Streams and adjacent terrestrial ecosystems are characterized by permeable boundaries that are crossed by resource subsidies. Although the importance of these subsidies for riverine ecosystems is increasingly recognized, little is known about how they may be influenced by global environmental change. Drawing from available evidence, in this review we propose a conceptual framework to evaluate the effects of global change on the quality and spatiotemporal dynamics of stream–terrestrial subsidies. We illustrate how changes to hydrological and temperature regimes, atmospheric CO2 concentration, land use and the distribution of nonindigenous species can influence subsidy fluxes by affecting the biology and ecology of donor and recipient systems and the physical characteristics of stream–riparian boundaries. Climate-driven changes in the physiology and phenology of organisms with complex life cycles will influence their development time, body size and emergence patterns, with consequences for adjacent terrestrial consumers. Also, novel species interactions can modify subsidy dynamics via complex bottom-up and top-down effects. Given the seasonality and pulsed nature of subsidies, alterations of the temporal and spatial synchrony of resource availability to consumers across ecosystems are likely to result in ecological mismatches that can scale up from individual responses, to communities, to ecosystems. Similarly, altered hydrology, temperature, CO2 concentration and land use will modify the recruitment and quality of riparian vegetation, the timing of leaf abscission and the establishment of invasive riparian species. Along with morphological changes to stream–terrestrial boundaries, these will alter the use and fluxes of allochthonous subsidies associated with stream ecosystems. Future research should aim to understand how subsidy dynamics will be affected by key drivers of global change, including agricultural intensification, increasing water use and biotic homogenization. Our conceptual framework based on the match–mismatch between donor and recipient organisms may facilitate understanding of the multiple effects of global change and aid in the development of future research questions.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13182","usgsCitation":"Larsen, S., Muehlbauer, J.D., and Marti Roca, M.E., 2016, Resource subsidies between stream and terrestrial ecosystems under global change: Global Change Biology, v. 22, no. 7, p. 2489-2504, https://doi.org/10.1111/gcb.13182.","productDescription":"16 p.","startPage":"2489","endPage":"2504","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067749","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":320998,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-25","publicationStatus":"PW","scienceBaseUri":"572c609ce4b09acee752ef96","contributors":{"authors":[{"text":"Larsen, Stefano","contributorId":169188,"corporation":false,"usgs":false,"family":"Larsen","given":"Stefano","email":"","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":628833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muehlbauer, Jeffrey D. 0000-0003-1808-580X jmuehlbauer@usgs.gov","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":5045,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","email":"jmuehlbauer@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marti Roca, Maria Eugenia","contributorId":169189,"corporation":false,"usgs":false,"family":"Marti Roca","given":"Maria","email":"","middleInitial":"Eugenia","affiliations":[{"id":25434,"text":"Centre d'Estudis Avancats de Blanes","active":true,"usgs":false}],"preferred":false,"id":628834,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170799,"text":"70170799 - 2016 - Contamination with bacterial zoonotic pathogen genes in U.S. streams influenced by varying types of animal agriculture","interactions":[],"lastModifiedDate":"2018-09-12T17:04:25","indexId":"70170799","displayToPublicDate":"2016-05-05T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Contamination with bacterial zoonotic pathogen genes in U.S. streams influenced by varying types of animal agriculture","docAbstract":"Animal waste, stream water, and streambed sediment from 19 small (< 32 km2) watersheds in 12 U.S. states having either no major animal agriculture (control, n = 4), or predominantly beef (n = 4), dairy (n = 3), swine (n = 5), or poultry (n = 3) were tested for: 1) cholesterol, coprostanol, estrone, and fecal indicator bacteria (FIB) concentrations, and 2) shiga-toxin producing and enterotoxigenic Escherichia coli, Salmonella, Campylobacter, and pathogenic and vancomycin-resistant enterococci by polymerase chain reaction (PCR) on enrichments, and/or direct quantitative PCR. Pathogen genes were most frequently detected in dairy wastes, followed by beef, swine and poultry wastes in that order; there was only one detection of an animal-source-specific pathogen gene (stx1) in any water or sediment sample in any control watershed. Post-rainfall pathogen gene numbers in stream water were significantly correlated with FIB, cholesterol and coprostanol concentrations, and were most highly correlated in dairy watershed samples collected from 3 different states. Although collected across multiple states and ecoregions, animal-waste gene profiles were distinctive via discriminant analysis. Stream water gene profiles could also be discriminated by the watershed animal type. Although pathogen genes were not abundant in stream water or streambed samples, PCR on enrichments indicated that many genes were from viable organisms, including several (shiga-toxin producing or enterotoxigenic E. coli, Salmonella, vancomycin-resistant enterococci) that could potentially affect either human or animal health. Pathogen gene numbers and types in stream water samples were influenced most by animal type, by local factors such as whether animals had stream access, and by the amount of local rainfall, and not by studied watershed soil or physical characteristics. Our results indicated that stream water in small agricultural U.S. watersheds was susceptible to pathogen gene inputs under typical agricultural practices and environmental conditions. Pathogen gene profiles may offer the potential to address both source of, and risks associated with, fecal pollution.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.04.087","usgsCitation":"Haack, S.K., Duris, J., Kolpin, D.W., Focazio, M.J., Meyer, M.T., Johnson, H., Oster, R.J., and Foreman, W., 2016, Contamination with bacterial zoonotic pathogen genes in U.S. streams influenced by varying types of animal agriculture: Science of the Total Environment, v. 563-564, p. 340-350, https://doi.org/10.1016/j.scitotenv.2016.04.087.","productDescription":"11 p.","startPage":"340","endPage":"350","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059123","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":320993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Indiana, Iowa, Kentucky, Maryland, Michigan, New York, North Carolina, Ohio, Pennsylvania, 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Center","active":true,"usgs":true}],"preferred":true,"id":628469,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Foreman, William T. wforeman@usgs.gov","contributorId":1473,"corporation":false,"usgs":true,"family":"Foreman","given":"William T.","email":"wforeman@usgs.gov","affiliations":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"preferred":false,"id":628470,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169399,"text":"ofr20161054 - 2016 - Evaluation of the Storm 3 data logger manufactured by WaterLOG/Xylem Incorporated—Results of bench, temperature, and field deployment testing","interactions":[],"lastModifiedDate":"2016-05-04T15:49:10","indexId":"ofr20161054","displayToPublicDate":"2016-05-04T15:15:00","publicationYear":"2016","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":"2016-1054","title":"Evaluation of the Storm 3 data logger manufactured by WaterLOG/Xylem Incorporated—Results of bench, temperature, and field deployment testing","docAbstract":"The Storm 3 is a browser-based data logger manufactured by WaterLOG/Xylem Incorporated that operates over a temperature range of −40 to 60 degrees Celsius (°C). A Storm logger with no built-in telemetry (Storm3-00) and a logger with built-in cellular modem (Storm3-03) were evaluated by the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) for conformance to the manufacturer’s specifications with bench tests, for recording data over the device’s operating temperature range with temperature chamber tests, and for field performance with an outdoor deployment test.
