Land use/land cover (LULC) change has significant impacts on nutrient loading to aquatic systems and has been linked to deteriorating water quality globally. While many relationships between LULC and nutrient loading have been identified, characterization of the interaction between LULC, climate (specifically variable hydrologic forcing) and solute export across seasonal and interannual time scales is needed to understand the processes that determine nutrient loading and responses to change. Recent advances in high-frequency water quality sensors provide opportunities to assess these interannual relationships with sufficiently high temporal resolution to capture the unpredictable, short-term storm events that likely drive important export mechanisms for dissolved organic carbon (DOC) and nitrate (NO3−–N). We deployed a network of in situ sensors in forested, agricultural, and urban watersheds across the northeastern United States. Using 2 years of high-frequency sensor data, we provide a regional assessment of how LULC and hydrologic variability affected the timing and magnitude of dissolved organic carbon and nitrate export, and the status of watershed fluxes as either supply or transport controlled. Analysis of annual export dynamics revealed systematic differences in the timing and magnitude of DOC and NO3−–N delivery among different LULC classes, with distinct regional similarities in the timing of DOC and NO3−–N fluxes from forested and urban watersheds. Conversely, export dynamics at agricultural sites appeared to be highly site-specific, likely driven by local agricultural practices and regulations. Furthermore, the magnitude of solute fluxes across watersheds responded strongly to interannual variability in rainfall, suggesting a high degree of hydrologic control over nutrient loading across the region. Thus, there is strong potential for climate-driven changes in regional hydrologic cycles to drive variation in the magnitude of downstream nutrient fluxes, particularly in watersheds where solute supply and/or transport has been modified.
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to improve biocrust colonization and establishment in dryland restoration","docAbstract":"Methods to reduce soil loss and associated loss of ecosystem functions due to land degradation are of particular importance in dryland ecosystems. Biocrusts are communities of cyanobacteria, lichens, and bryophytes that are vulnerable to soil disturbance, but provide vital ecosystem functions when present. Biocrusts stabilize soil, improve hydrologic function, and increase nutrient and carbon inputs. Methods to reestablish biocrust rapidly, when lost from ecosystems, have the potential to restore important dryland ecosystem functions and thereby increase probability of successful rehabilitation. The aim of this study was to identify habitat ameliorations to enhance the success of biocrust inoculation by: (1) reducing physiological stress on biocrusts and increasing resource availability (using shade, soil surface roughening, and watering), and (2) stabilizing mobile soils (using straw borders, three soil tackifiers [soil stabilizers], and a combination of shade, water, roughening, and tackifier). In the Great Basin Desert on the Utah Test and Training Range near Salt Lake City, we applied field‐harvested biocrust material to experimental plots on coarse‐ and fine‐textured soils with the top 2 cm of soil and biocrust removed. Habitat ameliorations were applied with and without biocrust addition. Shade provision increased biocrust cover 50% over controls. Biocrust cover and soil stability were 65% lower in straw border plots relative to controls. Soil tackifiers, alone and in combination with resource augmentation and stress reduction, did not improve cover and stabilization over inoculated controls. We found variability in recovery by time and between soil types. These results suggest plausible strategies to improve success of biocrust inoculation.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13052","usgsCitation":"Antoninka, A., Bowker, M.A., Barger, N., Belnap, J., Giraldo Silva, A., Reed, S., Garcia-Pichel, F., and Duniway, M.C., 2020, Addressing barriers to improve biocrust colonization and establishment in dryland restoration: Restoration Ecology, v. 28, no. 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This study examined the spatial distribution of septic systems, coastal surface water contamination, and related environmental factors of coastal Florida watersheds at the 8-digit hydrologic unit code level. Hydrology, in situ sampling data, and other ancillary data were combined in a geographic information system to examine spatial relationships. Spatial distribution data were correlated to nitrogen, Enterococci counts, and beach closures tabulated since 2000, 2007, and 2012. Significant positive correlations (α = 0.05) with nitrogen and Enterococci counts were consistent for percent agricultural cover, percent combined urban and agricultural cover, septic tank density, population density, and septic tank density in poorly drained soils. Beach closures since 2012 were significantly positively correlated (α = 0.05) to average impervious cover (IC) and percent urbanization. Statistics indicated that Enterococci counts, nitrogen, and beach closures may be related to specific environmental factors and septic tank densities. The combination of septic tanks in urban regions with high IC prone to elevated runoff could also be a factor in surface water contamination. Data availability was also highlighted as a limitation due to infrequent spatial and temporal sampling.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/02723646.2019.1671297","usgsCitation":"Flanagan, K., Dixon, B., Rivenbark, T., and Griffin, D.W., 2020, An integrative GIS approach to analyzing the impacts of septic systems on the coast of Florida, USA: Physical Geography, v. 41, no. 5, p. 407-432, https://doi.org/10.1080/02723646.2019.1671297.","productDescription":"26 p.","startPage":"407","endPage":"432","ipdsId":"IP-093655","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"41","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Flanagan, Kyle","contributorId":222648,"corporation":false,"usgs":false,"family":"Flanagan","given":"Kyle","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":782794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dixon, Barnali","contributorId":201960,"corporation":false,"usgs":false,"family":"Dixon","given":"Barnali","email":"","affiliations":[{"id":36308,"text":"USFSP","active":true,"usgs":false}],"preferred":false,"id":782795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rivenbark, Tess","contributorId":222649,"corporation":false,"usgs":false,"family":"Rivenbark","given":"Tess","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":782796,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":782793,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205866,"text":"70205866 - 2020 - Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss","interactions":[],"lastModifiedDate":"2020-02-06T10:54:20","indexId":"70205866","displayToPublicDate":"2019-09-18T17:02:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":789,"text":"Annals of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss","docAbstract":"Coastal wetlands have evolved to withstand stressful abiotic conditions through the maintenance of hydrologic feedbacks among vegetation production and flooding. However, disruption of these feedbacks can lead to ecosystem collapse, or a regime shift from vegetated wetland to open water. To prevent the loss of critical coastal wetland habitat, we must improve understanding of the abiotic-biotic linkages among flooding and wetland stability. The aim of this research was to identify characteristic landscape patterns and thresholds of wetland degradation that can be used to identify areas of vulnerability, reduce flooding threats, and improve habitat quality.
We measured local- and landscape-scale responses of coastal wetland vegetation to flooding stress in healthy and degrading coastal wetlands. We hypothesized that conversion of Spartina patens wetlands to open water could be defined by a distinct change in landscape configuration pattern, and that this change would occur at a discrete elevation threshold.
Despite similarities in total land and water cover, we observed differences in the landscape configuration of vegetated and open water pixels in healthy and degrading wetlands. Healthy wetlands were more aggregated, and degrading wetlands were more fragmented. Generally, greater aggregation was associated with higher wetland elevation and better drainage, compared to fragmented wetlands, which had lower elevation and poor drainage. The relationship between vegetation cover and elevation was non-linear, and the conversion from vegetated wetland to open water occurred beyond an elevation threshold of hydrologic stress.
The elevation threshold defined a transition zone where healthy, aggregated, wetland converted to a degrading, fragmented, wetland beyond an elevation threshold of 0.09 m NAVD88 (0.27 m MSL), and complete conversion to open water occurred beyond 0.03 m NAVD88 (0.21 m MSL). This work illustrates that changes in landscape configuration can be used as an indicator of wetland loss, with specific elevation thresholds to inform restoration and conservation planning to maximize wetland stability in anticipation of flooding threats.
