In 2014, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense’s Strategic Environmental Research and Development Program, initiated a project to evaluate the potential impacts of projected climate-change on Department of Defense installations that rely on Guam’s water resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model for the Fena Valley Reservoir. The southern Guam watershed model provides a physically based tool to estimate surface-water availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was used to construct the watershed model. The PRMS-IV code simulates different parts of the hydrologic cycle based on a set of user-defined modules. The southern Guam watershed model was constructed by updating a watershed model for the Fena Valley watersheds, and expanding the modeled area to include all of southern Guam. The Fena Valley watershed model was combined with a previously developed, but recently updated and recalibrated Fena Valley Reservoir water-balance model.
Two important surface-water resources for the U.S. Navy and the citizens of Guam were modeled in this study; the extended model now includes the Ugum River watershed and improves upon the previous model of the Fena Valley watersheds. Surface water from the Ugum River watershed is diverted and treated for drinking water, and the Fena Valley watersheds feed the largest surface-water reservoir on Guam. The southern Guam watershed model performed “very good,” according to the criteria of Moriasi and others (2007), in the Ugum River watershed above Talofofo Falls with monthly Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 1.0 represents perfect model fit). In the Fena Valley watershed, monthly simulated streamflow volumes from the watershed model compared reasonably well with the measured values for the gaging stations on the Almagosa, Maulap, and Imong Rivers—tributaries to the Fena Valley Reservoir—with Nash-Sutcliffe efficiency values of 0.87 or higher. The southern Guam watershed model simulated the total volume of the critical dry season (January to May) streamflow for the entire simulation period within –0.54 percent at the Almagosa River, within 6.39 percent at the Maulap River, and within 6.06 percent at the Imong River.
The recalibrated water-balance model of the Fena Valley Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume ranged from 6.04 percent (284.6 acre-feet or 92.7 million gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million gallons). Monthly simulation bias ranged from –0.48 percent for the calibration period to 0.87 percent for the verification period; relative error ranged from –0.60 to 0.88 percent for the calibration and verification periods, respectively. The small bias indicated that the model did not consistently overestimate or underestimate reservoir storage volume.
In the entirety of southern Guam, the watershed model has a “satisfactory” to “very good” rating when simulating monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam watershed model uses a more sophisticated climate-distribution scheme than the older model to make use of the sparse climate data, as well as includes updated land-cover parameters and the capability to simulate closed depression areas.
The new Fena Valley Reservoir water-balance model is useful as an updated tool to forecast short-term changes in the surface-water resources of Guam. Furthermore, the now spatially complete southern Guam watershed model can be used to evaluate changes in streamflow and recharge owing to climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 2017; DOI:10.5066/F7HH6HV4).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175093","collaboration":"Prepared in cooperation with the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP)","usgsCitation":"Rosa, S.N., and Hay, L.E., 2019, Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2017–5093, 64 p., https://doi.org/10.3133/sir20175093.","productDescription":"Report: viii, 64 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-081743","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":349631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5093/coverthb2.jpg"},{"id":349632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5093/sir20175093.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5093 v1.1"},{"id":361066,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5093/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017-5093 Version History"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.6240234375,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.230587802102518\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 2017; Version 1.1: February 2019","contact":"Director,
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Salinity has a major effect on water users in the Colorado River Basin, estimated to cause almost $300 million per year in economic damages. The Colorado River Basin Salinity Control Program implements and manages projects to reduce salinity loads, investing millions of dollars per year in irrigation upgrades, canal projects, and other mitigation strategies. To inform and improve mitigation efforts, there is a need to better understand sources of salinity to streams and how salinity has changed over time. This study explores salinity in the baseflow fraction of streamflow, assessing whether groundwater is a significant contributor of dissolved solids to streams in the Upper Colorado River Basin (UCRB). Chemical hydrograph separation was used to estimate baseflow discharge and baseflow dissolved solids loads at stream gages (n = 69) across the UCRB. On average, it is estimated that 89% of dissolved solids loads originate from the baseflow fraction of streamflow, indicating that subsurface transport processes play a dominant role in delivering dissolved solids to streams in the UCRB. A statistical trend analysis using weighted regressions on time, discharge, and season was used to evaluate changes in baseflow dissolved solids loads in streams (n = 27) from 1986 to 2011. Decreasing trends in baseflow dissolved solids loads were observed at 63% of streams. At the three most downstream sites, Green River at Green River, UT, Colorado River at Cisco, UT, and the San Juan River near Bluff, UT, baseflow dissolved solids loads decreased by a combined 823,000 metric tons (mT), which is approximately 69% of projected basin‐scale decreases in total dissolved solids loads as a result of salinity control efforts. Decreasing trends in baseflow dissolved solids loads suggest that salinity mitigation projects, landscape changes, and/or climate are reducing dissolved solids transported to streams through the subsurface. Notably, the pace and extent of decreases in baseflow dissolved solids loads declined during the most recent decade; average decreasing loads during the 2000s (28,200 mT) were only 54% of average decreasing loads in the 1990s (51,700 mT).