\nThe procedures followed and the results obtained from the testing are described in this publication. The device met most of the manufacturer’s stated specifications. An exception was power consumption, which was about 10 percent above the manufacturer’s specifications. It was also observed that enabling WiFi doubles the Storm 3’s power consumption. In addition, several logging errors were made by two units during deployment testing, but it could not be determined whether these errors were the fault of the Storm or of an attached sensor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161054","usgsCitation":"Kunkle, G.A., 2016, Evaluation of the Storm 3 data logger manufactured by Waterlog/Xylem Incorporated—Results of Bench, Temperature, and Field Deployment Testing: U.S. Geological Survey Open-File Report 2016–1054, 9 p., https://dx.doi.org/10.3133/ofr20161054.","productDescription":"iii, 9 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069059","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":320970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1054/ofr20161054.pdf","text":"Report","size":"373 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1054"},{"id":320969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1054/coverthb.jpg","description":"OFR 2016-1054"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
http://water.usgs.gov/hif/
The one-dimensional hydrodynamic ice model, DYRESM-WQ-I, was modified to simulate ice cover and thermal structure of dimictic Lake Mendota, Wisconsin, USA, over a continuous 104-year period (1911–2014). The model results were then used to examine the drivers of changes in ice cover and water temperature, focusing on the responses to shifts in air temperature, wind speed, and water clarity at multiyear timescales. Observations of the drivers include a change in the trend of warming air temperatures from 0.081 °C per decade before 1981 to 0.334 °C per decade thereafter, as well as a shift in mean wind speed from 4.44 m s−1 before 1994 to 3.74 m s−1 thereafter. Observations show that Lake Mendota has experienced significant changes in ice cover: later ice-on date(9.0 days later per century), earlier ice-off date (12.3 days per century), decreasing ice cover duration (21.3 days per century), while model simulations indicate a change in maximum ice thickness (12.7 cm decrease per century). Model simulations also show changes in the lake thermal regime of earlier stratification onset (12.3 days per century), later fall turnover (14.6 days per century), longer stratification duration (26.8 days per century), and decreasing summer hypolimnetic temperatures (−1.4 °C per century). Correlation analysis of lake variables and driving variables revealed ice cover variables, stratification onset, epilimnetic temperature, and hypolimnetic temperature were most closely correlated with air temperature, whereas freeze-over water temperature, hypolimnetic heating, and fall turnover date were more closely correlated with wind speed. Each lake variable (i.e., ice-on and ice-off dates, ice cover duration, maximum ice thickness, freeze-over water temperature, stratification onset, fall turnover date, stratification duration, epilimnion temperature, hypolimnion temperature, and hypolimnetic heating) was averaged for the three periods (1911–1980, 1981–1993, and 1994–2014) delineated by abrupt changes in air temperature and wind speed. Average summer hypolimnetic temperature and fall turnover date exhibit significant differences between the third period and the first two periods. Changes in ice cover (ice-on and ice-off dates, ice cover duration, and maximum ice thickness) exhibit an abrupt change after 1994, which was related in part to the warm El Niño winter of 1997–1998. Under-ice water temperature, freeze-over water temperature, hypolimnetic temperature, fall turnover date, and stratification duration demonstrate a significant difference in the third period (1994–2014), when air temperature was warmest and wind speeds decreased rather abruptly. The trends in ice cover and water temperature demonstrate responses to both long-term and abrupt changes in meteorological conditions that can be complemented with numerical modeling to better understand how these variables will respond in a future climate.
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To examine how hummocks support trees in mesohaline sites that are beyond physiological limits of the trees, we used salinity and stable isotopes (2H and 18O) of water as tracers to understand water fluxes in hummocks and uptake by baldcypress (Taxodium distichum (L.) Rich.), which is the most abundant tree species in coastal freshwater forests of the southeastern U.S. Hummocks were always partially submerged and were completely submerged 1 to 8% of the time during the two studied growing seasons, in association with high water in the estuary. Salinity, δ18O, and δ2H varied more in the shallow open water than in groundwater. Surface water and shallow groundwater were similar to throughfall in isotopic composition, which suggested dominance by rainfall. Salinity of groundwater in hummocks increased with depth, was higher than in swales, and fluctuated little over time. Isotopic composition of xylem water in baldcypress was similar to the vadose zone and unlike other measured sources, indicating that trees preferentially use unsaturated hummock tops as refugia from higher salinity and saturated soil in swales and the lower portions of hummocks. Sustained upward gradients of salinity from groundwater to surface water and vadose water, and low variation in groundwater salinity and isotopic composition, suggested long residence time, limited exchange with surface water, and that the shallow subsurface of hummocks is characterized by episodic salinization and slow dilution.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1738","usgsCitation":"Hsueh, Y., Chambers, J., Krauss, K.W., Allen, S.T., and Keim, R., 2016, Hydrologic exchanges and baldcypress water use on deltaic hummocks, Louisiana, USA: Ecohydrology, v. 9, no. 8, p. 1452-1463, https://doi.org/10.1002/eco.1738.","productDescription":"12 p.","startPage":"1452","endPage":"1463","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067366","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":320812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.14,\n 29.75\n ],\n [\n -90.14,\n 29.76\n ],\n [\n -90.15,\n 29.76\n ],\n [\n -90.15,\n 29.75\n ],\n [\n -90.14,\n 29.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-28","publicationStatus":"PW","scienceBaseUri":"57286c1be4b0b13d3917ce0e","contributors":{"authors":[{"text":"Hsueh, Yu-Hsin","contributorId":169051,"corporation":false,"usgs":false,"family":"Hsueh","given":"Yu-Hsin","email":"","affiliations":[],"preferred":false,"id":628310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chambers, Jim L.","contributorId":16498,"corporation":false,"usgs":true,"family":"Chambers","given":"Jim L.","affiliations":[],"preferred":false,"id":628311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":628298,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Scott T.","contributorId":168409,"corporation":false,"usgs":false,"family":"Allen","given":"Scott","email":"","middleInitial":"T.","affiliations":[{"id":25282,"text":"School of Renewable Natural Resources, Louisiana State University, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":628312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keim, Richard F.","contributorId":21858,"corporation":false,"usgs":true,"family":"Keim","given":"Richard F.","affiliations":[],"preferred":false,"id":628313,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170957,"text":"70170957 - 2016 - Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","interactions":[],"lastModifiedDate":"2018-08-09T12:12:53","indexId":"70170957","displayToPublicDate":"2016-04-30T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","docAbstract":"Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.
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2016 - Urban hydrology—Science capabilities of the U.S. Geological Survey","interactions":[],"lastModifiedDate":"2016-04-29T13:57:03","indexId":"fs20163023","displayToPublicDate":"2016-04-29T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3023","title":"Urban hydrology—Science capabilities of the U.S. Geological Survey","docAbstract":"Urbanization affects streamflow characteristics, coastal flooding, and groundwater recharge. Increasing impervious areas, streamflow diversions, and groundwater pumpage are some of the ways that the natural water cycle is affected by urbanization. Assessment of the relations among these factors and changes in land use helps water-resource managers with issues such as stormwater management and vulnerability to flood and drought. Scientists with the U.S. Geological Survey (USGS) have the expertise to monitor and model urban hydrologic systems. Streamflow and groundwater data are available in national databases, and analyses of these data, including identification of long-term streamflow trends and the efficacy of management practices, are published in USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163023","collaboration":"Northeast Region Urban Landscape Capabilities Team ","usgsCitation":"U.S. Geological Survey, 2016, Urban hydrology—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3023, 2 p., https://dx.doi.org/10.3133/fs20163023.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074174","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":320426,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025","description":"FS 2016-3023"},{"id":320425,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024","description":"FS 2016-3023"},{"id":320423,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3023/coverthb.jpg"},{"id":320427,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026","description":"FS 2016-3023"},{"id":320424,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3023/fs20163023.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3023"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Urban development creates multiple stressors that can degrade stream ecosystems by changing stream hydrology, water quality, and physical habitat. Contaminants, habitat destruction, and increasing streamflow variability resulting from urban development have been associated with the disruption of biological communities, particularly the loss of sensitive aquatic biota. Understanding how algal, invertebrate, and fish communities respond to these physical and chemical stressors can provide important clues as to how streams should be managed to protect stream ecosystems as a watershed becomes increasingly urbanized. The U.S. Geological Survey continues to lead monitoring efforts and scientific studies on the effects of urban development on stream ecosystems in metropolitan areas across the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163026","collaboration":"Northeast Region Urban Landscape Capabilities Team","usgsCitation":"U.S. Geological Survey, 2016, Urban development and stream ecosystem health—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3026, 2 p., https://dx.doi.org/10.3133/fs20163026.