","language":"English","publisher":"Oxford Academic Press","doi":"10.1093/aob/mcz144","usgsCitation":"Stagg, C., Osland, M., Moon, J.A., Hall, C., Feher, L., Jones, W.R., Couvillion, B., Hartley, S.B., and Vervaeke, W., 2020, Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss: Annals of Botany, v. 125, no. 2, p. 365-376, https://doi.org/10.1093/aob/mcz144.","productDescription":"12 p.","startPage":"365","endPage":"376","ipdsId":"IP-106464","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458646,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aob/mcz144","text":"Publisher Index Page"},{"id":437216,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SXJX2T","text":"USGS data release","linkHelpText":"Local and landscape-scale data describing patterns of coastal wetland loss in the Texas Chenier Plain, U.S.A."},{"id":437215,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7736Q51","text":"USGS data release","linkHelpText":"Land-water classification for selected sites in McFaddin NWR and J.D. Murphree WMA"},{"id":368133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Chenier Plain, Gulf of Mexico, McFaddin National Wildlife Refuge, J.D. Murphree Wildlife Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.42675781249999,\n 28.478348692223165\n ],\n [\n -91.219482421875,\n 28.478348692223165\n ],\n [\n -91.219482421875,\n 31.68143311662596\n ],\n [\n -97.42675781249999,\n 31.68143311662596\n ],\n [\n -97.42675781249999,\n 28.478348692223165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":206252,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":206246,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":772715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hall, Courtney 0000-0003-0990-5212","orcid":"https://orcid.org/0000-0003-0990-5212","contributorId":218912,"corporation":false,"usgs":true,"family":"Hall","given":"Courtney","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":214841,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, William R. 0000-0002-5493-4138 jonesb@usgs.gov","orcid":"https://orcid.org/0000-0002-5493-4138","contributorId":463,"corporation":false,"usgs":true,"family":"Jones","given":"William","email":"jonesb@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Couvillion, Brady 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":146832,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","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":772719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","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":772720,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vervaeke, William 0000-0002-1518-5197 vervaekew@usgs.gov","orcid":"https://orcid.org/0000-0002-1518-5197","contributorId":3265,"corporation":false,"usgs":true,"family":"Vervaeke","given":"William","email":"vervaekew@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772721,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205560,"text":"70205560 - 2020 - Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","interactions":[],"lastModifiedDate":"2020-01-20T12:22:35","indexId":"70205560","displayToPublicDate":"2019-09-09T10:19:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","docAbstract":"Streamwater quality can be affected by climate-related variability in hydrologic state, which controls flow paths and affects biogeochemical processes. Thirty-one years of input/output solute fluxes at Panola Mountain Research Watershed, a small, forested, seasonally water-limited watershed near Atlanta, Georgia, were used to quantify the effects of climatic-related variability in storage on streamwater solute concentrations and fluxes. Streamwater fluxes were estimated for ten solutes from weekly and event sample concentrations using regression-based methods. The most pertinent storage attribute (current or antecedent watershed, shallow, and deep storage) for each solute was determined by fitting separate concentration relationships. The concentration-discharge relationships varied more for reactive solutes such as potassium, sulfate, and DOC and less for weathering products (base cations and dissolved silica) and conservative chloride. Many solutes exhibited higher concentrations when storage levels were lower or wetting up, which was likely the result of the concentrating effects of evapotranspiration and/or the buildup and flushing of weathering products associated with longer residence times. The impacts of storage modeling on annual fluxes varied by solute, ranging from about 5% (magnesium) to 52% (nitrate) as relative standard deviations, and sufficiently removed climate-related patterns observed in streamwater concentrations. Sulfate was particularly mobilized following growing season droughts but only if deep storage was sufficiently recharged, possibly indicating that sulfides in the deep storage pool were oxidized to sulfate during droughts and mobilized when re-wetted. The lack of streamwater sulfate response to 61% declines in atmospheric deposition indicates the importance of watershed biogeochemical processes on controls of streamwater export of sulfate. The approach of explicitly incorporating storage in the streamwater concentration modeling elucidated the effects of climate on streamwater water-quality and may provide insight into the effects of climatic change on future fluxes.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13589","usgsCitation":"Aulenbach, B.T., 2020, Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States: Hydrological Processes, v. 34, no. 2, p. 189-208, https://doi.org/10.1002/hyp.13589.","productDescription":"20 p.","startPage":"189","endPage":"208","ipdsId":"IP-104585","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":367690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Panola Mountain Research Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.18994903564453,\n 33.61976556057674\n ],\n [\n -84.13021087646484,\n 33.61976556057674\n ],\n [\n -84.13021087646484,\n 33.64627826509988\n ],\n [\n -84.18994903564453,\n 33.64627826509988\n ],\n [\n -84.18994903564453,\n 33.61976556057674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771652,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211191,"text":"70211191 - 2020 - Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic","interactions":[],"lastModifiedDate":"2020-07-16T18:49:40.296564","indexId":"70211191","displayToPublicDate":"2019-09-03T13:44:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic","docAbstract":"Permafrost thaw in the Arctic is mobilizing old carbon (C) from soils to aquatic ecosystems and the atmosphere. Little is known, however, about the assimilation of old C by aquatic food webs in Arctic watersheds. Here, we used C isotopes (δ13C, Δ14C) to quantify C assimilation by biota across 12 streams in arctic Alaska. Streams spanned watersheds with varying permafrost hydrology, from ice-poor bedrock to ice-rich loess (that is, yedoma). We measured isotopic content of (1) C sources including dissolved organic C (DOC), dissolved inorganic C (DIC), and soil C, and (2) stream biota, including benthic biofilm and macroinvertebrates, and resident fish species (Arctic Grayling (Thymallus arcticus) and Dolly Varden (Salvelinus malma)). Findings document the assimilation of old C by stream biota, with depleted Δ14C values observed at multiple trophic levels, including benthic biofilm (14C ages = 5255 to 265 years before present (y BP)), macroinvertebrates (4490 y BP to modern), and fish (3195 y BP to modern). Mixing model results indicate that DOC and DIC contribute to benthic biofilm composition, with relative contributions differing across streams draining ice-poor and ice-rich terrain. DOC originates primarily from old terrestrial C sources, including deep peat horizons (39–47%; 530 y BP) and near-surface permafrost (12–19%; 5490 y BP). DOC also accounts for approximately half of fish isotopic composition. Analyses suggest that as the contribution of old C to fish increases, fish growth and nutritional status decline. We anticipate increases in old DOC delivery to streams under projected warming, which may further alter food web function in Arctic watersheds.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-019-00413-6","usgsCitation":"O'Donnell, J., Carey, M.P., Koch, J.C., Xu, X., Poulin, B., Walker, J., and Zimmerman, C.E., 2020, Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic: Ecosystems, v. 23, p. 435-453, https://doi.org/10.1007/s10021-019-00413-6.","productDescription":"19 p.","startPage":"435","endPage":"453","ipdsId":"IP-102831","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":437218,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NAUIQR","text":"USGS data release","linkHelpText":"Carbon Isotope Concentrations in Stream Food Webs of the Arctic Network National Parks, Alaska, 2014-2016"},{"id":376449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Land Bridge and Noatak National Preserves","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.58593749999997,\n 65.4217295985527\n ],\n [\n -156.09375,\n 65.4217295985527\n ],\n [\n -156.09375,\n 68.12248241161676\n ],\n [\n -165.58593749999997,\n 68.12248241161676\n ],\n [\n -165.58593749999997,\n 65.4217295985527\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Donnell, Jonathon A 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":222968,"corporation":false,"usgs":false,"family":"O'Donnell","given":"Jonathon A","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":793044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":793045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":793046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":793047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":793048,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jennifer","contributorId":201558,"corporation":false,"usgs":false,"family":"Walker","given":"Jennifer","affiliations":[],"preferred":false,"id":793049,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":793050,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209712,"text":"70209712 - 2020 - Assessment experimental semivariogram uncertainty in the presence of a polynomial drift","interactions":[],"lastModifiedDate":"2020-04-27T12:23:13.834768","indexId":"70209712","displayToPublicDate":"2019-05-14T09:54:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Assessment experimental semivariogram uncertainty in the presence of a polynomial drift","docAbstract":"The semivariogram, which measures the spatial variability between experimental data, is generally used as a structural input in all two-point geostatistical procedures. However, in most geoscience applications, experimental semivariograms are usually computed from a limited number of sparsely spaced measurements, which results in uncertainty associated with the semivariance values estimated for a specified number of lags. More importantly, considering a spatial variable modelled by a nonstationary random field, uncertainty is not only in the experimental semivariogram of the residuals, but also in the coefficients of the drift model estimated from the available experimental data. Therefore, when assessing the reliability of an experimental semivariogram (or estimated semivariances) in the nonstationary case, both aforementioned uncertainties should be taken into account. The aim of this paper is to extend the “Generalised Bootstrap” procedure to the nonstationary model by propagating the uncertainty associated with the estimated drift coefficients into the uncertainty in the experimental semivariogram of the residuals. The proposed methodology is demonstrated in a case study using abundant geophysical measurements characterised by a nonstationary random function. Two scenarios are evaluated in the case study: (1) it is assumed that the drift coefficients can be estimated without any uncertainty, and (2) uncertainty of the drift coefficients is taken into account. We have explained the methodology that allows to assess the uncertainty of the semivariogram lag estimates in the presence of the drift in the mean. Considering the second scenario, uncertainty is obviously larger than the case where uncertainty of the drift in the mean is ignored. This evaluation should be considered in applications where the data is often rather limited, such as subsurface hydrology (i.e. porosity, transmissivity), soil science (i.e. heavy metal content, soil moisture) and mining (i.e. scoping or pre-feasibility stage of the project). In fact, modern geostatistics should provide not only the semivariogram estimates but also estimation of its uncertainty.","language":"English","publisher":"Springer","doi":"10.1007/s11053-019-09496-3","collaboration":"","usgsCitation":"Oktay, E., Pardo-Iguzquiza, E., and Olea, R.A., 2020, Assessment experimental semivariogram uncertainty in the presence of a polynomial drift: Natural Resources Research, v. 29, no. 2, p. 1087-1099, https://doi.org/10.1007/s11053-019-09496-3.","productDescription":"13 p.","startPage":"1087","endPage":"1099","ipdsId":"IP-087621","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":374190,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Oktay, Erten","contributorId":224283,"corporation":false,"usgs":false,"family":"Oktay","given":"Erten","email":"","affiliations":[{"id":40846,"text":"Curtin U. Australia","active":true,"usgs":false}],"preferred":false,"id":787635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pardo-Iguzquiza, Eulogio","contributorId":208073,"corporation":false,"usgs":false,"family":"Pardo-Iguzquiza","given":"Eulogio","email":"","affiliations":[{"id":40847,"text":"Instituto Geologico y Minero de Espana","active":true,"usgs":false}],"preferred":false,"id":787636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olea, Ricardo A. 0000-0003-4308-0808 rolea@usgs.gov","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":208109,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo","email":"rolea@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":787637,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212046,"text":"70212046 - 2020 - Mapping climate change resistant vernal pools in the northeastern U.S.","interactions":[],"lastModifiedDate":"2021-09-22T15:37:46.324155","indexId":"70212046","displayToPublicDate":"2017-12-31T10:37:14","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Mapping climate change resistant vernal pools in the northeastern U.S.","docAbstract":"Vernal pools are seasonal wetlands that provide important breeding habitat for a variety of amphibian species. As future climate projections indicate warmer growing seasons and earlier seasonal increases in evapotranspiration, some managers of vernal pools have expressed concern that pools may dry earlier in the season, potentially interfering with completion of amphibian life cycles. In this context, a subset of pools might function as hydrologic refugia by providing wetland habitat later into the year under relatively dry conditions, thus supporting species persistence even as summer conditions become warmer and droughts more frequent. This study used approximately 3,000 field observations of inundation from 450 pools in the northeastern United States—located from West Virginia to Maine—to train machine-learning models for predicting the likelihood of pool inundation. Inputs to these models included pool size, day of