Transport and fate of dissolved organic carbon (DOC) in rivers are important aspects of the carbon cycle and the critical linkage between terrestrial, aquatic, and marine systems. Recent studies have quantified fluvial export to the marine environment in many systems, but in-stream losses of DOC are poorly constrained. This study compares DOC yields (kg C/ha) between the area-weighted averages of several tributaries within larger watersheds with the DOC yields of the larger watersheds to gain insight on in-stream losses in larger river systems. Four large watersheds, 22 tributaries to those watersheds, and 5 additional main stem locations in Maine were studied during 1 April to 15 November in 2011 through 2013. There were no significant differences in the area-weighted average DOC yield of the tributaries and the larger watersheds indicating little net in-stream loss in the main stems of the larger rivers. It is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries based on similarity in DOC yield longitudinally along the main stems of two of the rivers. In addition, wetland abundance, which is associated with higher DOC yield in this environment, did not consistently increase from tributaries to the larger watershed or longitudinally along the main stems. This geographic distribution of wetlands therefore also indicates that it is unlikely that inputs of DOC from un-gauged areas compensated for losses from gauged tributaries. These findings suggest that in-stream losses of DOC in these larger river systems are minimal and that the vast majority of DOC in major rivers in Maine is transported conservatively to the coastal ocean.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.03.076","usgsCitation":"Huntington, T.G., Roesler, C.S., and Aiken, G.R., 2019, Evidence for conservative transport of dissolved organic carbon in major river basins in the Gulf of Maine Watershed: Journal of Hydrology, v. 573, p. 755-767, https://doi.org/10.1016/j.jhydrol.2019.03.076.","productDescription":"13 p.","startPage":"755","endPage":"767","ipdsId":"IP-074338","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":468142,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2019.03.076","text":"Publisher Index Page"},{"id":349166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":" Gulf of Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.00,\n 47.5\n ],\n [\n -67.00,\n 47.5\n ],\n [\n -67.00,\n 44.00\n ],\n [\n -71.00,\n 44.00\n ],\n [\n -71.00,\n 47.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"573","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcb7e4b06e28e9c24168","contributors":{"authors":[{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roesler, Collin S.","contributorId":152025,"corporation":false,"usgs":false,"family":"Roesler","given":"Collin","email":"","middleInitial":"S.","affiliations":[{"id":18855,"text":"Department of Earth and Oceanographic Science, Bowdoin College, Brunswick, ME","active":true,"usgs":false}],"preferred":false,"id":717933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aiken, George R. 0000-0001-8454-0984 graiken@usgs.gov","orcid":"https://orcid.org/0000-0001-8454-0984","contributorId":1322,"corporation":false,"usgs":true,"family":"Aiken","given":"George","email":"graiken@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":717934,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259113,"text":"70259113 - 2018 - Late Neogene–Quaternary tephrochronology, stratigraphy, and paleoclimate of Death Valley, California, USA","interactions":[],"lastModifiedDate":"2024-09-27T12:09:03.422027","indexId":"70259113","displayToPublicDate":"2020-01-02T07:06:33","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Late Neogene–Quaternary tephrochronology, stratigraphy, and paleoclimate of Death Valley, California, USA","docAbstract":"Sedimentary deposits in midlatitude continental basins often preserve a paleoclimate record complementary to marine-based records. However, deriving that paleoclimate record depends on having well-exposed deposits and establishing a sufficiently robust geochronology. After decades of research, we have been able to correlate 77 tephra beds exposed in multiple stratigraphic sections in the Death Valley area, California, United States. These correlations identify 25 different tephra beds that erupted from at least five different volcanic centers from older than 3.58 Ma to ca. 32 ka. We have informally named and determined the ages for seven previously unrecognized beds: ca. 3.54 Ma tuff of Curry canyon, ca. 3.45 Ma tuff of Furnace Creek, ca. 3.1 Ma tuff of Kit Fox Hills, ca. 3.1 Ma tuff of Mesquite Flat, ca. 3.15 Ma tuff of Texas Spring, 3.117 ± 0.011 Ma tuff of Echo Canyon, and the ca. 1.3 Ma Amargosa ash bed. Several of these tephra beds are found as far northeast as central Utah and could be important marker beds in western North America.
