-series ages for basal layers deposited on weathered wall rock indicate subaerial conditions in this part of the cave persisted between 1000 and 300 ka. Ages and elevations of wall coatings and cave rafts establish a 300,000 yr paleohydrograph indicating that water-table highstands occurred during interglacial or interstadial-to-early glacial periods and lowstands occurred during full-glacial and stadial episodes.
Isotopes of Sr, U, C, and O from dated calcite samples were obtained to evaluate potential shifts in paleo-groundwater composition. For comparison, Sr and U isotopic compositions were determined for modern groundwater from 18 sites previously classified into five hydrogeologic domains. Isotope data for different domains tend to cluster in separate fields, although several fields overlap. Compositions of Calcite Lake (informal name) water reflect modern recharge to shallow aquifers. In contrast, speleothem data indicate that paleo-groundwater highstands were not supported by increased infiltration associated with local recharge, or by upwelling from deeper Proterozoic sources. Instead, cave water was similar to deeper, warmer groundwater from the Madison Aquifer discharging at modern artesian springs flanking the southern Black Hills. Highstands were likely influenced by large-scale hydraulic processes associated with recharge to the Madison Aquifer under the Laurentide ice sheet on the northeast side of the Williston Basin, causing increased hydrostatic pressures in confined aquifers on the south side of the basin.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35312.1","usgsCitation":"Paces, J.B., Palmer, M.V., Palmer, A.N., Long, A.J., and Emmons, M.P., 2019, 300,000 yr history of water-table fluctuations at Wind Cave, South Dakota, USA—Scale, timing, and groundwater mixing in the Madison Aquifer: GSA Bulletin, v. 132, no. 7-8, p. 1447-1468, https://doi.org/10.1130/B35312.1.","productDescription":"22 p.","startPage":"1447","endPage":"1468","ipdsId":"IP-102435","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":385560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","city":"Rapid City, Hot Springs","otherGeospatial":"southern Black Hills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.029541015625,\n 42.98857645832184\n ],\n [\n -103.095703125,\n 42.98857645832184\n ],\n [\n -103.095703125,\n 44.33956524809713\n ],\n [\n -104.029541015625,\n 44.33956524809713\n ],\n [\n -104.029541015625,\n 42.98857645832184\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2019-11-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":815425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmer, Margaret V.","contributorId":257970,"corporation":false,"usgs":false,"family":"Palmer","given":"Margaret","email":"","middleInitial":"V.","affiliations":[{"id":52191,"text":"State University of New York, Oneonta","active":true,"usgs":false}],"preferred":false,"id":815426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmer, Arthur N. 0000-0002-2770-0053","orcid":"https://orcid.org/0000-0002-2770-0053","contributorId":257971,"corporation":false,"usgs":false,"family":"Palmer","given":"Arthur","email":"","middleInitial":"N.","affiliations":[{"id":52191,"text":"State University of New York, Oneonta","active":true,"usgs":false}],"preferred":false,"id":815427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emmons, Matthew P. 0000-0002-3429-396X memmons@usgs.gov","orcid":"https://orcid.org/0000-0002-3429-396X","contributorId":5023,"corporation":false,"usgs":true,"family":"Emmons","given":"Matthew","email":"memmons@usgs.gov","middleInitial":"P.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206244,"text":"sir20195124 - 2019 - Updates to the Madison Lake (Minnesota) CE–QUAL–W2 water-quality model for assessing algal community dynamics","interactions":[],"lastModifiedDate":"2019-12-05T09:47:00","indexId":"sir20195124","displayToPublicDate":"2019-11-06T10:02:47","publicationYear":"2019","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":"2019-5124","displayTitle":"Updates to the Madison Lake (Minnesota) CE–QUAL–W2 Water-Quality Model for Assessing Algal Community Dynamics","title":"Updates to the Madison Lake (Minnesota) CE–QUAL–W2 water-quality model for assessing algal community dynamics","docAbstract":"A previously developed CE–QUAL–W2 model for Madison Lake, Minnesota, simulated the algal community dynamics, water quality, and fish habitat suitability of Madison Lake under recent (2014) meteorological conditions. Additionally, this previously developed model simulated the complex interplay between external nutrient loading, internal nutrient loading from sediment release of phosphorus, and the organic matter decomposition of the algal biomass. However, the partitioning of Cyanophyta within the modeling framework was simplified to one group and did not account for how different Cyanophyta populations are affected by light conditions, use of nitrogen, temperature growth ranges, and differences in settling rates. Properly capturing Cyanophyta dynamics is important given the potential risks posed by potential large algal blooms. For example, when Cyanophyta form large blooms, recreational activities can become restricted in certain areas because of thick algal scums or algal mats, in addition to the possible production of a class of toxins, known as cyanotoxins, capable of threatening human health, domestic animals, and wildlife. Therefore, we updated the model to partition the Cyanophyta into a group that fixed nitrogen and a second, more buoyant Cyanophyta group that did not independently fix nitrogen.
The U.S. Geological Survey, in cooperation with the St. Croix Watershed Research Station (Science Museum of Minnesota) with support from the Environmental and Natural Resources Trust Fund of Minnesota (Legislative-Citizen Commission on Minnesota Resources), updated the Madison Lake CE–QUAL–W2 model to address the shortcomings of simulating Cyanophyta in the previously developed model and better characterize Cyanophyta into two groups. In addition to updating the Cyanophyta group differentiation, the part of the model that handles the simulation of algal community dynamics was updated while preserving model predictive capabilities for nutrients, water temperature, and dissolved oxygen. The calibration and validation of the model was done under recent meteorological conditions with large and persistent Cyanophyta blooms (2014 and 2016).
Overall, the model simulations predicted the persistently large total phosphorus concentrations in the hypolimnion of Madison Lake and key differences in nutrient concentrations between 2014 and 2016. The Cyanophyta bloom persistence throughout the summer was also simulated by the model in 2014 and 2016, a critical goal of the model update. Finally, monthly total phosphorus budgets were calculated for the updated Madison Lake model for 2014 and 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195124","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and St. Croix Watershed Research Station—Science Museum of Minnesota","usgsCitation":"Smith, E.A., and Kiesling, R.L., 2019, Updates to the Madison Lake (Minnesota) CE–QUAL–W2 water-quality model for assessing algal community dynamics: U.S. Geological Survey Scientific Investigations Report 2019–5124, 35 p., https://doi.org/10.3133/sir20195124.","productDescription":"Report: viii, 35 p.; Data Release","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-109825","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":368957,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5124/coverthb.jpg"},{"id":368958,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5124/sir20195124.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5124"},{"id":368959,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YEVPO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Updated CE–QUAL–W2 water-quality model for Madison Lake, Minnesota (2014 and 2016)"}],"country":"United States","state":"Minnesota","county":"Blue Earth County","otherGeospatial":"Madison Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.82830619812012,\n 44.17063113749022\n ],\n [\n -93.77620697021484,\n 44.17063113749022\n ],\n [\n -93.77620697021484,\n 44.20368152239254\n ],\n [\n -93.82830619812012,\n 44.20368152239254\n ],\n [\n -93.82830619812012,\n 44.17063113749022\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112