the year, climate conditions, short-term weather patterns, and attributes of the landscapes in which pools were embedded. Predictions of pool wetness were generated on a daily time step from late April through late July using three short-term weather scenarios (dry, wet, and average) under historical climate conditions and four sets of downscaled climate projections (2050s and 2080s under Representative Concentration Pathways 4.5 and 8.5). The modeling and inundation prediction process was replicated using four inundation thresholds on wetted area and depth. Model outputs can enable users to examine the inundation thresholds, time points, weather scenarios, and future climate projections most relevant to their management needs. Together with long-term monitoring of individual pools at the site scale, this regional-scale study can support amphibian conservation by helping to identify subsets of pools that may be most likely to function as hydrologic refugia from changing climate conditions.","language":"English","publisher":"Northeast Climate Adaptation Science Center","usgsCitation":"Cartwright, J.M., and Campbell Grant, E.H., 2020, Mapping climate change resistant vernal pools in the northeastern U.S.: Cooperator Report, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-117745","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389593,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necasc.umass.edu/projects/mapping-climate-change-resistant-vernal-pools-northeastern-us"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia","otherGeospatial":"northeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.025390625,\n 37.020098201368114\n ],\n [\n -66.97265625,\n 44.59046718130883\n ],\n [\n -68.64257812499999,\n 47.45780853075031\n ],\n [\n -80.947265625,\n 42.61779143282346\n ],\n [\n -81.38671875,\n 39.436192999314095\n ],\n [\n -76.025390625,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":796184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796185,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204151,"text":"tm4A12 - 2019 - Regionalization of surface-water statistics using multiple linear regression","interactions":[],"lastModifiedDate":"2021-02-19T12:50:53.020266","indexId":"tm4A12","displayToPublicDate":"2021-02-18T15:40:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-A12","displayTitle":"Regionalization of Surface-Water Statistics Using Multiple Linear Regression","title":"Regionalization of surface-water statistics using multiple linear regression","docAbstract":"This report serves as a reference document in support of the regionalization of surface-water statistics using multiple linear regression. Streamflow statistics are quantitative characterizations of hydrology and are often derived from observed streamflow records. In the absence of observed streamflow records, as at unmonitored or ungaged locations, other techniques are required. Multiple linear regression is one tool that is widely used to regionalize or transfer information from gaged to ungaged locations. This report provides the background to support regression-based regionalization of streamflow statistics. This background includes tools for data assembly, exploratory data analysis, model estimation in a least-squares framework, and model evaluation.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A12","usgsCitation":"Farmer, W.H., Kiang, J.E., Feaster, T.D., and Eng, K., 2019, Regionalization of surface-water statistics using multiple linear regression (ver. 1.1, February 2021): U.S. Geological Survey Techniques and Methods, book 4, chap. A12, 40 p., https://doi.org/10.3133/tm4A12.","productDescription":"Report: v, 40 p.; Data Release; Version History","onlineOnly":"Y","ipdsId":"IP-081278","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":366986,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a12/coverthb2.jpg"},{"id":366987,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a12/tm4a12.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 4-A12"},{"id":366988,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T5ZEXV","text":"USGS Data Release","linkHelpText":"An example dataset for exploration of multiple linear regression"},{"id":383290,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/04/a12/versionHist.txt","text":"version history","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"T and M 4-A12 version history"}],"edition":"Version 1.0: August 29, 2019; Version 1.1: February 18, 2021","contact":"Director, Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
MS 415
12201 Sunrise Valley Drive
Reston, VA 20192
Fiber‐optic distributed temperature sensing (FO‐DTS) has proven to be a transformative technology for the hydrologic sciences, with application to diverse problems including hyporheic exchange, groundwater/surface‐water interaction, fractured‐rock characterization, and cold regions hydrology. FO‐DTS produces large, complex, and information‐rich datasets. Despite the potential of FO‐DTS, adoption of the technology has been impeded by lack of tools for data processing, analysis, and visualization. New tools are needed to efficiently and fully capitalize on the information content of FO‐DTS datasets. To this end, we present DTSGUI, a public‐domain Python‐based software package for editing, parsing, processing, statistical analysis, georeferencing, and visualization of FO‐DTS data.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12974","usgsCitation":"Domanski, M.M., Quinn, D., Day-Lewis, F.D., Briggs, M.A., Werkema, D.D., and Lane, J., 2019, DTSGUI: A python program to process and visualize fiber‐optic distributed temperature sensing data: Groundwater, v. 58, no. 5, p. 799-804, https://doi.org/10.1111/gwat.12974.","productDescription":"6 p.","startPage":"799","endPage":"804","ipdsId":"IP-112500","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":458818,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7814658","text":"External Repository"},{"id":380115,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Domanski, Marian M. 0000-0002-0468-314X mdomanski@usgs.gov","orcid":"https://orcid.org/0000-0002-0468-314X","contributorId":5035,"corporation":false,"usgs":true,"family":"Domanski","given":"Marian","email":"mdomanski@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinn, Daven","contributorId":244360,"corporation":false,"usgs":false,"family":"Quinn","given":"Daven","email":"","affiliations":[{"id":48903,"text":"University of Wisconsin–Madison, Department of Geoscience","active":true,"usgs":false}],"preferred":false,"id":803882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":803949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":803883,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werkema, Dale D.","contributorId":40488,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":803885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803886,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215098,"text":"70215098 - 2019 - Evaluating environmental change and behavioral decision-making for sustainability policy using an agent-based model: A case study for the Smoky Hill River Watershed, Kansas","interactions":[],"lastModifiedDate":"2020-10-08T13:27:57.138391","indexId":"70215098","displayToPublicDate":"2020-08-07T08:15:09","publicationYear":"2019","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":"Evaluating environmental change and behavioral decision-making for sustainability policy using an agent-based model: A case study for the Smoky Hill River Watershed, Kansas","docAbstract":"Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.
A groundwater flow model for western Chippewa County, Wisconsin, was developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS) using the computer program MODFLOW. The model is the result of a five-year groundwater study commissioned by Chippewa County in 2012 to evaluate the effects of industrial sand mining and irrigated agriculture on the county’s water resources. The study incorporates existing data and newly acquired data from fieldwork conducted within the study area. The groundwater model may be useful for future investigations, such as evaluation of proposed high-capacity well sites, development of municipal wellhead protection plans, and studies that seek to further quantify surface water-groundwater relationships.
The model conceptualizes the hydrostratigraphy of western Chippewa County as six stacked layers. Each layer is distinct, beginning with unlithified glacial material at the surface, and alternating between sandstones (that act as aquifers) and shale units (that serve as aquitards). The model is bounded below by Precambrian crystalline bedrock and its perimeter was derived from a regional-scale groundwater flow model.
The MODFLOW model represented average conditions during 2011–2013 with “steady-state” assumptions, meaning that simulated water levels do not fluctuate seasonally or from year to year. Steady-state models simplify natural variability, making results of scenario simulations easier to interpret and compare while also maximizing effects of stressors because the simulated stress is always applied (not halted after a few months or years). Model calibration used the parameter estimation code (PEST), and calibration targets included heads (groundwater levels) and streamflows. Calibration focused on 2011–2013 because a large amount of head and streamflow data were available for that period.
The MODFLOW model explicitly simulates all sources and sinks of water, including groundwater/surface-water interaction with streamflow routing. Model input included estimates of aquifer hydraulic conductivity and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance (SWB) model applied to the model area. Groundwater withdrawals were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and adjacent portions of Dunn, Barron, and Rusk Counties. Collectively, these wells withdrew about 1.14 million gallons per year between 2011 and 2013.
Once the model was calibrated, it was applied to two distinct scenarios of increased groundwater withdrawals: one evaluating hydrologic effects of more intensive industrial sand mining and the second evaluating the hydrologic effects of more intensive agricultural irrigation practices. Each scenario was developed with input from Chippewa County and a stakeholder group established expressly for this study. The scenarios were designed to represent reasonable future buildout conditions for both mining and irrigated agriculture. The mining scenario underscores the potential hydrologic effects related to changing land-use practices (i.e., hilltops and farmland becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydrologic effects of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields).
While each scenario evaluated distinctly different conditions, modeling results demonstrated the potential of both scenarios to lower the water table and reduce baseflows in headwater streams within the modeled area. In the case of irrigated agriculture, hydrologic effects were associated directly with groundwater withdrawals. By assuming that irrigation did not decrease, this steady-state simulation represented a sustained future effect. By contrast, hydrologic effects of industrial sand mining were the result of both groundwater withdrawals at mines and land-use changes that effectively reduced recharge to groundwater over distinct phases of active mining. This scenario included a post-mining phase, during which groundwater withdrawals stopped and mined areas were reclaimed to undeveloped prairie grass cover. If reclamation to undeveloped prairie indeed occurs as simulated, long-term increases in the water table and stream baseflows are possible. In this sense, the scenario representing build out of irrigated agriculture led to long-term baseflow declines while the future buildout of industrial sand mining led to declines that dissipated following mine reclamation to undisturbed prairie.
Future investigations in similar hydrogeologic settings may find the following insights gleaned from this study useful:
❚❚ The characterization of hydrogeologic properties, delineation of hydrogeologic units, and calibration of groundwater flow models benefited from incorporation of accurate well construction reports, high-quality borehole geophysical logs, and streamflow gaging data.
❚❚ Infiltration testing performed in active mining areas provided evidence that reducing the degree and extent of compaction and enhancing areas designed to retain and infiltrate stormwater runoff could potentially reduce runoff and increase groundwater recharge.
❚❚ Similarly, reclaiming mined areas to prairie grasses would be expected to reduce runoff and increase groundwater recharge by reducing compaction and improving soil structure and vegetation that can slow runoff and enhance infiltration.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Parsen, M., Juckem, P.F., Gotkowitz, M., and Fienen, M.N., 2019, Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture: Wisconsin Geological and NaturalHistory Survey Bulletin B112, 74 p.","productDescription":"74 p.","ipdsId":"IP-093476","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":375174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375173,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/pubs/b112/"}],"country":"United States","state":"Wisconsin","county":"Chippewa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8511962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 44.859762688042736\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsen, Michael","contributorId":216283,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gotkowitz, Madeline","contributorId":216284,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207596,"text":"sir20195149 - 2019 - An update of hydrologic conditions and distribution of selected constituents in water, Eastern Snake River Plain aquifer and perched groundwater zones, Idaho National Laboratory, Idaho, emphasis 2016–18","interactions":[],"lastModifiedDate":"2022-04-25T20:30:32.652286","indexId":"sir20195149","displayToPublicDate":"2020-02-18T10:32:38","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5149","displayTitle":"An Update of Hydrologic Conditions and Distribution of Selected Constituents in Water, Eastern Snake River Plain Aquifer and Perched Groundwater Zones, Idaho National Laboratory, Idaho, Emphasis 2016–18","title":"An update of hydrologic conditions and distribution of selected constituents in water, Eastern Snake River Plain aquifer and perched groundwater zones, Idaho National Laboratory, Idaho, emphasis 2016–18","docAbstract":"Since 1952, wastewater discharged to infiltration ponds (also called percolation ponds) and disposal wells at the Idaho National Laboratory (INL) has affected water quality in the eastern Snake River Plain (ESRP) aquifer and perched groundwater zones underlying the INL. The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, maintains groundwater-monitoring networks at the INL to determine hydrologic trends and to delineate the movement of radiochemical and chemical wastes in the aquifer and in perched groundwater zones. This report presents an analysis of water-level and water-quality data collected from the ESRP aquifer and perched groundwater wells in the USGS groundwater monitoring networks during 2016–18.