Our tephrochronologic data, combined with magnetic polarity data and 40Ar/39Ar age determinations, redefine Neogene sedimentary deposits exposed across 175 km2 of the Death Valley area. The alluvial/lacustrine Furnace Creek Formation is a time-transgressive sedimentary sequence ranging from ca. 6.0 to 2.5 Ma in age. The ca. 2.5–1.7 Ma Funeral Formation is typically exposed as a proximal alluvial-fan facies overlying the Furnace Creek Formation. We have correlated deposits in the Kit Fox Hills, Salt Creek, Nova Basin, and southern Death Valley with the informally named ca. 1.3–0.5 Ma Mormon Point formation. In addition, our correlation of the late Pleistocene Wilson Creek ash bed 15 in the Lake Rogers deposits represents the first unambiguous sequences deposited during the Last Glacial Maximum (marine isotope stage [MIS] 2) in Death Valley.
Based on this new stratigraphic framework, we show that the Pliocene and Pleistocene climate in Death Valley is consistent with the well-established marine tropical/subtropical record. Pluvial lakes in Death Valley and Searles Valley began to form ca. 3.5–3.4 Ma in the late Pliocene during MIS MG5. Initiation of lakes in these two hydrologically separated valleys at the same time at the beginning of a cooling trend in the marine climate record suggests a link to a cooler, wetter (glacial) regional climate in North America. The Death Valley lake persisted until ca. 3.30 Ma, at the peak of the M2 glaciation, after which there is no evidence of Pliocene lacustrine deposition, even at the peak of the Northern Hemisphere Glaciation (ca. 2.75 Ma). If pluvial lakes in the Pliocene are an indirect record of glacial climate conditions, as they are for the Pleistocene, then a glacial climate was present in western North America for ∼200,000 yr during the Pliocene, encompassing MIS MG5–M2.
Pleistocene pluvial lakes in Death Valley that formed ca. 1.98–1.78 Ma, 1.3–1.0 Ma, and ca. 0.6 Ma (MIS 16) are consistent with other regional climate records that indicate a regional glacial climate; however, Death Valley was relatively dry at ca. 0.77 Ma (MIS 19), when large lakes existed in other basins. The limited extent of the MIS 2 marsh/shallow lake in the Lake Rogers basin of northern Death Valley reflects the well-known regional glacial climate at that time; however, Death Valley received relatively lower inflow and rainfall in comparison.
Accurate estimates of flood frequency and magnitude are a key component of any effective nationwide flood risk management and flood damage abatement program. In addition to accuracy, methods for estimating flood risk must be uniformly and consistently applied because management of the Nation’s water and related land resources is a collaborative effort involving multiple actors including most levels of government and the private sector.
Flood frequency guidelines have been published in the United States since 1967, and have undergone periodic revisions. In 1967, the U.S. Water Resources Council presented a coherent approach to flood frequency with Bulletin 15, “A Uniform Technique for Determining Flood Flow Frequencies.” The method it recommended involved fitting the log-Pearson Type III distribution to annual peak flow data by the method of moments.
The first extension and update of Bulletin 15 was published in 1976 as Bulletin 17, “Guidelines for Determining Flood Flow Frequency” (Guidelines). It extended the Bulletin 15 procedures by introducing methods for dealing with outliers, historical flood information, and regional skew. Bulletin 17A was published the following year to clarify the computation of weighted skew. The next revision of the Bulletin, the Bulletin 17B, provided a host of improvements and new techniques designed to address situations that often arise in practice, including better methods for estimating and using regional skew, weighting station and regional skew, detection of outliers, and use of the conditional probability adjustment.