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074179","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":320444,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3026/fs20163026.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3026"},{"id":320445,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163023","text":"Fact Sheet 2016-3023"},{"id":320443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3026/coverthb.jpg"},{"id":320446,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024"},{"id":320447,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Successful restoration of wetland ecosystems requires knowledge of wetland hydrologic patterns and an understanding of how those patterns affect wetland plant and animal populations.Within the Everglades, Florida, USA restoration, an applied science strategy including conceptual ecological models linking drivers to indicators is being used to organize current scientific understanding to support restoration efforts. A key driver of the ecosystem affecting the distribution and abundance of organisms is the timing, distribution, and volume of water flows that result in water depth patterns across the landscape. American alligators (Alligator mississippiensis) are one of the ecological indicators being used to assess Everglades restoration because they are a keystone species and integrate biological impacts of hydrological operations through all life stages. Alligator body condition (the relative fatness of an animal) is one of the metrics being used and targets have been set to allow us to track progress. We examined trends in alligator body condition using Fulton’s K over a 15 year period (2000–2014) at seven different wetland areas within the Everglades ecosystem, assessed patterns and trends relative to restoration targets, and related those trends to hydrologic variables. We developed a series of 17 a priori hypotheses that we tested with an information theoretic approach to identify which hydrologic factors affect alligator body condition. Alligator body condition was highest throughout the Everglades during the early 2000s and is approximately 5–10% lower now (2014). Values have varied by year, area, and hydrology. Body condition was positively correlated with range in water depth and fall water depth. Our top model was the “Current” model and included variables that describe current year hydrology (spring depth, fall depth, hydroperiod, range, interaction of range and fall depth, interaction of range and hydroperiod). Across all models, interaction between range and fall water depth was the most important variable (relative weight of 1.0) followed by spring and fall water depths (0.99), range (0.96), hydroperiod (0.95) and interaction between range and hydroperiod (0.95). Our work provides additional evidence that restoring a greater range in annual water depths is important for improvement of alligator body condition and ecosystem function. This information can be incorporated into both planning and operations to assist in reaching Everglades restoration goals.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2016.03.003","usgsCitation":"Brandt, L., Beauchamp, J.S., Jeffery, B.M., Cherkiss, M.S., and Mazzotti, F., 2016, Fluctuating water depths affect American alligator (Alligator mississippiensis) body condition in the Everglades, Florida, USA: Ecological Indicators, v. 67, p. 441-450, https://doi.org/10.1016/j.ecolind.2016.03.003.","productDescription":"10 p.","startPage":"441","endPage":"450","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069792","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":471046,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2016.03.003","text":"Publisher Index Page"},{"id":320585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.4473876953125,\n 26.676913083105454\n ],\n [\n -80.36773681640625,\n 26.684275490019488\n ],\n [\n -80.23590087890624,\n 26.571333057252076\n ],\n [\n -80.23590087890624,\n 26.382027976025352\n ],\n [\n -80.29083251953124,\n 26.347575438494673\n ],\n [\n -80.29632568359375,\n 26.185018250078308\n ],\n [\n -80.35400390625,\n 26.118452068488097\n ],\n [\n -80.44189453125,\n 26.145576207592274\n ],\n [\n -80.43365478515625,\n 25.962983554822678\n ],\n [\n -80.48858642578125,\n 25.760319754713887\n ],\n [\n -80.57373046875,\n 25.46559428893416\n ],\n [\n -80.93353271484375,\n 25.40854689267053\n ],\n [\n -81.10382080078125,\n 25.631621577258493\n ],\n [\n -80.8428955078125,\n 25.767740402713383\n ],\n [\n -80.83740234375,\n 26.165298896316042\n ],\n [\n -80.82366943359375,\n 26.330345320410842\n ],\n [\n -80.54901123046875,\n 26.406630642556795\n ],\n [\n -80.452880859375,\n 26.458279167084125\n ],\n [\n -80.4473876953125,\n 26.676913083105454\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5721d4a3e4b0b13d39129145","contributors":{"authors":[{"text":"Brandt, Laura A.","contributorId":18608,"corporation":false,"usgs":false,"family":"Brandt","given":"Laura A.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":627656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beauchamp, Jeffrey S.","contributorId":138880,"corporation":false,"usgs":false,"family":"Beauchamp","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[{"id":12559,"text":"University of Florida, FLEC","active":true,"usgs":false}],"preferred":false,"id":627657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeffery, Brian M.","contributorId":138879,"corporation":false,"usgs":false,"family":"Jeffery","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":12558,"text":"University of Florida, Gainesville","active":true,"usgs":false}],"preferred":false,"id":627658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cherkiss, Michael S. 0000-0002-7802-6791 mcherkiss@usgs.gov","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":4571,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","email":"mcherkiss@usgs.gov","middleInitial":"S.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":627655,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":627659,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70171018,"text":"70171018 - 2016 - Numerical experiments to explain multiscale hydrological responses to mountain pine beetle tree mortality in a headwater watershed","interactions":[],"lastModifiedDate":"2016-05-19T10:54:17","indexId":"70171018","displayToPublicDate":"2016-04-26T12:00:00","publicationYear":"2016","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":"Numerical experiments to explain multiscale hydrological responses to mountain pine beetle tree mortality in a headwater watershed","docAbstract":"The effects of mountain pine beetle (MPB)-induced tree mortality on a headwater hydrologic system were investigated using an integrated physical modeling framework with a high-resolution computational grid. Simulations of MPB-affected and unaffected conditions, each with identical atmospheric forcing for a normal water year, were compared at multiple scales to evaluate the effects of scale on MPB-affected hydrologic systems. Individual locations within the larger model were shown to maintain hillslope-scale processes affecting snowpack dynamics, total evapotranspiration, and soil moisture that are comparable to several field-based studies and previous modeling work. Hillslope-scale analyses also highlight the influence of compensating changes in evapotranspiration and snow processes. Reduced transpiration in the Grey Phase of MPB-induced tree mortality was offset by increased late-summer evaporation, while overall snowpack dynamics were more dependent on elevation effects than MPB-induced tree mortality. At the watershed scale, unaffected areas obscured the magnitude of MPB effects. Annual water yield from the watershed increased during Grey Phase simulations by 11 percent; a difference that would be difficult to diagnose with long-term gage observations that are complicated by inter-annual climate variability. The effects on hydrology observed and simulated at the hillslope scale can be further damped at the watershed scale, which spans more life zones and a broader range of landscape properties. These scaling effects may change under extreme conditions, e.g., increased total MPB-affected area or a water year with above average snowpack.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1002/2015WR018300","usgsCitation":"Penn, C.A., Bearup, L.A., Maxwell, R.M., and Clow, D.W., 2016, Numerical experiments to explain multiscale hydrological responses to mountain pine beetle tree mortality in a headwater watershed: Water Resources Research, v. 52, no. 4, p. 3143-3161, https://doi.org/10.1002/2015WR018300.","productDescription":"19 p.","startPage":"3143","endPage":"3161","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070327","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":471047,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr018300","text":"Publisher Index Page"},{"id":321299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-26","publicationStatus":"PW","scienceBaseUri":"573ee3d2e4b04a3a6a24ad3d","contributors":{"authors":[{"text":"Penn, Colin A. 0000-0002-5195-2744 cpenn@usgs.gov","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":5336,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"cpenn@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":629555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bearup, Lindsay A.","contributorId":139257,"corporation":false,"usgs":false,"family":"Bearup","given":"Lindsay","email":"","middleInitial":"A.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":629556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maxwell, Reed M.","contributorId":95373,"corporation":false,"usgs":true,"family":"Maxwell","given":"Reed","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":629557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":629558,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70175518,"text":"70175518 - 2016 - Surficial geological tools in fluvial geomorphology: Chapter 2","interactions":[],"lastModifiedDate":"2017-06-16T12:15:08","indexId":"70175518","displayToPublicDate":"2016-04-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Surficial geological tools in fluvial geomorphology: Chapter 2","docAbstract":"Increasingly, environmental scientists are being asked to develop an understanding of how rivers and streams have been altered by environmental stresses, whether rivers are subject to physical or chemical hazards, how they can be restored, and how they will respond to future environmental change. These questions present substantive challenges to the discipline of fluvial geomorphology, especially since decades of geomorphologic research have demonstrated the general complexity of fluvial systems. It follows from the concept of complex response that synoptic and short-term historical views of rivers will often give misleading understanding of future behavior. Nevertheless, broadly trained geomorphologists can address questions involving complex natural systems by drawing from a tool box that commonly includes the principles and methods of geology, hydrology, hydraulics, engineering, and ecology.