From March–May 2015 to March–May 2018, water levels in wells completed in the ESRP aquifer declined in the northern part of the INL and increased in the southwestern part. Water-level decreases ranged from 0.5 to 3.0 feet (ft) in the northern part of the INL and increases ranged from 0.5 to 3.0 ft in the southwestern part.
Detectable concentrations of radiochemical constituents in water samples from wells in the ESRP aquifer at the INL generally decreased or remained constant during 2016–18. Decreases in concentrations were attributed to radioactive decay, changes in waste-disposal methods, and dilution from recharge and underflow.
In 2018, concentrations of tritium in water samples collected from 46 of 111 aquifer wells were greater than the reporting level of three times the sample standard deviation and ranged from 260±50 to 5,100±190 picocuries per liter (pCi/L). Tritium concentrations in water from 10 wells completed in deep perched groundwater above the ESRP aquifer near the Advanced Test Reactor (ATR) Complex generally were greater than or equal to the reporting level during at least one sampling event during 2016–18, and concentrations ranged from 150 ±50 to 12,900 ±200 pCi/L.
Concentrations of strontium-90 in water from 17 of 60 ESRP aquifer wells sampled during April or October 2018 exceeded the reporting level, ranging from 2.2±0.7 to 363±19 pCi/L. Strontium-90 was not detected in the ESRP aquifer beneath the ATR Complex. During at least one sampling event during 2016–18, concentrations of strontium-90 in water from eight wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex equaled or exceeded the reporting levels, and concentrations ranged from 0.57±0.17 to 34.3±1.2 pCi/L.
During 2016–18, concentrations of cesium-137 were less than the reporting level in all but one ESRP aquifer well, and concentrations of plutonium-238, -239, and -240 (undivided), and americium-241 were less than the reporting level in water samples from all ESRP aquifer wells.
In April 2009, the dissolved chromium concentration in water from one ESRP aquifer well, USGS 65, south of the ATR Complex equaled the maximum contaminant level (MCL) of 100 micrograms per liter (μg/L). In April 2018, the concentration of chromium in water from that well had decreased to 76.0 μg/L, less than the MCL. Concentrations in water samples from 62 other ESRP aquifer wells sampled ranged from less than 0.6 to 21.6 μg/L. During 2016–18, dissolved chromium was detected in water from all wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex, and concentrations ranged from 4.2 to 98.8 μg/L.
In 2018, concentrations of sodium in water from most ESRP aquifer wells in the southern part of the INL were greater than the western tributary background concentration of 8.3 milligrams per liter (mg/L). After the new percolation ponds were put into service in 2002 southwest of the Idaho Nuclear Technology and Engineering Center (INTEC), concentrations of sodium in water samples from the Rifle Range well increased steadily until 2008, when concentrations generally began decreasing. The increases and decreases were attributed to disposal variability in the new percolation ponds. During 2016–18, dissolved sodium concentrations in water from 18 wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex ranged from 6.37 to 143 mg/L.
In 2018, concentrations of chloride in most water samples from ESRP aquifer wells south of the INTEC and at the Central Facilities Area exceeded the background concentrations. Chloride concentrations in water from wells south of the INTEC generally have decreased since 2002 when chloride disposal to the old percolation ponds was discontinued. After the new percolation ponds southwest of the INTEC were put into service in 2002, concentrations of chloride in water samples from one well rose steadily until 2008 then began decreasing. During 2016–18, dissolved chloride concentrations in deep perched groundwater above the ESRP aquifer from 18 wells at the ATR Complex ranged from 3.89 to 176 mg/L.
In 2018, sulfate concentrations in water samples from ESRP aquifer wells in the south-central part of the INL exceeded the background concentration of sulfate and ranged from 22 to 151 mg/L. The greater-than-background concentrations in water from these wells probably resulted from sulfate disposal at the ATR Complex infiltration ponds or the old INTEC percolation ponds. In 2018, sulfate concentrations in water samples from wells near the Radioactive Waste Management Complex (RWMC) mostly were greater than background concentrations and could have resulted from well construction techniques and (or) waste disposal at the RWMC or the ATR complex. The maximum dissolved sulfate concentration in shallow perched groundwater above the ESRP aquifer near the ATR Complex was 215 mg/L in well CWP 3 in April 2016. During 2018, dissolved sulfate concentrations in water from wells completed in deep perched groundwater above the ESRP aquifer near the cold-waste ponds at the ATR Complex ranged from 65.8 to 171 mg/L.
In 2018, concentrations of nitrate in water from most ESRP aquifer wells at and near the INTEC exceeded the western tributary background concentration of 0.655 mg/L. Concentrations of nitrate in wells southwest of the INTEC and farther away from the influence of disposal areas and the Big Lost River show a general decrease in nitrate concentration through time. Two wells south of the INTEC show increasing trends that could be the result of wastewater beneath the INTEC tank farm being mobilized to the aquifer.
During 2016–18, water samples from several ESRP aquifer wells were collected and analyzed for volatile organic compounds (VOCs). Sixteen VOCs were detected. At least 1 and as many as 7 VOCs were detected in water samples from 15 wells. The primary VOCs detected include carbon tetrachloride, trichloromethane, tetrachloroethene, 1,1,1-trichloroethane, and trichloroethene. In 2016–18, concentrations for all VOCs were less than their respective MCLs for drinking water, except carbon tetrachloride in water from two wells and trichloroethene in one well.
During 2016–18, variability and bias were evaluated from 37 replicate and 15 blank quality-assurance samples. Results from replicate analyses were investigated to evaluate sample variability. Constituents with acceptable reproducibility were major ions, trace elements, nutrients, and VOCs. All radiochemical constituents had acceptable reproducibility except for gross alpha- and beta-particle radioactivity. The gross alpha- and beta-particle radioactivity samples that did not meet reproducibility criteria had low concentrations. Bias from sample contamination was evaluated from equipment, field, and source-solution blanks. Cadmium had a concentration slightly greater than its reporting level in a source-solution blank, and chloride and ammonia had concentrations that were slightly greater than their respective reporting levels in field and equipment blanks. Subtracting concentrations of chloride and ammonia in field blanks from the concurrently collected equipment blank indicates that adjusted concentrations for chloride and ammonia in the equipment blanks were less than their respective reporting levels. Therefore, no sample bias was observed for any of the sample periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195149","collaboration":"DOE/ID-22251Director, Idaho Water Science Center
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Although spatial information describing the supply and quality of surface water is critical for managing water resources for human uses and for ecological health, monitoring is expensive and cannot typically be done over large scales or in all streams or waterbodies. To address the need for such data, the U.S. Geological Survey developed SPAtially Referenced Regression On Watershed attributes (SPARROW) for the Pacific region of the U.S. for streamflow and three water-quality constituents–total nitrogen, total phosphorus, and suspended sediment, based on a decadal time frame centered on the year 2012. The domain for these models included the Columbia River basin, the Puget Sound, the coastal drainages of Washington, Oregon, and California, and the Central Valley of California. Landscape runoff (represented by the difference between precipitation and evapotranspiration) was the largest source of streamflow, wastewater discharge, and atmospheric deposition were the largest contributors to total nitrogen yield from the Pacific region, wastewater discharge was the largest contributor to total phosphorus yield, and forest land was the largest contributor to suspended-sediment yield. Watersheds with relatively high water yields also generally had relatively high yields of total nitrogen, total phosphorous, and suspended sediment–except where there were large contributions from developed land and wastewater discharge.