The current version of these Guidelines are presented in this document, denoted Bulletin 17C. It incorporates changes motivated by four of the items listed as “Future Work” in Bulletin 17B and 30 years of post-17B research on flood processes and statistical methods. The updates include: adoption of a generalized representation of flood data that allows for interval and censored data types; a new method, called the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval data; a generalized approach to identification of low outliers in flood data; and an improved method for computing confidence intervals.
Federal agencies are requested to use these Guidelines in all planning activities involving water and related land resources. State, local, and private organizations are encouraged to use these Guidelines to assure uniformity in the flood frequency estimates that all agencies concerned with flood risk should use for Federal planning decisions.
This revision is adopted with the knowledge and understanding that review of these procedures will be ongoing. Updated methods will be adopted when warranted by experience and by examination and testing of new techniques.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface water in Book 4: Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4B5","isbn":"978-1-4113-4223-1","usgsCitation":"England, J.F., Jr., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Jr., Veilleux, A.G., Kiang, J.E., and Mason, R.R., Jr., 2018, Guidelines for determining flood flow frequency — Bulletin 17C (ver. 1.1, May 2019): U.S. Geological Survey Techniques and Methods, book 4, chap. B5, 148 p., https://doi.org/10.3133/tm4B5.","productDescription":"xiii, 148 p.","numberOfPages":"168","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-065340","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":352936,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://acwi.gov/hydrology/Frequency/b17c/","text":"Advisory Committee on Water Information - Bulletin 17C"},{"id":352416,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/b05/tm4b5.pdf","text":"Report","size":"29.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-B5"},{"id":352415,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/b05/coverthb2.jpg"},{"id":399694,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_107081.htm"},{"id":363942,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/04/b05/versionHist.pdf","size":"153 KB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.5178,\n 33.9703\n ],\n [\n -83.8928,\n 33.9703\n ],\n [\n -83.8928,\n 34.2625\n ],\n [\n -84.5178,\n 34.2625\n ],\n [\n -84.5178,\n 33.9703\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: May 31, 2019","publicComments":"This report is Chapter 5 of Section B: Surface water in Book 4: Hydrologic analysis and interpretation.","contact":"Chief, Analysis and Prediction Branch
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Mail Stop 415
Reston, VA 20192
Executive Summary
Drainage for agricultural production over the past 150 years has been an integral component of human-driven change to Minnesota’s rural landscapes.
Benefits of drainage
Historically, poorly drained soils across much of the State would often remain saturated or flooded after spring snowmelt, preventing timely farm operations such as tilling and planting crops (Arneman, 1963). Installation of agricultural drainage, both surface ditches and subsurface drainage, accelerated transport of water off farm fields and imparted producers higher crop yields (Beauchamp, 1987; Stoner and others, 1993). Agricultural drainage offered many other benefits such as preventing crop drown out, aerating the soil profile for improved plant growth, limiting surface runoff and soil erosion, and allowing farmers better access to croplands (Fausey and others, 1987). Without agricultural drainage on much of Minnesota’s croplands, it would have been difficult to realize high enough crop yields to remain economically viable.
Environmental concerns
While drainage of Minnesota croplands provided the benefits mentioned above, several environmental concerns result. These include wetland loss, degradation of downstream water quality, and reduced [potential for] recharge.
Early agricultural drainage efforts (pre-20th century) led to the disappearance of much of Minnesota’s natural wetlands. Increased focus on preventing or mitigating wetland loss over the last 50 years has helped curtail further losses, even as agricultural drainage proceeds. Prior to establishment of Minnesota statehood, wetlands accounted for more than 10 million acres in Minnesota, including prairie wetlands, peatlands, and forest wetlands that comprised approximately 19 percent of the total land area (Palmer, 1915; King, 1980). In 2018, only half of Minnesota’s pre-settlement wetlands remain, mostly in parts of the State that have not experienced widespread drainage, such as northern Minnesota.
Water-quality monitoring has shown that agricultural drainage, in particular the practice of subsurface drainage, provides a direct flow path for nutrient (nitrogen and soluble phosphorus) losses to surface water resources. The negative consequences of agricultural drainage on surface water quality are well documented (for example, Dinnes and others, 2002; Kladivko and others, 2004; Richards and others, 2008; Rozemeijer and others, 2010; Schottler and others, 2013). Agricultural basins with a high percentage of agricultural drainage have been implicated as part of the cause of the Gulf of Mexico hypoxia zone due to excessive nitrogen export (Goolsby and Battaglin, 2001; Randall and Mulla, 2001).