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Tools in fluvial geomorphology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781118648551.ch2","usgsCitation":"Jacobson, R.B., O'Connor, J., and Oguchi, T., 2016, Surficial geological tools in fluvial geomorphology: Chapter 2, chap. of Tools in fluvial geomorphology, p. 24-57, https://doi.org/10.1002/9781118648551.ch2.","productDescription":"34 p.","startPage":"24","endPage":"57","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-078967","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":328109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-22","publicationStatus":"PW","scienceBaseUri":"57c7ffbee4b0f2f0cebfc337","contributors":{"authors":[{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":645549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Connor, James E. oconnor@usgs.gov","contributorId":138998,"corporation":false,"usgs":true,"family":"O'Connor","given":"James E.","email":"oconnor@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":645550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oguchi, Takashi","contributorId":173700,"corporation":false,"usgs":false,"family":"Oguchi","given":"Takashi","email":"","affiliations":[{"id":7267,"text":"University of Tokyo","active":true,"usgs":false}],"preferred":false,"id":645551,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169904,"text":"sir20165041 - 2016 - A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon","interactions":[],"lastModifiedDate":"2016-04-20T12:53:23","indexId":"sir20165041","displayToPublicDate":"2016-04-19T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5041","title":"A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon","docAbstract":"This report describes a method for estimating ecologically relevant low-flow metrics that quantify late‑season streamflow regime for ungaged sites in the upper Grande Ronde River Basin, Oregon. The analysis presented here focuses on sites sampled by the Columbia River Inter‑Tribal Fish Commission as part of their efforts to monitor habitat restoration to benefit spring Chinook salmon recovery in the basin. Streamflow data were provided by the U.S. Geological Survey and the Oregon Water Resources Department. Specific guidance was provided for selection of streamgages, development of probabilistic frequency distributions for annual 7-day low-flow events, and regionalization of the frequency curves based on multivariate analysis of watershed characteristics. Evaluation of the uncertainty associated with the various components of this protocol indicates that the results are reliable for the intended purpose of hydrologic classification to support ecological analysis of factors contributing to juvenile salmon success. They should not be considered suitable for more standard water-resource evaluations that require greater precision, especially those focused on management and forecasting of extreme low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20165041","collaboration":"Prepared in cooperation with Columbia River Inter-Tribal Fish Commission","usgsCitation":"Kelly, V.J., and White, Seth, 2016, A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5041, 41 p.,\nhttps://dx.doi.org/10.3133/sir20165041.","productDescription":"Report: vi, 41 p.; Appendixes A-F","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059110","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":320198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5041 Report PDF"},{"id":320199,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041_appendixes.xlsx","text":"Appendixes A-F","size":"75 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5041 Appendixes"},{"id":320197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5041/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Grande Ronde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 44\n ],\n [\n -115,\n 47\n ],\n [\n -119,\n 47\n ],\n [\n -119,\n 44\n ],\n [\n -115,\n 44\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
The Rio Rico and Nogales (Arizona) 1:24,000-scale quadrangles are located in the Basin and Range Province of southern Arizona, and the southern edge of the map is the international border with Sonora, Mexico. The major urban area is Nogales, a bi-national city known as “the gateway to Mexico.” Rocks exposed in the map area range in age from Jurassic through Quaternary. Major physiographic, geologic, and hydrologic features in the map area include the southern San Cayetano Mountains, Grosvenor Hills, and Sonoita Creek in the northern part, and Mount Benedict and the Mount Benedict horst block in the southcentral part. The horst block is bounded by the Santa Cruz River on the east and Nogales Wash on the west.
\nThe objectives of our mapping were to define the geologic framework for the Nogales area and the upper Santa Cruz basin to support ongoing multidisciplinary projects. This new work will improve understanding of the Nogales Formation to more fully assess its groundwater resource potential. We significantly revised the Miocene Nogales Formation based on geologic mapping combined with new geochronologic, geophysical, and petrographic studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3354","usgsCitation":"Page, W.R., Menges, C.M., Gray, Floyd, Berry, M.E., Bultman, M.W., Cosca, M.A., and VanSistine, D.P., 2016, Geologic map of the Rio Rico and Nogales 7.5’ quadrangles, Santa Cruz County, Arizona: U.S. Geological Survey Scientific Investigations Map 3354, 32 p. pamphlet, 2 sheets, scale 1:24,000, https://dx.doi.org/10.3133/sim3354.","productDescription":"Pamphlet: v, 32 p.; 3 Sheets: 37.93 x 40.26 inches or smaller; Appendix; Metadata: text, xml; Read Me; Spatial Data: Base maps, Geodatabase, Shapefiles.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059210","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":320004,"rank":8,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_shapefiles.zip","text":"Shapefiles","size":"1.94 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3354 Shapefiles"},{"id":320000,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_sheet2.pdf","text":"Cross sections","size":"184 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Cross sections"},{"id":320006,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3354/sim3354.met","text":"Metadata text","size":"24.0 kB","description":"SIM 3354 Metadata Text"},{"id":319994,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_sheet1.pdf","text":"Geologic map","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Geologic Map"},{"id":320007,"rank":10,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_basemaps.zip","text":"Base maps","size":"18.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3354 Base maps"},{"id":319992,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_pamphlet.pdf","text":"Pamphlet","size":"21.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Pamphlet"},{"id":319993,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3354 ReadMe"},{"id":320009,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_appendix.xlsx","text":"Combined 40Ar/39Ar data","size":"68.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIM 3354 Combined 40Ar/39Ar data"},{"id":320002,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_gdb.zip","text":"Geodatabase","size":"39.5 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3354 Geodatabase"},{"id":320052,"rank":12,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_metadata.xml","text":"Metadata xml","size":"20.0 kB","description":"SIM 3354 Metadata xml"},{"id":319996,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_sheet1_geo.pdf","text":"Georeferenced geologic map","size":"146.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Georeferenced geologic map"},{"id":319989,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3354/coverthb.jpg"}],"country":"United States","state":"Arizona","county":"Santa Cruz County","otherGeospatial":"Rio Rico and Nogales 7.5' Quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 31.5\n ],\n [\n -111,\n 31.3325\n ],\n [\n -110.875,\n 31.3329\n ],\n [\n -110.875,\n 31.5\n ],\n [\n -111,\n 31.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
Iodinated disinfection byproducts (DBPs) are among the most toxic DBPs, but they are not typically measured in treated water. Iodinated DBPs can be toxic to humans, and they also have the potential to affect aquatic communities. Because of the specific use of iodine and iodine-containing compounds in dairies, such livestock operations can be a potential source of iodinated DBPs in corresponding receiving water bodies. DBPs [trihalomethanes (THMs), including iodinated THMs] were measured within dairy processing facilities (milking and cheese manufacturing) and surface waters that receive dairy-impacted effluents [either directly from the dairy or through wastewater treatment plants (WWTPs)] in three areas of the United States (California, New York, and Wisconsin). Iodo-THMs comprised 15−29% of the total THMs in surface water near WWTP effluents that were impacted by dairy waste and 0−100% of the total THMs in samples from dairy processing facilities.
","language":"English","publisher":"American Chemical Society","publisherLocation":"Washington, DC","doi":"10.1021/acs.estlett.6b00109","usgsCitation":"Hladik, M., Hubbard, L.E., Kolpin, D.W., and Focazio, M.J., 2016, Dairy-impacted wastewater is a source of iodinated disinfection byproducts in the environment: Environmental Science & Technology, v. 3, no. 5, p. 190-193, https://doi.org/10.1021/acs.estlett.6b00109.","productDescription":"4 p.","startPage":"190","endPage":"193","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073754","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":320062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, New York, 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Jamison et al. (2010 & Jamison, 2015) have reviewed the current state of knowledge about the hydrology, physical characteristics, food supply, ecology and biodiversity of life in hadal trenches. This review concluded that, there appears to be a high level of endemism based on the few specimens collected from historical sampling efforts in the 1950s (Danish Galathea and Soviet Vitjaz expeditions), but because trenches are still largely unexplored there is a lot we do not know about the ecological structure and functioning of hadal environments. However, relatively recent advances in technology using remotely operated vehicles (ROV) and landers can help us explore hadal trenches in greater detail.