The data used in this study, including many that improved upon existing national data or were compiled specifically for the Pacific region, characterized the complex hydrologic and water-quality conditions in the region more completely than previous models. By using these new datasets, this investigation was able to account for the complex network of water diversions and transfers, quantify the contribution of nutrients from different sources of livestock manure, discern a signal from unpaved logging roads in the suspended-sediment yields from forested coastal watersheds, show how recent wildfire disturbance influences phosphorus and sediment delivery to streams, and how sediment delivery to streams is also sensitive to the intensity of cattle grazing. The results from this study could complement research and inform water-quality management activities in the Pacific region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings to Pacific coast estuaries where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195112","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Pacific region of the United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5112, 64 p., https://doi.org/10.3133/sir20195112.","productDescription":"Report: x, 64 p.; Data Release; Application Site; Companion Files; Version History; Read Me","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103780","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":437240,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V1RY3N","text":"USGS data release","linkHelpText":"Application of nutrients generated by non-cattle livestock to 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Streams of the Southwestern United States"}],"country":"United States","otherGeospatial":"Pacific Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.42187500000001,\n 33.7243396617476\n ],\n [\n -120.58593749999999,\n 38.54816542304656\n ],\n [\n -120.58593749999999,\n 42.16340342422401\n ],\n [\n -118.828125,\n 42.16340342422401\n ],\n [\n -117.24609374999999,\n 43.58039085560784\n ],\n [\n -113.5546875,\n 44.84029065139799\n ],\n [\n -115.83984375,\n 48.3416461723746\n ],\n [\n -119.53125,\n 49.38237278700955\n ],\n [\n -124.27734374999999,\n 48.922499263758255\n ],\n [\n -124.98046874999999,\n 47.39834920035926\n ],\n [\n -124.8046875,\n 43.70759350405294\n ],\n [\n -124.45312499999999,\n 38.41055825094609\n ],\n [\n -120.76171875,\n 33.87041555094183\n ],\n [\n -117.42187500000001,\n 33.7243396617476\n ]\n ]\n ]\n }\n }\n 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Reston, VA 20192–0002
Given the predicted imbalance between water supply and demand in the Southwest region of the United States, and the widespread problems with excessive nutrients and suspended sediment, there is a growing need to quantify current streamflow and water quality conditions throughout the region. Furthermore, current monitoring stations exist at a limited number of locations, and many streams lack streamflow and water quality information. SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed for hydrologic conditions representative of 2012 in order to understand how climate, land use, and other landscape characteristics control the yields of water, total nitrogen, total phosphorus, and suspended sediment across the Southwest region. The calibration data (mean annual streamflow and loads) for each of the four SPARROW models were based on continuous streamflow and discrete water-quality observations from throughout the region. Explanatory variables for the models consisted of regional datasets representing a range of potential sources of streamflow, nitrogen, phosphorous, and sediment, and processes that control the transport from land to water and attenuate loads within streams and waterbodies. Calibration and explanatory data were referenced to a surface water drainage network that allowed for routing and transport of water and loads through the region. The model results showed that wastewater discharge is the largest contributor to total nitrogen and total phosphorus yield from the Southwest region and forest land is the largest contributor to suspended-sediment yield, but that other sources such as atmospheric nitrogen deposition, agricultural runoff, and runoff from developed land are locally important across the region. The results from this study could complement research and inform water-quality management activities in the Southwest region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195106","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., Anning, D.W., and Miller, O.L., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment transport in streams of the southwestern United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5106, 66 p., https://doi.org/10.3133/sir20195106.","productDescription":"Report: viii, 66 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105772","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437233,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94EKLPP","text":"USGS data release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 Base Year (ver. 2.0, October 2020)"},{"id":370350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5106/coverthb.jpg"},{"id":371969,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5106/sir20195106.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5106"},{"id":370352,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GFLBG8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 base year"},{"id":370377,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southwest-2012","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370703,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370704,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370705,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":376099,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5106/VersionHist.txt"},{"id":370706,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"country":"United States","otherGeospatial":"Southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.927734375,\n 32.39851580247402\n ],\n [\n -114.873046875,\n 32.76880048488168\n ],\n [\n -111.09374999999999,\n 31.203404950917395\n ],\n [\n -108.28125,\n 31.052933985705163\n ],\n [\n -108.19335937499999,\n 32.02670629333614\n ],\n [\n -105.8203125,\n 30.977609093348686\n ],\n [\n -103.447265625,\n 28.76765910569123\n ],\n [\n -102.83203125,\n 29.6880527498568\n ],\n [\n -101.689453125,\n 29.76437737516313\n ],\n [\n -100.1953125,\n 27.916766641249065\n ],\n [\n -98.349609375,\n 26.115985925333536\n ],\n [\n -97.20703125,\n 26.509904531413927\n ],\n [\n -96.767578125,\n 28.22697003891834\n ],\n [\n -93.515625,\n 29.53522956294847\n ],\n [\n -99.49218749999999,\n 33.87041555094183\n ],\n [\n -103.623046875,\n 38.8225909761771\n ],\n [\n -104.94140625,\n 40.245991504199026\n ],\n [\n -111.97265625,\n 42.94033923363181\n ],\n [\n -113.90625,\n 41.376808565702355\n ],\n [\n -116.806640625,\n 41.77131167976407\n ],\n [\n -118.740234375,\n 44.213709909702054\n ],\n [\n -120.498046875,\n 44.59046718130883\n ],\n [\n -120.84960937499999,\n 41.44272637767212\n ],\n [\n -118.65234374999999,\n 36.10237644873644\n ],\n [\n -115.927734375,\n 32.39851580247402\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 7, 2020; Version 1.1: June 2, 2020","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
In this report, SPAtially Referenced Regression On Watershed attributes (SPARROW) models developed to describe long-term (2000–14) mean-annual streamflow, total nitrogen (TN), total phosphorus (TP), and suspended-sediment (SS) transport in streams of the Midwestern part of the United States (the Mississippi River, Great Lakes, and Red River of the North Basins) are described. The nutrient and suspended-sediment models have a base year of 2012, which means they were developed based on source inputs and management practices similar to those existing during or near 2012 and average hydrological conditions detrended to 2012 (2000–14), whereas the streamflow model has base years of 2000–14, which means it was developed based on the average input precipitation minus actual evapotranspiration from 2000 to 2014. In developing the models, several updates and improvements were made to the data inputs and statistical approaches used to calibrate/develop the models from those used in the previous 2002 SPARROW models. The 2012 SPARROW models were constructed using a higher resolution stream network, which resulted in a mean catchment size of 2.7 square kilometers compared to 480 square kilometers in the 2002 models; more detailed and updated wastewater treatment plant contribution estimates; inputs from background phosphorus sources that were not included in the 2002 model; and more accurate loads for calibration that were computed using a modified Beale ratio-estimator technique whenever no trend in load was determined. Statistical approaches were added to compensate for the unequal effect of each monitoring site during the calibration process by adjusting for the fraction of the basin included in other upstream monitored sites (nested share) and thinning the calibration sites if a negative statistical correlation between nearby sites was determined.
Results from 2012 SPARROW models describe how much of each water, TN, TP, and SS source was delivered to the stream network, and the major landscape factors that affected their delivery. Atmospheric deposition and natural (background) sources of TN and TP, respectively, were the dominant sources in anthropogenically unaffected areas (especially in the Rocky Mountains and north-central areas of the Midwest), whereas fertilizers, manure, and fixation were dominant sources in agricultural areas, especially in the Corn Belt and near the Mississippi River. Urban sources of TN and TP were typically localized, but they were still important for some large areas, especially the Lake Erie Basin. All of the land-to-water delivery variables in the nutrient and sediment SPARROW models, such as runoff, soil erodibility, basin slope, and the amount of tile drains, are commonly included in process-driven models. In the SPARROW TN and TP models, best management practices (BMPs) reduced the delivery of these nutrients to streams.
Long-term mean-annual flows and nutrient and sediment loads were simulated in streams throughout the Midwest. The simulated flows from the SPARROW flow model were used in the SPARROW TN, TP, and SS models to help describe nutrient and sediment transport from the watershed and through the stream network. Outputs from the TN, TP, and SS models describe loads and yields of these constituents throughout the Midwest, and from major drainage basins throughout the Midwest. Highest TN, TP, and SS yields and delivered yields were from the Lake Erie, Ohio River, Upper Mississippi River, and Lower Mississippi River Basins, whereas lowest yields were spread over most other areas. Losses during downstream delivery resulted in part of the TN, TP, and SS that reach the stream network not reaching the downstream receiving bodies: 14, 15, and 28 percent of the TN, TP, and SS, respectively, are lost during delivery to the Great Lakes and 19, 23, and 52 percent of the TN, TP, and SS, respectively, are lost during delivery to the Gulf of Mexico. The largest losses of nutrients and sediments during transport were in the Missouri and Arkansas River Basins.
Information from these SPARROW models can help guide nutrient and sediment reduction strategies throughout the Midwest. Model results provide information on what may be the most appropriate general type of actions to reduce total loading by describing the relative importance of each source, and where to most efficiently place the efforts to reduce loading by describing the distribution of nutrient and sediment loading. By implementing management efforts addressing the major sources of the loads in areas contributing the highest loads, it may be possible to reduce nutrient loading throughout the Mississippi River Basin and thus reduce the size of the hypoxic zone in the Gulf of Mexico; reduce nutrient loading into lakes, and thus reduce the occurrence of harmful algal blooms; and reduce sediment losses, and thus improve the benthic habitat in streams and rivers throughout the Midwest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195114","collaboration":"National Water Quality Program","usgsCitation":"Robertson, D.M., and Saad, D.A., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Midwestern United States: U.S. Geological Survey Scientific Investigations Report 2019–5114, 74 p. including 5 appendixes, https://doi.org/10.3133/sir20195114.","productDescription":"Report: ix, 74 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103244","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370714,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"},{"id":370711,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370712,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":371971,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5114/sir20195114.pdf","text":"Report","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5114"},{"id":370371,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-midwest-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370713,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":370369,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93QMXC9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 base year"},{"id":370914,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5114/coverthb3.jpg"}],"otherGeospatial":"Midwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 44.213709909702054\n ],\n [\n -79.27734374999999,\n 43.389081939117496\n ],\n [\n -79.541015625,\n 42.61779143282346\n ],\n [\n -82.529296875,\n 41.44272637767212\n ],\n [\n -82.177734375,\n 43.068887774169625\n ],\n [\n -82.705078125,\n 45.02695045318546\n ],\n [\n -84.287109375,\n 46.6795944656402\n ],\n [\n -90,\n 47.931066347509784\n ],\n [\n -94.5703125,\n 49.210420445650286\n ],\n [\n -95.361328125,\n 49.26780455063753\n ],\n [\n -95.44921875,\n 48.922499263758255\n ],\n [\n -114.2578125,\n 49.095452162534826\n ],\n [\n -113.90625,\n 46.73986059969267\n ],\n [\n -112.236328125,\n 45.336701909968134\n ],\n [\n -110.12695312499999,\n 42.48830197960227\n ],\n [\n -106.69921875,\n 38.06539235133249\n ],\n [\n -101.42578124999999,\n 32.99023555965106\n ],\n [\n -97.20703125,\n 28.536274512989916\n ],\n [\n -92.900390625,\n 29.305561325527698\n ],\n [\n -89.912109375,\n 33.063924198120645\n ],\n [\n -79.541015625,\n 39.774769485295465\n ],\n [\n -76.11328125,\n 44.213709909702054\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
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Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP−III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length greater than 36 years, 368 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval, censored, and historic/paleo flow data, as well as the Multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction (AVPnew) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVPnew corresponds to an effective record length of 54 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195105","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2019, Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2019–5105, 26 p., https://doi.org/10.3133/sir20195105.","productDescription":"Report: vi, 25 p.; 5 Figures; Table; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101994","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":371689,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_table1.xlsx","text":"Table 1","size":"99.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Streamgages in parts of the Great Lakes and Ohio River Basins considered for use in regional skew analysis"},{"id":371684,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01a.pdf","text":"Figure 1A","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing 4-digit hydrologic units"},{"id":371685,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01b.pdf","text":"Figure 1B","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing locations of streamgages used in skew analysis"},{"id":371682,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N7UAFJ","text":"USGS data release","linkHelpText":"Annual peak-flow data, PeakFQ specification files and PeakFQ output files for 368 selected streamflow gaging stations operated by the U.S. Geological Survey in the Great Lakes and Ohio River basins that were used to estimate regional skewness of annual peak flows"},{"id":371681,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5105"},{"id":371680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5105/coverthb.jpg"},{"id":399546,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109629.htm"},{"id":371688,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig05.pdf","text":"Figure 5","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing residuals from constant model of skew for 368 streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371687,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig03.pdf","text":"Figure 3","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing unbiased station skew of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371686,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig02.pdf","text":"Figure 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing the pseudo record lengths of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"}],"country":"United States, Canada","otherGeospatial":"Great Lakes and Ohio River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3740234375,\n 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Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
To address the lack of information on the spatial and temporal variability of recharge to groundwater systems in Maine, a study was initiated in cooperation with the Maine Geological Survey to use the U.S. Geological Survey Soil-Water-Balance model to evaluate annual average potential recharge across the State over a 25-year period from 1991 to 2015. The Maine Soil-Water-Balance model was calibrated using annual observations of recharge, runoff, and evapotranspiration for 32 calibration watersheds in the State during 2001–12 (902 total observations). Observations of recharge, runoff, and evapotranspiration were developed for each watershed to reduce the possibility of nonunique combinations of model parameters during the calibration. The Maine Soil-Water-Balance model was run using an optional evapotranspiration calculation method that provides more control for calibration than the standard method. The model was calibrated using the Parameter ESTimation software suite.