The connection of hydrological effects of agricultural subsurface drainage on groundwater recharge and aquifers, on the other hand, has not been well-established. Agricultural subsurface drainage intercepts infiltrating water below croplands and directly discharges the water to nearby surface waters. However, the size of the water balance shift from drained water that would have evapotranspired or run off the land to drained water that would recharge underlying aquifers has been poorly characterized (Schuh, 2008).
Drain Tiles and Groundwater
Given the poor accounting of subsurface drainage effects on groundwater resources, the Minnesota Ground Water Association (MGWA) deemed it imperative that we document these effects so that groundwater resources in agricultural regions with substantial drainage can be effectively managed. This white paper documents the relations of drain tiles and groundwater resources and discusses the historical significance of agricultural drainage practices, the recognized positive benefits and potential negative consequences of agricultural drainage practices, and the gaps in understanding of the connections between agricultural drainage and groundwater resources.
The major messages emerged from the findings of this white paper are:
High productivity temperate wetlands that accrete peat via belowground biomass (peatlands) may be managed for climate mitigation benefits due to their global distribution and notably negative emissions of atmospheric carbon dioxide (CO2) through rapid storage of carbon (C) in anoxic soils. Net emissions of additional greenhouse gases (GHG)—methane (CH4) and nitrous oxide (N2O)—are more difficult to predict and monitor due to fine-scale temporal and spatial variability, but can potentially reverse the climate mitigation benefits resulting from CO2 uptake. To support management decisions and modeling, we collected continuous 96 hour high frequency GHG flux data for CO2, CH4 and N2O at multiple scales—static chambers (1 Hz) and eddy covariance (10 Hz)—during peak productivity in a well-studied, impounded coastal peatland in California's Sacramento Delta with high annual rates of C fluxes, sequestering 2065 ± 150 g CO2 m−2 y−1 and emitting 64.5 ± 2.4 g CH4 m−2 y−1. Chambers (n = 6) showed strong spatial variability along a hydrologic gradient from inlet to interior plots. Daily (24 hour) net CO2 uptake (NEE) was highest near inlet locations and fell dramatically along the flowpath (−25 to −3.8 to +2.64 g CO2 m−2 d−1). In contrast, daily net CH4 flux increased along the flowpath (0.39 to 0.62 to 0.88 g CH4 m−2d−1), such that sites of high daily CO2 uptake were sites of low CH4 emission. Distributed, continuous chamber data exposed five novel insights, and at least two important datagaps for wetland GHG management, including: (1) increasing dominance of CH4 ebullition fluxes (15%–32% of total) along the flowpath and (2) net negative N2O flux across all sites as measured during a 4 day period of peak biomass (−1.7 mg N2O m−2 d−1; 0.51 g CO2 eq m−2 d−1). The net negative emissions of re-established peat-accreting wetlands are notably high, but may be poorly estimated by models that do not consider within-wetland spatial variability due to water flowpaths.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/aaae74","usgsCitation":"Windham-Myers, L., Bergamaschi, B.A., Anderson, F.A., Knox, S., Miller, R., and Fujii, R., 2018, Potential for negative emissions of greenhouse gases (CO2, CH4 and N2O) through coastal peatland re-establishment: Novel insights from high frequency flux data at meter and kilometer scales: Environmental Research Letters, v. 13, no. 4, p. 1-14, https://doi.org/10.1088/1748-9326/aaae74.","productDescription":"Article 045005; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-091738","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468152,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aaae74","text":"Publisher Index Page"},{"id":361209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","volume":"13","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Frank A. 0000-0002-1418-4678","orcid":"https://orcid.org/0000-0002-1418-4678","contributorId":203975,"corporation":false,"usgs":true,"family":"Anderson","given":"Frank","email":"","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knox, Sarah 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":167493,"corporation":false,"usgs":false,"family":"Knox","given":"Sarah","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":757072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Robin 0000-0002-1875-0390 