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The source of loess in the Matanuska Valley has been debated for more than seven decades, with the Knik River and the Matanuska River, both to the east, being the leading candidates and the Susitna River, to the west, as a less favorable source. We report here new stratigraphic, mineralogic, and geochemical data that test the competing hypotheses of these river sources. Loess thickness data are consistent with previous studies that show that a source or sources lay to the east, which rules out the Susitna River as a source. Knik and Matanuska River silts can be distinguished using Sc–Th–La, LaN/YbN vs. Eu/Eu∗, Cr/Sc, and As/Sb. Matanuska Valley loess falls clearly within the range of values for these ratios found in Matanuska River silt. Dust storms from the Matanuska River are most common in autumn, when river discharge is at a minimum and silt-rich point bars are exposed, wind speed from the north is beginning to increase after a low-velocity period in summer, snow depth is still minimal, and soil temperatures are still above freezing. Thus, seasonal changes in climate and hydrology emerge as critical factors in the timing of aeolian silt transport in southern Alaska. These findings could be applicable to understanding seasonal controls on Pleistocene loess accretion in Europe, New Zealand, South America, and elsewhere in North America.
","language":"English","publisher":"Elsevier Science","doi":"10.1016/j.aeolia.2016.02.005","usgsCitation":"Muhs, D., Budahn, J.R., Skipp, G.L., and McGeehin, J., 2016, Geochemical evidence for seasonal controls on the transportation of Holocene loess, Matanuska Valley, southern Alaska, USA: Aeolian Research, v. 21, p. 61-73, https://doi.org/10.1016/j.aeolia.2016.02.005.","productDescription":"13 p.","startPage":"61","endPage":"73","ipdsId":"IP-068983","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2016.02.005","text":"Publisher Index Page"},{"id":320018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"21","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"570f5f9ce4b0ef3b7ca32956","chorus":{"doi":"10.1016/j.aeolia.2016.02.005","url":"http://dx.doi.org/10.1016/j.aeolia.2016.02.005","publisher":"Elsevier BV","authors":"Muhs Daniel R., Budahn James R., Skipp Gary L., McGeehin John P.","journalName":"Aeolian Research","publicationDate":"6/2016"},"contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":626544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budahn, James R. 0000-0001-9794-8882 jbudahn@usgs.gov","orcid":"https://orcid.org/0000-0001-9794-8882","contributorId":1175,"corporation":false,"usgs":true,"family":"Budahn","given":"James","email":"jbudahn@usgs.gov","middleInitial":"R.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":626545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skipp, Gary L. 0000-0002-9404-0980 gskipp@usgs.gov","orcid":"https://orcid.org/0000-0002-9404-0980","contributorId":2102,"corporation":false,"usgs":true,"family":"Skipp","given":"Gary","email":"gskipp@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":626546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGeehin, John mcgeehin@usgs.gov","contributorId":167455,"corporation":false,"usgs":true,"family":"McGeehin","given":"John","email":"mcgeehin@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":626547,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70177031,"text":"70177031 - 2016 - Estimating evapotranspiration and groundwater flow from water-table fluctuations for a general wetland scenario","interactions":[],"lastModifiedDate":"2016-10-17T16:04:56","indexId":"70177031","displayToPublicDate":"2016-04-12T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating evapotranspiration and groundwater flow from water-table fluctuations for a general wetland scenario","docAbstract":"The use of diurnal water-table fluctuation methods to calculate evapotranspiration (ET) and groundwater flow is of increasing interest in ecohydrological studies. Most studies of this type, however, have been located in riparian wetlands of semi-arid regions where groundwater levels are consistently below topographic surface elevations and precipitation events are infrequent. Current methodologies preclude application to a wider variety of wetland systems. In this study, we extended a method for estimating sub-daily ET and groundwater flow rates from water-level fluctuations to fit highly dynamic, non-riparian wetland scenarios. Modifications included (1) varying the specific yield to account for periodic flooded conditions and (2) relating empirically derived ET to estimated potential ET for days when precipitation events masked the diurnal signal. To demonstrate the utility of this method, we estimated ET and groundwater fluxes over two growing seasons (2006–2007) in 15 wetlands within a ridge-and-swale wetland complex of the Laurentian Great Lakes under flooded and non-flooded conditions. Mean daily ET rates for the sites ranged from 4.0 mm d−1 to 6.6 mm d−1. Shallow groundwater discharge rates resulting from evaporative demand ranged from 2.5 mm d−1 to 4.3 mm d−1. This study helps to expand our understanding of the evapotranspirative demand of plants under various hydrologic and climate conditions. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.","language":"English","publisher":"John Wiley & Sons","publisherLocation":"Hoboken, NJ","doi":"10.1002/eco.1356","usgsCitation":"Weber, L.C., Wiley, M.J., and Wilcox, D., 2016, Estimating evapotranspiration and groundwater flow from water-table fluctuations for a general wetland scenario: Ecohydrology, v. 7, no. 2, p. 378-390, https://doi.org/10.1002/eco.1356.","productDescription":"13 p.","startPage":"378","endPage":"390","numberOfPages":"13","ipdsId":"IP-018006","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471080,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2027.42/106891","text":"External Repository"},{"id":329644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":329620,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1002/eco.1356/abstract"}],"volume":"7","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2013-01-07","publicationStatus":"PW","scienceBaseUri":"5805e34fe4b0824b2d1c24c2","contributors":{"authors":[{"text":"Weber, Lisa C.","contributorId":124586,"corporation":false,"usgs":true,"family":"Weber","given":"Lisa","email":"","middleInitial":"C.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":651055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiley, Michael J.","contributorId":139111,"corporation":false,"usgs":false,"family":"Wiley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6649,"text":"University of Michigan, School of Natural Resources and Environment","active":true,"usgs":false}],"preferred":false,"id":651057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilcox, Douglas 0000-0002-2871-4131","orcid":"https://orcid.org/0000-0002-2871-4131","contributorId":175418,"corporation":false,"usgs":false,"family":"Wilcox","given":"Douglas","email":"","affiliations":[{"id":27569,"text":"SUNY – College at Brockport","active":true,"usgs":false}],"preferred":false,"id":651056,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70157329,"text":"sir20155137 - 2016 - Hydraulic model and flood-inundation maps developed for the Pee Dee National Wildlife Refuge, North Carolina","interactions":[],"lastModifiedDate":"2017-01-18T13:22:46","indexId":"sir20155137","displayToPublicDate":"2016-04-08T10:45:00","publicationYear":"2016","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":"2015-5137","title":"Hydraulic model and flood-inundation maps developed for the Pee Dee National Wildlife Refuge, North Carolina","docAbstract":"A one-dimensional step-backwater model was developed by the U.S. Geological Survey (USGS) in cooperation with the U.S. Fish and Wildlife Service, Pee Dee National Wildlife Refuge, North Carolina, to provide a means for predicting flood-plain inundation. The model was developed for selected reaches of the Pee Dee River, Brown Creek, and Rocky River, using the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System (HEC-RAS) software. Multiple cross sections were defined on each modeled stream, and hydrologic data were collected between August 2011 and August 2013 at selected locations on the Pee Dee River and on its tributaries Brown Creek, Rocky River, and Thoroughfare Creek. Cross-section, stage, and flow data were used to develop the model and simulate water-surface profiles at 1.0-foot increments at the USGS streamgage Pee Dee River at Pee Dee Refuge near Ansonville, N.C. The profiles were produced for 31 selected water levels that ranged from approximately 193.0 feet to 223.0 feet in elevation at the Pee Dee River at Pee Dee Refuge streamgage.