The overall mean model error (average of all annual residuals for recharge, runoff, and precipitation) was 0.39 inch. The mean of the absolute value of the residuals, or the mean absolute error, was 2.32 inches. The root mean squared error for the calibrated model overall was 3.14 inches. Statistical tests indicated that the model residuals are normally distributed. To determine the potential uncertainty in the median annual potential recharge that results from uncertainty in the parameters as they relate to information contained in the observations, 300 alternate model realizations were run, and the standard deviation of the median potential recharge value at every pixel was calculated.
Simulated 25-year median potential recharge across the State is widely variable; this variability closely follows patterns of precipitation, with additional variability contributed by the patchwork nature of the combinations of land-use class and hydrologic soil group inputs, and distribution of available water capacity in the soil across the State. Overall, the 25-year median annual potential recharge across the State is 7.5 inches, ranging from a low of about 5 inches to over 30 inches. The statewide range in the 25-year minimum values is from just over 2 inches to just over 20 inches. The statewide range in the 25-year maximum potential recharge is between 15 and 48 inches per year.
The model areas with the highest simulated median potential recharge include areas underlain by type A soils (sandy and well drained), particularly those that also have land uses with low or little vegetation (blueberry barrens, developed, open space, scrub/shrub, and cropland, for example). The potential recharge values for these areas are similar to previously published values for comparable soil types.
The 25-year average potential recharge grids were compared to recharge evaluated through groundwater-flow models or other methods in four hydrogeologic settings at six study areas in the State. A key factor in the ability of the Soil-Water-Balance model to reproduce the earlier study results was whether the available water-capacity data were an appropriate match for the hydrologic soil groups. The Maine Soil-Water-Balance model does a good job in representing an accurate potential recharge under circumstances where the surficial mapped soils extend below the surface to the water-table aquifer and where the available water-capacity data are in an appropriate range for the hydrologic soil group. One hydrogeologic setting that was challenging for the model was where a silt and clay layer was below a shallow soil unit that did not have available water-capacity data that were appropriate for the hydrologic soil group. In these cases, typically the available water-capacity data were very low, not accounting for the impedance of water flow provided by the underlying soil. The model also does not simulate well areas where bedrock surfaces are above the water table but below the plant rooting zone.
The data products accompanying this report are intended to be used to provide first-cut estimates of recharge for geographic areas no smaller than the smallest watersheds used in the calibration of the model—or about 1.5 square miles. It is recommended that the grids are used to calculate an area-wide average potential recharge for any given area of study, and an uncertainty around the mean should be calculated from the standard deviation grid at the same time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195125","collaboration":"Prepared in cooperation with the Maine Geological Survey","usgsCitation":"Nielsen, M.G., and Westenbroek, S.M., 2019, Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015: U.S. Geological Survey Scientific Investigations Report 2019–5125, 58 p., https://doi.org/10.3133/sir20195125.","productDescription":"Report: vii, 56 p.; Tables; 2 Data Releases","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106360","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380621,"rank":7,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The United States Geological Survey (USGS) provided technical support to the Washington Department of Ecology (Ecology) in their assessment of the role groundwater plays in contributing pollutant loading to the Green-Duwamish River near Seattle, Washington. Ecology is developing watershed hydrology models of the Green-Duwamish watershed, and need to assign realistic contaminant concentrations to the various Hydrologic Response Units represented in their models. The USGS compiled existing groundwater quality data in the Green-Duwamish watershed, and this report summarizes results and interpretation of the dataset, including identifying data gaps and needs for further research and monitoring. The sources of existing data were the USGS’s National Water Information System, Ecology’s Environmental Information Management System, and a compilation of several studies by Leidos, a scientific research company. The water-quality parameters of interest included polychlorinated biphenyl (PCB) Aroclors and congeners, phthalates, carcinogenic polycyclic aromatic hydrocarbons (cPAHs), arsenic, copper, and zinc. Results were grouped into the four subwatersheds delineated in Ecology’s hydrology models: Duwamish, Lower Green, Soos, and Upper Green. Results from the Duwamish subwatershed were further sub-divided by the USGS into the Lower Duwamish, containing land adjacent to the Lower Duwamish Waterway Superfund site, and the Upper Duwamish, containing the remaining area of the Duwamish subwatershed. Groundwater quality data in the Lower Duwamish were treated separately because there is known contamination in this area. The availability of water quality data varied by subwatershed as follows: phthalate data was only available within the Duwamish, PCB data was available within the Duwamish and Lower Green, cPAH data was available within the Duwamish, Lower Green, and Soos, and data for arsenic, copper, and zinc were available within all four subwatersheds. More than 99 percent of the available data was within the Duwamish subwatershed, identifying a need for additional monitoring of groundwater quality in the other subwatersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191131","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Senter, C.A., Conn, K.E., Black, R.W., Welch, W.B., and Fasser, E.T., 2020, Assessment of existing groundwater quality data in the Green-Duwamish watershed, Washington: U.S. Geological Survey Open-File Report 2019-1131, 35 p., https://doi.org/10.3133/ofr20191131.","productDescription":"iv, 35 p.","onlineOnly":"Y","ipdsId":"IP-111911","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":371094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1131/coverthb2.jpg"},{"id":371095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1131/ofr20191131.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1131"},{"id":399422,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109587.htm"}],"country":"United States","state":"Washington","otherGeospatial":"Green-Duwamish watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.26684570312499,\n 47.59505101193038\n ],\n [\n -122.420654296875,\n 47.60245929546312\n ],\n [\n -122.43713378906249,\n 47.51349065484327\n ],\n [\n -122.431640625,\n 47.32393057095941\n ],\n [\n -122.420654296875,\n 47.18597932702905\n ],\n [\n -122.3876953125,\n 47.010225655683485\n ],\n [\n -122.288818359375,\n 46.912750956378915\n ],\n [\n -122.2283935546875,\n 46.781254534638606\n ],\n [\n -122.0855712890625,\n 46.558860303117164\n ],\n [\n -121.59667968749999,\n 46.32417161725691\n ],\n [\n -121.39892578125,\n 46.32796494040746\n ],\n [\n -121.30004882812499,\n 46.49839225859763\n ],\n [\n -121.168212890625,\n 46.78501604269254\n ],\n [\n -121.36596679687499,\n 46.991494313050424\n ],\n [\n -121.95922851562501,\n 47.4355191531953\n ],\n [\n -122.26684570312499,\n 47.59505101193038\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Climate change poses unique challenges in snow-fed river basins across the western United States because the majority of water supply originates as snow (Dettinger, Udall, & Georgakakos, 2015). In the Sierra Nevada, recent observations include changes in snow accumulation and snowmelt, and shifts in peak streamflow timing (Barnhart et al., 2016; Hatchett et al., 2017; Kim & Jain, 2010; McCabe, Wolock, & Valentin, 2018; Mote, Li, Lettenmaier, Xiao, & Engel, 2018). Such changes upstream alter surface water deliveries downstream, as well as groundwater recharge utilized as both primary and supplemental water supply (Godsey et al., 2014; Harpold, 2016; Jasechko et al., 2014).
basin where snowmelt runoff produces substantial water supply to meet diverse agricultural, environmental and urban water demand (Figure 1). The East and West Forks join at the confluence of the Carson River near the north end of the Carson Valley, a rich agricultural region (40,000 acres) that grows primarily alfalfa hay. The majority of irrigators rely on surface water delivered through a network of earthen ditches constructed in the mid-19th and early 20th centuries. Flow through these earthen networks and the practice of flood irrigation contribute significantly to groundwater recharge.
Because no upstream surface water reservoirs exist, snowpack that accumulates through winter and melts slowly through spring has acted as a “natural” reservoir, providing ample supply through the summer irrigati agricultural, environmental and urban water demand (Figure 1). The East and West Forks join at the confluence of the Carson River near the north end of the season. Some irrigators have permitted access to supplemental groundwater that is useful during periods of drought for augmenting shortfalls in surface water delivery. Groundwater is the primary source of municipal and industrial water supply for surrounding communities (e.g., Carson City, Minden, Gardnerville, Dayton).
Across the basin, water use is highly regulated through federal, tribal, state and local water-sharing agreements based on prior appropriation doctrine (Wilds, 2014). Carson River surface water allocations follow the Alpine Decree, initiated by the United States Department of Interior in 1925 and signed into law in 1980, following 55 years of litigation, to adjudicate surface water rights to individual parties (NDWP, 1999). The Alpine Decree acknowledges return flows to lower river segments, and thus each river segment is distributed autonomously. This means that the most junior water right on an upper segment can be fulfilled before considering the most senior water right on a lower segment. Ultimately, the ruling is at the discretion of the Federal Water Master to satisfy the needs of each water right
Downstream of Carson Valley, surface water flows are stored in Lahontan Reservoir, the nation’s first desert reclamation project (est. 1906), where releases are managed to meet the Newland’s Project irrigation water demand and for environmental use on the Stillwater National Wildlife Refuge. Flows from the Carson River are supplemented through diversions from the Truckee River via the Truckee Canal, resulting in a trans-basin water supply system.
In the Truckee-Carson River System, researchers and local water managers are working together to assess climate change impacts to water supply and explore how model simulations can produce useful information to support local climate adaptation. Twelve key water managers represent agricultural, environmental, urban and regulatory water-use communities, and bring to the table diverse input and perspectives on how to adapt to climate change.
Hydrologists use this input to craft scenarios and simulations that meet the information needs of local water managers. Biannual workshops provide an opportunity for information exchange, where researchers and key water managers generate new knowledge of river system function. That is, researchers share results of models that examine the physical potential, and managers validate the on-the-ground potential, further informing the research process.
Coincident to this research program, the region faced a prolonged drought period (2012-2016) with historically low snowpack, followed by a historic wet year (2017) that brought winter and spring flooding as a result of atmospheric river storm events (Sterle et al., 2019). For the Carson River, an important observation made by managers was that peak streamflow that had traditionally coincided with peak irrigation demands, had shifted to earlier in the spring, with summer baseflow also decreasing (Sterle & Singletary, 2017). Managers shared with researchers concerns over potential future impacts that changing snowpack will have on surface water deliveries and reliance on groundwater, as the region’s population and economy continue to grow. During workshops that occurred over this period, local water managers and researchers discussed ways to evaluate water distribution and use that honors the existing legal framework and accounts for changing snowpack regimes (amount, rain versus snow, timing). In response to managers growing interest, researchers introduced the concept of managed aquifer recharge as one potential strategy to adapt and enhance regional water sustainability.
What is managed aquifer recharge? Simply stated, managed aquifer recharge is the intentional recharge of structures to spread water over agricultural lands, allowing water to naturally infiltrate into the groundwater system (Bouwer, 1999; Niswonger et al., 2017). The latter may occur during the irrigation season by applying excess water, or during the nonirrigation season when evapotranspiration losses are low. Figure 2 illustrates managed aquifer recharge in a snow-fed river basin, where streamflow generated from snowmelt runoff is diverted to agricultural lands to recharge the aquifer. Such flood irrigation practices, including water delivery through earthen ditch networks, provide incidental but significant aquifer recharge through seepage and deep drainage beneath fields (Niswonger, Allander, & Jeton, 2014). The effects of managed aquifer recharge can vary depending on the location and intensity of practice.
For example, implementing managed aquifer recharge water into the groundwater system (Dillon, 2009). This differs from the incidental recharge that may occur as part of normal irrigation practices. Managed recharge may occur by injection into the aquifer through existingwells, or by using existing conveyance adjacent to/along the river’s floodplain has the potential to enhance late-season instream flows due to increased return flows, resulting in greater downstream deliveries as well as improving ecological conditions (Niswonger et al., 2017). Implementing managed aquifer recharge away from the river’s floodplain has the potential to enhance groundwater supply which is increasingly relied upon during surface water shortage (Green et al., 2011), by storing water in available aquifer space in the deep aquifer. At the basin scale, managed aquifer recharge may lead to regional groundwater sustainability.
The physical limitations to implementing managed aquifer recharge in the Carson River Basin hinges on three key factors. The first factor relates to the physical connectivity between rivers and streams, and the irrigation delivery network of canals and ditches that divert water to agricultural lands (Niswonger et al., 2017). In the Carson River Basin the mechanisms for getting water to fields is already in place. Thus, intentionally routing high flows that occur in wet years through this system during the nonirrigation season would mimic what occurs naturally during the irrigation season. The second factor relates to the occurrence of atmospheric river storm events that deliver large amounts of precipitation to the region, much greater than average (Dettinger et al., 2015). With increased frequency and intensity projected under a warmer climate, such events have the potential to produce excess water over short periods of time that could be stored through mechanisms such as managed aquifer recharge (Niswonger et al., 2017). The third factor relates to the change in snowpack accumulation and shifts in snowmelt timing observed elsewhere in the Sierra Nevada (e.g., Godsey et al., 2014; Mote et al., 2018). Having a mechanism in place to maximize use of earlier snowmelt and shifts in streamflow timing could be advantageous and enhance regional groundwater sustainability. As part of the Water for the Seasons study, a hypothetical scenario was developed to determine the feasibility of managed aquifer recharge in the Carson River Basin, assuming no legal constraints. During “wet” or above-average water years, irrigators in the Upper Carson Valley would divert high flows and spread water over agricultural lands during the nonirrigation season. Assuming flows are abundant and “early,” diversions would begin prior to the growing season, when water would otherwise flow downstream to the Lahontan Reservoir. During “dry” years or drought periods, when surface water availability is less, irrigators in the Upper Carson Valley could augment surface water shortages with groundwater, allowing available surface water flows to flow downstream. Researchers hypothesize the amount of water has the potential to boost baseflow to support environmental instream flows, for example.
The hypothetical managed aquifer recharge scenario was presented to water managers in a workshop setting. Presentations included an overview of the hydrologic and operations modeling tools used to evaluate managed aquifer recharge by simulating the timing and distribution of water in the upper watershed. Specifically, in the Upper Carson Valley, a hydrologic model (GSFLOW) simulates streamflow driven by snowmelt, and surface and groundwater interactions, while a river basin operations model (MODSIM) allocates water according to the prior appropriation doctrine in the basin (see Figure 1) (Morway, Niswonger, & Triana, 2016; Niswonger et al., 2017). Integrating these two modeling tools advances the evaluation of climate impacts on water availability in agricultural communities and the resulting impacts of alternative management strategies (Morway et al., 2016).
When asked about the viability of managed aquifer recharge, the perspectives of 11 managers varied (Figure 3). Regardless of rating, all managers questioned, “How would thisreally work?” Several managers questioned whether models could simulate the connectivity between surface and groundwater to accurately quantify changes to instream flow. Others raised concerns that managed aquifer recharge violates the Alpine Decree and Nevada Water Law. Still others requested researchers consider alternatives that could work within the confines of current (2019) water law.
Managers posed specific questions that should be considered when evaluating the potential for managed aquifer recharge. For example:
Managers’ perspectives help to validate the on-the-ground potential of particular strategies and further refine alternative management scenarios. For example, understanding that managers are concerned with oversaturated fields helps researchers to define conditions in the model, such as what defines a wet versus “too” wet type of year and where to focus irrigation for managed aquifer recharge. Incorporating these nuances provides more accurate quantification of the potential benefits and consequences for users across the basin. Modeling is underway to simulate managed aquifer recharge scenarios and explore basin-wide implications. Researchers and local water managers will convene to collaboratively review results and further assess whether this or other strategies could work under the confines of existing water law. Subsequent fact sheets will present these findings.
","language":"English","publisher":"University of Nevada, Reno Extension","usgsCitation":"Sterle, K., Kitlasten, W., Morway, E.D., Niswonger, R.G., and Singletary, L., 2019, Managed aquifer recharge in snow-fed river basins: What, why and how?: Fact Sheet 19-10, 8 p.","productDescription":"8 p.","ipdsId":"IP-106943","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":377948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377947,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://extension.unr.edu/publication.aspx?PubID=3416"}],"country":"United States","state":"Nevada","city":"Carson City","otherGeospatial":"Carson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.74685668945312,\n 38.849333913235476\n ],\n [\n -119.67681884765624,\n 38.976492485539396\n ],\n [\n -119.64248657226562,\n 39.15881700964971\n ],\n [\n -119.06295776367188,\n 39.299236474818194\n ],\n [\n -118.96545410156251,\n 39.454221498848895\n ],\n [\n -118.70590209960938,\n 39.459523110465156\n ],\n [\n -118.61114501953125,\n 39.68288289049806\n ],\n [\n -118.62213134765626,\n 39.79059962227577\n ],\n [\n -118.73886108398438,\n 39.79059962227577\n ],\n [\n -118.8336181640625,\n 39.53899882354987\n ],\n [\n -119.1412353515625,\n 39.527348072681455\n ],\n [\n -119.32662963867188,\n 39.35659979720227\n ],\n [\n -119.53262329101562,\n 39.34598050985849\n ],\n [\n -119.77157592773436,\n 39.196076813671695\n ],\n [\n -119.88418579101561,\n 39.03838632847035\n ],\n [\n -119.86358642578125,\n 38.935911987561624\n ],\n [\n -119.74685668945312,\n 38.849333913235476\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sterle, Kelley","contributorId":195683,"corporation":false,"usgs":false,"family":"Sterle","given":"Kelley","email":"","affiliations":[],"preferred":false,"id":797450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kitlasten, Wesley 0000-0002-2049-9107","orcid":"https://orcid.org/0000-0002-2049-9107","contributorId":217832,"corporation":false,"usgs":true,"family":"Kitlasten","given":"Wesley","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":767634,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":767635,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singletary, Loretta","contributorId":195685,"corporation":false,"usgs":false,"family":"Singletary","given":"Loretta","email":"","affiliations":[],"preferred":false,"id":797451,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204640,"text":"70204640 - 2019 - Identifying characteristics of actionable science for drought planning and adaptation: Final report to the North Central Climate Adaptation Science Center","interactions":[],"lastModifiedDate":"2020-06-08T16:09:16.903228","indexId":"70204640","displayToPublicDate":"2019-12-31T11:02:49","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Identifying characteristics of actionable science for drought planning and adaptation: Final report to the North Central Climate Adaptation Science Center","docAbstract":"Changing climate conditions can make water management planning and drought preparedness decisions more complicated than ever before. Resource managers can no longer rely solely on historical data and trends to base their actions, and are in need of science that is relevant to their specific needs and can directly inform important planning decisions. Questions remain, however, regarding the most effective and efficient methods for extending scientific knowledge and products into management and decision-making.
This study analyzed two unique cases of water management to better understand how science can be translated into resource management actions and decision-making. In particular, this project sought to understand 1) the characteristics that make science actionable and useful for water resource management and drought preparedness, and 2) the ideal types of scientific knowledge or science products that facilitate the use of science in management and decision-making.
The first case study focused on beaver mimicry, an emerging nature-based solution that increases the presence of wood and woody debris in rivers and streams to mimic the actions of beavers. This technique has been rapidly adopted by natural resource managers as a way to restore riparian areas, increase groundwater infiltration, and slow surface water flow so that more water is available later in the year during hotter and dryer months. The second case study focused on an established research program, Colorado Dust on Snow, that provides water managers with scientific information explaining how the movement of dust particles from the Colorado Plateau influences hydrology and the timing and intensity of snow melt and water runoff into critical water sources. This program has support from and is being used by several water conservation districts in the state.
Understanding how scientific knowledge translates into action and decision-making in these cases is expected to strengthen our knowledge of actionable science in the context of drought and its impacts on ecosystems. The project team gathered qualitative data through stakeholder interviews and will conduct an extensive literature review. Findings from these efforts will also be incorporated into a broader Intermountain West synthesis effort to determine and assess commonalities and differences among socio-ecological aspects of drought adaptation and planning.