\nA series of digital flood-inundation maps were developed on the basis of the water-surface profiles produced by the model. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent and depth of flooding corresponding to selected water levels at the USGS streamgage Pee Dee River at Pee Dee Refuge near Ansonville, N.C. These maps, when combined with real-time water-level information from USGS streamgages, provide managers with critical information to help plan flood-response activities and resource protection efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155137","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Smith, D.G., and Wagner, C.R., 2016, Hydraulic model and flood-inundation maps developed for the Pee Dee National Wildlife Refuge, North Carolina: U.S. Geological Survey Scientific Investigations Report 2015–5137, 14 p., https://dx.doi.org/10.3133/sir20155137.","productDescription":"Document: vi, 14 p.; Metadata: 3 downloadable files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069288","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":319758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137.pdf","size":"1.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5137"},{"id":319789,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137_peedee.xml","size":"15.6 KB xml","description":"SIR 2015-5137"},{"id":319787,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137_peedee-grids.zip","size":"302 MB grids","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5137"},{"id":319788,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5137/sir20155137_peedee-shapefiles.zip","size":"18.8 MB","linkFileType":{"id":4,"text":"shapefile"},"description":"SIR 2015-5137"},{"id":319757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5137/coverthb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Pee Dee National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.266667,\n 35.233333\n ],\n [\n -80.266667,\n 35\n ],\n [\n -79.85,\n 35\n ],\n [\n -79.85,\n 35.233333\n ],\n [\n -80.266667,\n 35.233333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
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Columbia, SC 29210
http://www.usgs.gov/water/southatlantic/
The contamination of major continental river systems by endocrine-active chemicals (EACs) derived from the discharge of wastewater treatment plant (WWTP) effluents can affect human and ecosystem health. As part of a long-term effort to develop a native fish model organism for assessment of endocrine disruption in Australia's largest watershed, the Murray-Darling River Basin, the present study evaluated endocrine disruption in adult males of the native Australian Murray rainbowfish (Melanotaenia fluviatilis) exposed to effluent from an activated sludge WWTP and water from the Murray River during a 28-d, continuous-flow, on-site experiment. Analysis of the WWTP effluent and river water detected estrone and 17β-estradiol at concentrations up to approximately 25 ng L−1. Anti-estrogenicity of effluent samples was detected in vitro using yeast-based bioassays (yeast estrogen screen) throughout the experiment, but estrogenicity was limited to the first week of the experiment. Histological evaluation of the testes indicated significant suppression of spermatogenesis by WWTP effluent after 28 d of exposure. Plasma vitellogenin concentrations and expression of vitellogenin messenger RNA in liver were not significantly affected by exposure to WWTP effluent. The combination of low contaminant concentrations in the WWTP effluent, limited endocrine disrupting effects in the Murray rainbowfish, and high in-stream dilution factors (>99%) suggest minimal endocrine disruption impacts on native Australian fish in the Murray River downstream from the WWTP outfall.
","language":"English","publisher":"Elsevier Science","doi":"10.1002/etc.2895","usgsCitation":"Vajda, A.M., Kumar, A., Woods, M., Williams, M., Doan, H., Tolsher, P., Kookana, R.S., and Barber, L.B., 2016, Integrated assessment of wastewater treatment plant effluent estrogenicity in the Upper Murray River, Australia, using the native Murray rainbowfish (Melanotaenia fluviatilis): Environmental Toxicology and Chemistry, v. 34, no. 5, p. 1078-1087, https://doi.org/10.1002/etc.2895.","productDescription":"10 p.","startPage":"1078","endPage":"1087","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062599","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":320526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","otherGeospatial":"Upper Murray 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Australia","active":true,"usgs":false}],"preferred":false,"id":627316,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tolsher, Peter","contributorId":168797,"corporation":false,"usgs":false,"family":"Tolsher","given":"Peter","email":"","affiliations":[{"id":25362,"text":"EGL Management Services, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":627317,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kookana, Rai S.","contributorId":168798,"corporation":false,"usgs":false,"family":"Kookana","given":"Rai","email":"","middleInitial":"S.","affiliations":[{"id":25361,"text":"CSIRO Land and Water, Adelaide, South Australia","active":true,"usgs":false}],"preferred":false,"id":627318,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barber, Larry B. 0000-0002-0561-0831 lbbarber@usgs.gov","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":921,"corporation":false,"usgs":true,"family":"Barber","given":"Larry","email":"lbbarber@usgs.gov","middleInitial":"B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":627311,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169134,"text":"fs20163014 - 2016 - Hydrologic monitoring for Chicago’s Sustainable Streetscapes Program","interactions":[],"lastModifiedDate":"2016-06-24T09:00:51","indexId":"fs20163014","displayToPublicDate":"2016-04-05T13:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3014","title":"Hydrologic monitoring for Chicago’s Sustainable Streetscapes Program","docAbstract":"The Chicago Department of Transportation’s Sustainable Streetscapes Program is an innovative program that strives to convert Chicago’s neighborhood commercial areas, riverwalks, and bicycle facilities into active, attractive places for Chicagoans to live, work, and play. The objective of each project is to create flourishing public places while improving the ability of infrastructure to support dense urban living. The U.S. Geological Survey (USGS), in cooperation with the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC), and the Chicago Department of Transportation (CDOT), is monitoring the pre- and postconstruction hydrologic characteristics of an urban corridor on the south side of Chicago that is being renovated using sustainable streetscapes technology.
The CDOT Sustainable Streetscapes Program utilizes urban stormwater best-management practices (BMPs) to reduce the storm runoff to the local combined sewer system. The urban stormwater BMPs include permeable pavement, bioswales, infiltration basins, and planters. The urban stormwater BMPs are designed to capture the first flush of storm runoff through features that enhance the infiltration of stormwater runoff to shallow groundwater.
The hydrology of the Sustainable Streetscapes Program area is being monitored to evaluate the impacts and effectiveness of the urban stormwater BMP’s. Continuous monitoring of rainfall, sewer flows, stormwater runoff, soil moisture, and groundwater levels will give engineers and scientists measured data to define baseline pre- and postconstruction conditions for the evaluation of the BMPs.
Three tipping-bucket rain gages are located along the project corridor. The data provide information on the intensity and volume of rainfall. Rainfall can be highly variable even over a small area like the project corridor.
Continuous recording meters are located at specific locations in the combined sewers to record water level and flow during both dry weather (mostly sanitary flow) and wet weather conditions (stormwater runoff in addition to the sanitary flow). Sanitary flow is the largest source of flow in the combined sewers during dry weather, and stormwater runoff and sanitary flow combine during wet weather. The sewer flow data allow engineers and scientists to calculate total runoff volume for selected storm events.
Wells are located within the project corridor to record water levels and help determine the direction of movement of groundwater in response to rainfall and snowmelt. In urban settings with aging sewer systems, groundwater can seep into the sewers or combined sewage can seep from the sewers into the local groundwater system. The groundwater data are also important in evaluating the overall impacts of increased infiltration resulting from BMPs.
Data from wells show the relative water levels of shallow groundwater, water levels in the combined sewer system, and nearby surface-water channels within the project corridor. In some aging urban sewer systems, the local combined sewer system lies below the water table and receives substantial amounts of groundwater inflow, which can significantly reduce the amount of additional water the sewer system can accept.
The bioswale along the south side of West Cermak Road near South Throop Street functions to infiltrate stormwater runoff from the road. Stormwater on the road surface initially drains to the curb and then flows along the curb until it reaches a curb cut-out. Materials within the bioswale allow stormwater to infiltrate and reduce the load to the combined sewer.
A common feature in urban areas are curbside catch basins that collect stormwater runoff from paved streets. Stormwater drains first to the curb and then flows along the curb to the catch basin. Lateral sewer pipe connects the catch basin to the combined sewer beneath the street. The use of permeable pavers along the curbs in the project study reach let stormwater infiltrate before it reaches the curb, thus reducing the amount of stormwater draining to the combined sewers.
Water-level data from catch basins in the project study area show the effects of permeable pavers in reducing the stormwater drainage to the combined sewers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163014","collaboration":"Prepared in cooperation with the Metropolitan Water Reclamation District of Greater Chicago (MWRDGC), and the Chicago Department of Transportation (CDOT)","usgsCitation":"Duncker, J.J., and Morrow, W.S., 2016, Hydrologic monitoring for Chicago’s Sustainable Streetscapes Program. U.S. Geological Survey Fact Sheet 2016–3014, 6 p., https://dx.doi.org/10.3133/fs20163014.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054040","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":319331,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3014/coverthb.jpg"},{"id":319332,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3014/fs20163014.pdf","text":"Report","size":"3.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3014"}],"country":"United States","state":"Illinois","city":"Chicago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.68531799316406,\n 41.8384904853889\n ],\n [\n -87.68531799316406,\n 41.86035637336661\n ],\n [\n -87.64274597167967,\n 41.86035637336661\n ],\n [\n -87.64274597167967,\n 41.8384904853889\n ],\n [\n -87.68531799316406,\n 41.8384904853889\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave
Urbana, IL 61801
http://il.water.usgs.gov/
The climatic conditions of the specific time period during which peak-flow data were collected at a given streamflow-gaging station (hereinafter referred to as gaging station) can substantially affect how well the peak-flow frequency (hereinafter referred to as frequency) results represent long-term hydrologic conditions. Differences in the timing of the periods of record can result in substantial inconsistencies in frequency estimates for hydrologically similar gaging stations. Potential for inconsistency increases with decreasing peak-flow record length. The representativeness of the frequency estimates for a short-term gaging station can be adjusted by various methods including weighting the at-site results in association with frequency estimates from regional regression equations (RREs) by using the Weighted Independent Estimates (WIE) program. Also, for gaging stations that cannot be adjusted by using the WIE program because of regulation or drainage areas too large for application of RREs, frequency estimates might be improved by using record extension procedures, including a mixed-station analysis using the maintenance of variance type I (MOVE.1) procedure. The U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources and Conservation, completed a study to provide adjusted frequency estimates for selected gaging stations through water year 2011.
The purpose of Chapter D of this Scientific Investigations Report is to present adjusted frequency estimates for 504 selected streamflow-gaging stations in or near Montana based on data through water year 2011. Estimates of peak-flow magnitudes for the 66.7-, 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities are reported. These annual exceedance probabilities correspond to the 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
The at-site frequency estimates were adjusted by weighting with frequency estimates from RREs using the WIE program for 438 selected gaging stations in Montana. These 438 selected gaging stations (1) had periods of record less than or equal to 40 years, (2) represented unregulated or minor regulation conditions, and (3) had drainage areas less than about 2,750 square miles.
The weighted-average frequency estimates obtained by weighting with RREs generally are considered to provide improved frequency estimates. In some cases, there are substantial differences among the at-site frequency estimates, the regression-equation frequency estimates, and the weighted-average frequency estimates. In these cases, thoughtful consideration should be applied when selecting the appropriate frequency estimate. Some factors that might be considered when selecting the appropriate frequency estimate include (1) whether the specific gaging station has peak-flow characteristics that distinguish it from most other gaging stations used in developing the RREs for the hydrologic region; and (2) the length of the peak-flow record and the general climatic characteristics during the period when the peak-flow data were collected. For critical structure-design applications, a conservative approach would be to select the higher of the at-site frequency estimate and the weighted-average frequency estimate.
The mixed-station MOVE.1 procedure generally was applied in cases where three or more gaging stations were located on the same large river and some of the gaging stations could not be adjusted using the weighted-average method because of regulation or drainage areas too large for application of RREs. The mixed-station MOVE.1 procedure was applied to 66 selected gaging stations on 19 large rivers.
The general approach for using mixed-station record extension procedures to adjust at-site frequencies involved (1) determining appropriate base periods for the gaging stations on the large rivers, (2) synthesizing peak-flow data for the gaging stations with incomplete peak-flow records during the base periods by using the mixed-station MOVE.1 procedure, and (3) conducting frequency analysis on the combined recorded and synthesized peak-flow data for each gaging station. Frequency estimates for the combined recorded and synthesized datasets for 66 gaging stations with incomplete peak-flow records during the base periods are presented. The uncertainties in the mixed-station record extension results are difficult to directly quantify; thus, it is important to understand the intended use of the estimated frequencies based on analysis of the combined recorded and synthesized datasets. The estimated frequencies are considered general estimates of frequency relations among gaging stations on the same stream channel that might be expected if the gaging stations had been gaged during the same long-term base period. However, because the mixed-station record extension procedures involve secondary statistical analysis with accompanying errors, the uncertainty of the frequency estimates is larger than would be obtained by collecting systematic records for the same number of years in the base period.
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\"}}]}","edition":"Version 1.0: April 6, 2016; Version 1.1: February 26, 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The U.S. Geological Survey (USGS), in cooperation with the Montana Department of Natural Resources and Conservation, completed a study to update methods for estimating peak-flow frequencies at ungaged sites in Montana based on peak-flow data at streamflow-gaging stations through water year 2011. The methods allow estimation of peak-flow frequencies (that is, peak-flow magnitudes, in cubic feet per second, associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites. The annual exceedance probabilities correspond to 1.5-, 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Regional regression analysis is a primary focus of Chapter F of this Scientific Investigations Report, and regression equations for estimating peak-flow frequencies at ungaged sites in eight hydrologic regions in Montana are presented. The regression equations are based on analysis of peak-flow frequencies and basin characteristics at 537 streamflow-gaging stations in or near Montana and were developed using generalized least squares regression or weighted least squares regression.
All of the data used in calculating basin characteristics that were included as explanatory variables in the regression equations were developed for and are available through the USGS StreamStats application (http://water.usgs.gov/osw/streamstats/) for Montana. StreamStats is a Web-based geographic information system application that was created by the USGS to provide users with access to an assortment of analytical tools that are useful for water-resource planning and management. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report chapter can be conveniently solved using the Montana StreamStats application.
Selected results from this study were compared with results of previous studies. For most hydrologic regions, the regression equations reported for this study had lower mean standard errors of prediction (in percent) than the previously reported regression equations for Montana. The equations presented for this study are considered to be an improvement on the previously reported equations primarily because this study (1) included 13 more years of peak-flow data; (2) included 35 more streamflow-gaging stations than previous studies; (3) used a detailed geographic information system (GIS)-based definition of the regulation status of streamflow-gaging stations, which allowed better determination of the unregulated peak-flow records that are appropriate for use in the regional regression analysis; (4) included advancements in GIS and remote-sensing technologies, which allowed more convenient calculation of basin characteristics and investigation of many more candidate basin characteristics; and (5) included advancements in computational and analytical methods, which allowed more thorough and consistent data analysis.
This report chapter also presents other methods for estimating peak-flow frequencies at ungaged sites. Two methods for estimating peak-flow frequencies at ungaged sites located on the same streams as streamflow-gaging stations are described. Additionally, envelope curves relating maximum recorded annual peak flows to contributing drainage area for each of the eight hydrologic regions in Montana are presented and compared to a national envelope curve. In addition to providing general information on characteristics of large peak flows, the regional envelope curves can be used to assess the reasonableness of peak-flow frequency estimates determined using the regression equations.
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\"}}]}","edition":"Version 1.1: February 2018","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
The U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, developed regional regression equations based on basin and streamflow characteristics for streamflow-gaging stations through water year 2009 that can be used to estimate streamflow characteristics for ungaged sites in western Montana. The regression equations allow estimation of low-flow frequencies; mean annual and mean monthly streamflows; and the 20-, 50-, and 80-percent durations for annual and monthly duration streamflows for ungaged sites in western Montana that are unaffected by regulation.
\nRegression equations were developed for 54 streamflow characteristics in each of the 4 hydrologic regions generally located in western Montana. Exploratory data analyses determined that of the 40 basin characteristics used as explanatory variables, only 3 were significant in the final equations. These three basin characteristics are contributing drainage area, mean annual precipitation of the drainage basin, and percentage of basin with slopes greater than 50 percent. Low-flow frequency data and daily mean streamflow data from 152 streamflow-gaging stations were used to develop the regression equations. These 152 streamflow-gaging stations were considered to be unregulated (or only the unregulated period of record for regulated streamflow-gaging stations were used). For this study, a streamflow-gaging station is considered to be unregulated if cumulative drainage area upstream from all upstream dams is less than 20 percent of the drainage basin for the streamflow-gaging station, and if no large diversion canals are upstream from the streamflow-gaging station.
\nIn addition to the regional regression equations, two methods for estimating streamflow characteristics based on drainage areas of a nearby streamflow-gaging station are provided. Also, spreadsheet tools are provided for calculating streamflow characteristics based on regression equations or the drainage-area ratio method.