The destructive and deadly nature of debris flows has motivated research into empirical rainfall thresholds to provide situational awareness, inform early warning systems, and reduce loss of life and property. Disturbances such as wildfire and land-cover change can influence the hydrological processes of infiltration and runoff generation; in steep terrain this typically lowers empirical thresholds for debris-flow initiation. However, disturbance impacts, and the post-disturbance recovery may differ, depending on the severity, nature, extent, and duration of the disturbance, as well as on the prevailing hydroclimatic conditions. Thus, it can be difficult to predict impacts on debris-flows hazards in regions where historically such disturbances have been less frequent or severe. Given the increasing magnitude and incidence of wildfires, among other disturbances, we seek to develop a conceptual framework for assessing their impacts on debris-flow hazards across geographic regions. We characterize the severity of disturbances in terms of changes from undisturbed hydrologic functioning, including hillslope drainage and available unsaturated storage capacity, which can have contrasting influences on debris-flow initiation mechanisms in different hydroclimatic settings. We compare the timescale of disturbance-recovery cycles relative to the return period of threshold exceeding storms to describe vulnerability to post-disturbance debris flows. Similarly, we quantify resilience by comparing the timescales of disturbance-recovery cycles with those of disturbance-recurrence intervals. We illustrate the utility of these concepts using information from U.S. Geological Survey landslide monitoring sites in burned and unburned areas across the United States. Increasing severity of disturbance may influence both recovery timescales and lower the return period for debris-flow inducing storms, thus increasing the vulnerability to disturbance-related hazards while also decreasing system resilience. The proposed conceptual framework can inform future data acquisition and model development to improve debris-flow initiation thresholds in areas experiencing increasingly frequent, severe, and even overlapping landscape disturbances.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Debris-flow hazards mitigation: Mechanics, monitoring, modeling, and assessment; proceedings of the Seventh International Conference on Debris-Flow Hazards Mitigation","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Seventh International Conference on Debris-Flow Hazards Mitigation","conferenceDate":"Jun 10-13, 2019","conferenceLocation":"Golden, CO","language":"English","publisher":"Association of Environmental and Engineering Geologists","doi":"10.25676/11124/173176","usgsCitation":"Mirus, B.B., Staley, D.M., Kean, J.W., Smith, J.B., Wooten, R., McGuire, L.A., and Ebel, B., 2019, Conceptual framework for assessing disturbance impacts on debris-flow initiation thresholds across hydroclimatic settings, in Debris-flow hazards mitigation: Mechanics, monitoring, modeling, and assessment; proceedings of the Seventh International Conference on Debris-Flow Hazards Mitigation, Golden, CO, Jun 10-13, 2019, 8 p., https://doi.org/10.25676/11124/173176.","productDescription":"8 p.","ipdsId":"IP-105027","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":393369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":829098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":829099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":829100,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":829101,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wooten, Rick","contributorId":217741,"corporation":false,"usgs":false,"family":"Wooten","given":"Rick","email":"","affiliations":[{"id":24614,"text":"North Carolina Geological Survey","active":true,"usgs":false}],"preferred":false,"id":829102,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":829103,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":829104,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203456,"text":"sir20195001 - 2019 - Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980–2014","interactions":[],"lastModifiedDate":"2022-04-22T21:11:02.782667","indexId":"sir20195001","displayToPublicDate":"2019-12-30T07:30:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5001","displayTitle":"Severity and Extent of Alterations to Natural Streamflow Regimes Based on Hydrologic Metrics in the Conterminous United States, 1980-2014","title":"Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980–2014","docAbstract":"Alteration of the natural streamflow regime by land and water management, such as land-cover change and dams, is associated with aquatic ecosystem degradation. The severity and geographic extent of streamflow alteration at regional and national scales, however, remain largely unquantified. The primary goal of this study is to characterize the severity and extent of alterations to natural streamflow regimes for 1980–2014 based on hydrologic metrics at 3,355 U.S. Geological Survey streamgages in the conterminous United States. Twelve hydrologic metrics with known relevance to aquatic ecosystem health were used to characterize the streamflow regime. Alterations to the 12 hydrologic metrics were quantified by taking ratios of the metrics calculated from observed daily streamflow records divided by the same metrics predicted for natural conditions by random forest statistical models. Some level of streamflow alteration (diminishment or inflation of hydrologic metrics) compared to natural conditions was indicated at about 80 percent of the assessed streamgages across the conterminous United States. The severity of alteration differed among ecoregions because of differences in dominant land and water management practices. Finally, when compared over the period 1980–2014, climate variability generally played a minor role in the alteration of streamflows across the United States when compared to the effects of land and water management.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195001","usgsCitation":"Eng, K., Carlisle, D.M., Grantham, T.E., Wolock, D.M., and Eng, R.L., 2019, Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980–2014: U.S. Geological Survey Scientific Investigations Report 2019–5001, 25 p., https://doi.org/10.3133/sir20195001.","productDescription":"Report: iv, 25 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099228","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":370492,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/cir1461","text":"Circular 1461","linkHelpText":"- Flow Modification in the Nation's Streams and Rivers"},{"id":363900,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5001/sir20195001.pdf","text":"Report","size":"3.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5001"},{"id":363899,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5001/coverthb.jpg"},{"id":363901,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ULGVLI","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrologic Metric Changes Across the Conterminous United States"},{"id":399534,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109569.htm"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.55859375,\n 25.48295117535531\n ],\n [\n -97.20703125,\n 27.371767300523047\n ],\n [\n -95.185546875,\n 29.075375179558346\n ],\n [\n -92.8125,\n 29.53522956294847\n ],\n [\n -90.52734374999999,\n 28.613459424004414\n ],\n [\n -88.06640625,\n 28.92163128242129\n ],\n [\n -88.857421875,\n 29.916852233070173\n ],\n [\n -85.166015625,\n 28.92163128242129\n ],\n [\n -83.84765625,\n 29.6880527498568\n ],\n [\n -83.056640625,\n 27.293689224852407\n ],\n [\n -80.771484375,\n 24.84656534821976\n ],\n [\n -79.27734374999999,\n 27.137368359795584\n ],\n [\n -80.68359375,\n 30.06909396443887\n ],\n [\n -80.68359375,\n 31.42866311735861\n ],\n [\n -75.498046875,\n 34.95799531086792\n ],\n [\n -75.5859375,\n 37.3002752813443\n ],\n [\n -73.388671875,\n 40.38002840251183\n ],\n [\n -69.60937499999999,\n 41.50857729743935\n ],\n [\n -70.6640625,\n 42.8115217450979\n ],\n [\n -66.70898437499999,\n 44.77793589631623\n ],\n [\n -67.67578124999999,\n 47.100044694025215\n ],\n [\n -69.08203125,\n 47.27922900257082\n ],\n [\n -71.3671875,\n 45.089035564831036\n ],\n [\n -74.8828125,\n 44.902577996288876\n ],\n [\n -79.27734374999999,\n 43.004647127794435\n ],\n [\n -82.529296875,\n 41.83682786072714\n ],\n [\n -82.177734375,\n 43.197167282501276\n ],\n [\n -82.96875,\n 45.336701909968134\n ],\n [\n -84.55078125,\n 46.558860303117164\n ],\n [\n -89.033203125,\n 47.989921667414194\n ],\n [\n -95.00976562499999,\n 49.210420445650286\n ],\n [\n -121.640625,\n 48.922499263758255\n ],\n [\n -124.62890625,\n 49.03786794532644\n ],\n [\n -124.62890625,\n 44.02442151965934\n ],\n [\n -124.18945312500001,\n 38.54816542304656\n ],\n [\n -121.728515625,\n 35.10193405724606\n ],\n [\n -117.68554687499999,\n 32.39851580247402\n ],\n [\n -115.04882812499999,\n 31.952162238024975\n ],\n [\n -106.69921875,\n 31.353636941500987\n ],\n [\n -104.4140625,\n 28.76765910569123\n ],\n [\n -102.216796875,\n 29.22889003019423\n ],\n [\n -97.55859375,\n 25.48295117535531\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Analysis and Prediction Branch
Integrated Modeling and Prediction Division
Water Resources Mission Area
U.S. Geological Survey, Mail Stop 415
12201 Sunrise Valley Drive
Reston, VA 20192
Digital flood-inundation maps for an 8.8-mile reach of the North Platte River, from 1.5 miles upstream from the Highway 92 bridge to 3 miles downstream from the Highway 71 bridge in Scottsbluff County, were created by the U.S. Geological Survey (USGS) in cooperation with the Cities of Scottsbluff and Gering, Nebraska. The flood-inundation maps, which can be accessed through the Flood Inundation Mapping (FIM) Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program?qt-science_center_objects=0#qt-science_center_objects, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the North Platte River at Scottsbluff, Nebr. (station number 06680500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service (site SBRN1) at https://water.weather.gov/ahps2/hydrograph.php?wfo=cys&gage=sbrn1.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated by using the current (2018) stage-discharge relation at the North Platte River at Scottsbluff, Nebr., streamgage.
The hydraulic model was then used to compute 10 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 9 ft, or near bankfull, to 18 ft, which exceeds the stage that corresponds to the estimated 1-percent annual exceedance probability flood (100-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging data having a 0.6-ft root mean square error and 2-ft horizontal resolution resampled to a 6-ft grid to delineate the area flooded at each water level. The availability of these maps, along with internet information regarding current stage from the USGS streamgage, may provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195099","collaboration":"Prepared in cooperation with the City of Scottsbluff and the City of Gering","usgsCitation":"Strauch, K.R., 2019, Flood-inundation maps for the North Platte River at Scottsbluff and Gering, Nebraska, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5099, 9 p., https://doi.org/10.3133/sir20195099.","productDescription":"Report: vi, 9 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-102434","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":399544,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109564.htm"},{"id":370451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5099/sir20195099.pdf","text":"Report","size":"25.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5099"},{"id":370452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NCAIKN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-inundation geospatial datasets for the North Platte River at Scottsbluff and Gering, Nebraska"},{"id":370450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5099/coverthb.jpg"}],"country":"United States","state":"Nebraska","city":"Scottsbluff, Gering","otherGeospatial":"North Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.05426025390625,\n 41.74467659677642\n ],\n [\n -103.33740234375,\n 41.74467659677642\n ],\n [\n -103.33740234375,\n 42.05948945192712\n ],\n [\n -104.05426025390625,\n 42.05948945192712\n ],\n [\n -104.05426025390625,\n 41.74467659677642\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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