\nAll of the data used to calculate basin characteristics were derived from publicly available data sources and are available through the U.S. Geological Survey Streamstats program (http://water.usgs.gov/osw/streamstats/) for Montana. The primary purpose of the Montana StreamStats application is to provide estimates of basin characteristics and streamflow characteristics for user-selected ungaged sites on Montana streams. The regional regression equations presented in this report have been loaded to the Montana StreamStats application and can be used to derive streamflow characteristics for ungaged sites.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019G","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality and Montana Department of Natural Resources and Conservation","usgsCitation":"McCarthy, P.M., Sando, Roy, Sando, S.K., and Dutton, D.M., 2016, Methods for estimating streamflow characteristics at ungaged sites in western Montana based on data through water year 2009: U.S. Geological Survey Scientific Investigations Report 2015–5019–G, 19 p., https://dx.doi.org/10.3133/sir20155019G.","productDescription":"Report: vii, 19 p.; 10 Tables","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-067085","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":319659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5019G/coverthb.jpg"},{"id":319661,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G_tables.xlsx","text":"Tables 1 through 4, 1–1 through 1–4, and 2–1 through 2–2","size":"483 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015–5019–G Tables"},{"id":319660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5019G/sir20155019G.pdf","text":"Report","size":"9.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5019–G"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.03759765625,\n 48.99463598353408\n ],\n [\n -116.08154296875001,\n 47.97521412341618\n ],\n [\n -115.6201171875,\n 47.39834920035926\n ],\n [\n -114.64233398437499,\n 46.66451741754235\n ],\n [\n -114.43359375,\n 46.52863469527167\n ],\n [\n -114.53247070312499,\n 46.14178273759234\n ],\n [\n -114.43359375,\n 45.87471224890479\n ],\n [\n -114.53247070312499,\n 45.79050946752472\n ],\n [\n -114.576416015625,\n 45.590978249451936\n ],\n [\n -114.334716796875,\n 45.4524242413431\n ],\n [\n -113.9501953125,\n 45.69850658738848\n ],\n [\n -113.7744140625,\n 45.57560020947799\n ],\n [\n -113.7744140625,\n 45.390735154248894\n ],\n [\n -113.433837890625,\n 45.0502402697946\n ],\n [\n -113.51074218749999,\n 44.933696389694674\n ],\n [\n -113.26904296874999,\n 44.78573392716592\n ],\n [\n -113.08227539062499,\n 44.66865287227321\n ],\n [\n -112.9833984375,\n 44.43377984606825\n ],\n [\n -112.8515625,\n 44.33956524809713\n ],\n [\n -112.796630859375,\n 44.457309801319305\n ],\n [\n -112.47802734375,\n 44.4808302785626\n ],\n [\n -112.379150390625,\n 44.457309801319305\n ],\n [\n -112.291259765625,\n 44.574817404670306\n ],\n [\n -111.8408203125,\n 44.55133484083592\n ],\n [\n -111.52221679687499,\n 44.535674532413196\n ],\n [\n -111.42333984375,\n 44.69989765840318\n ],\n [\n -111.346435546875,\n 44.72332018895825\n ],\n [\n -111.060791015625,\n 44.465151013519616\n ],\n [\n -111.060791015625,\n 44.99588261816546\n ],\n [\n -107.22656249999999,\n 45.02695045318546\n ],\n [\n -107.57812499999999,\n 45.99696161820381\n ],\n [\n -108.12744140625,\n 47.234489635299184\n ],\n [\n -111.42333984375,\n 47.2195681123155\n ],\n [\n -112.7197265625,\n 48.06339653776211\n ],\n [\n -113.35693359375,\n 49.009050809382046\n ],\n [\n -116.03759765625,\n 48.99463598353408\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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3162 Bozeman Ave
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A large-scale study by the U.S. Geological Survey, in cooperation with the Montana Department of Transportation and the Montana Department of Natural Resources and Conservation, was done to investigate general patterns in peak-flow temporal trends and stationarity through water year 2011 for 24 long-term streamflow-gaging stations (hereinafter referred to as gaging stations) in Montana. Hereinafter, all years refer to water years; a water year is the 12-month period from October 1 through September 30 and is designated by the year in which it ends. The primary focus of the study was to identify general patterns in peak-flow temporal trends and stationarity that are relevant to application of peak-flow frequency analyses within a statewide gaging-station network.
\nTemporal trends were analyzed for two hydrologic variables: annual peak flow and peak-flow timing. Annual peak flow is the maximum instantaneous discharge, in cubic feet per second, recorded each year a gaging station was operated. Peak-flow timing is the day of the annual peak flow (hereinafter referred to as day of peak), recorded each year a gaging station was operated.
\nStudy results provide evidence that annual peak flow for most of the long-term gaging stations can be reasonably considered as stationary for application of peak-flow frequency analyses within a statewide gaging station network. Upward trends in annual peak flow during 1930–76 generally were stronger than downward trends during 1967–2011 for most long-term gaging stations. Statistical distributions of annual peak flow generally were similar among three summary time periods (1930–78, 1979–2011, and the entire period of record). However, for two low-elevation gaging stations in eastern Montana (Poplar River at international boundary [gaging station 06178000] and Powder River at Moorhead, Montana [gaging station 06324500]), substantial downward trends in annual peak flow during 1967–2011 were of similar or stronger magnitude than the upward trends during 1930–76, and the annual-peak-flow medians for 1979–2011 were substantially lower than the medians for the entire period of record.
\nFor peak-flow timing for most long-term gaging stations, differences in trends between 1930–76 and 1967–2011 are variable and not particularly strong. Statistical distributions generally are similar among the summary time periods. However, for two high-elevation gaging stations on a Missouri River headwater tributary (Gallatin River near Gallatin Gateway, Montana [gaging station 06043500] and Gallatin River at Logan, Montana [gaging station 06052500]) and for five high-elevation gaging stations in the Yellowstone River Basin (Yellowstone River at Corwin Springs, Montana [gaging station 06191500], Yellowstone River near Livingston, Montana [gaging station 06192500], Clarks Fork Yellowstone River near Belfry, Montana [gaging station 06207500], Clarks Fork Yellowstone River at Edgar, Montana [gaging station 06208500], and Yellowstone River at Billings, Montana [gaging station 06214500]) downward trends in peak-flow timing during 1967–2011 generally were stronger than upward trends during 1930–1976, and day-of-peak medians for 1979–2011 were considerably less than medians for 1930–78. The downward trends in peak-flow timing for 1967–2011 indicate that the timing of annual peak flows changed from later in the year to earlier in the year. For the seven high-elevation gaging stations, the mean change during 1967–2011 was about 13 days (range of 8 to 22 days).
\nFor most of the high-elevation gaging stations in the Missouri River headwaters, Yellowstone River Basin, and Columbia River Basin, there was general correspondence between trend patterns for annual peak flow and trend patterns for peak-flow timing; that is, during periods when there were upward trends in annual peak flow, there generally also were upward trends in peak-flow timing. Conversely, during periods when there were downward trends in annual peak flow, there generally also were downward trends in peak-flow timing.
\nThe two low-elevation gaging stations in eastern Montana (Poplar River at international boundary [gaging station 06178000] and Powder River at Moorhead, Montana [gaging station 06324500]) had considerable changes in annual-peakflow characteristics after the mid-1970s, which might provide evidence of potential nonstationarity in the peak-flow records. The two low-elevation gaging stations that have potential nonstationarity are located in drainage basins that are strongly affected by agricultural activities that potentially affect the hydrologic regimes. Primary agricultural activities that might alter natural hydrologic conditions include construction of small impoundments (primarily for stock-watering purposes) and irrigation diversions. Temporal variability in these activities might contribute to the potential nonstationarity issues. Changes in climatic characteristics after the mid-1970s also possibly contribute to the potential nonstationarity issues. Lack of considerable indication of potential nonstationarity in annual peak flow for the other long-term gaging stations in this study might indicate that climatic changes have been more pronounced with respect to effects on peak flows in low elevation areas in eastern Montana than in areas represented by the other long-term gaging stations. Another possibility is that climatic changes after the mid-1970s are exacerbated in low-elevation areas where small-impoundment development and potential effects of irrigation diversions might be more extensive.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Montana StreamStats (Scientific Investigations Report 2015-5019)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155019B","collaboration":"Prepared in cooperation with the Montana Department of Transportation and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, S.K., McCarthy, P.M., Sando, Roy, and Dutton, D.M., 2016, Temporal trends and stationarity in annual peak flow and peak-flow timing for selected long-term streamflow-gaging stations in or near Montana through water year 2011: U.S. Geological Survey Scientific Investigations Report 2015–5019–B, 48 p., https://dx.doi.org/10.3133/sir20155019B.","productDescription":"vi, 48 p.","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-060698","costCenters":[{"id":5050,"text":"WY-MT Water Science 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Ave
Helena, MT 59601
Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.
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