romiller@usgs.gov","orcid":"https://orcid.org/0000-0002-1875-0390","contributorId":213190,"corporation":false,"usgs":true,"family":"Miller","given":"Robin","email":"romiller@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fujii, Roger 0000-0002-4616-7231 rfujii@usgs.gov","orcid":"https://orcid.org/0000-0002-4616-7231","contributorId":213191,"corporation":false,"usgs":true,"family":"Fujii","given":"Roger","email":"rfujii@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757074,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201996,"text":"70201996 - 2018 - Predicting geogenic arsenic in drinking water wells in glacial aquifers, north-central USA: Accounting for depth-dependent features","interactions":[],"lastModifiedDate":"2019-02-04T16:32:24","indexId":"70201996","displayToPublicDate":"2019-01-01T16:32:18","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Predicting geogenic arsenic in drinking water wells in glacial aquifers, north-central USA: Accounting for depth-dependent features","docAbstract":"Chronic exposure to arsenic (As) via drinking groundwater is a human health concern worldwide. Probabilities of elevated geogenic As concentrations in groundwater were predicted in complex, glacial aquifers in Minnesota, north‐central USA, a region that commonly has elevated As concentrations in well water. Maps of elevated As hazard were created for depths typical of drinking water supply and with well construction attributes common for domestic wells. Conventional variables describing aquifer properties and materials, position on the hydrologic landscape, and soil geochemistry were among the most influential for predicting the probability of elevated As. We also found that certain well construction attributes were influential in predicting As hazard. Smaller distances between the top of the well screen and overlying aquitard (proximity) and shorter well screen lengths were each associated with higher probabilities of elevated As. Influential predictor variables, which are either mapped across the region or are well construction attributes, are proxies in the model for measurable physical or geochemical causes of elevated As (e.g., redox condition, till or aquifer sediment chemistry, and water chemistry), which are not mapped across the region. Our setting shares some important characteristics with deltaic and other high‐As aquifers in Southeast Asia: late Quaternary age, complex layering of coarse‐ and fine‐grained materials, low‐As sediment concentrations, and geochemical controls on As mobilization. Translating three‐dimensional geologic and geochemical understanding of As mobility to quantifiable variables for modeling with powerful, flexible statistical tools could improve predictions and help identify safer groundwater supply options in the USA, Southeast Asia, and elsewhere.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023106","usgsCitation":"Erickson, M., Elliott, S.M., Christenson, C., and Krall, A.L., 2018, Predicting geogenic arsenic in drinking water wells in glacial aquifers, north-central USA: Accounting for depth-dependent features: Water Resources Research, v. 54, no. 12, p. 10172-10187, https://doi.org/10.1029/2018WR023106.","productDescription":"16 p.","startPage":"10172","endPage":"10187","ipdsId":"IP-090485","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":468158,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018wr023106","text":"Publisher Index Page"},{"id":437637,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77H1HH8","text":"USGS data release","linkHelpText":"Groundwater arsenic data and ASCII grids for predicting elevated arsenic in northwestern and central Minnesota using boosted regression tree methods"},{"id":360995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-96.0683,47.1542],[-95.1696,47.1496],[-95.1713,46.9788],[-95.1646,46.9789],[-95.1624,46.6306],[-95.1545,46.6303],[-95.1563,46.2828],[-95.1464,46.2825],[-95.147,45.9326],[-95.1391,45.9327],[-95.1384,45.5864],[-95.1319,45.5856],[-95.1322,45.4128],[-94.7635,45.414],[-94.7612,45.3272],[-94.3819,45.327],[-94.382,45.2836],[-94.2615,45.2844],[-94.2613,45.2976],[-94.2544,45.3001],[-94.2576,45.3058],[-94.2504,45.3086],[-94.1915,45.3193],[-94.1821,45.3154],[-94.1741,45.3012],[-94.161,45.3011],[-94.1373,45.3162],[-94.1046,45.3445],[-94.093,45.3487],[-94.0828,45.3702],[-94.0816,45.3862],[-94.063,45.4133],[-94.0463,45.4275],[-94.0227,45.4043],[-93.9909,45.4017],[-93.9557,45.378],[-93.946,45.3849],[-93.9194,45.3836],[-93.9115,45.3731],[-93.894,45.365],[-93.8815,45.3476],[-93.8342,45.3372],[-93.8062,45.3145],[-93.7614,45.2972],[-93.7322,45.3027],[-93.7134,45.2964],[-93.6843,45.3046],[-93.6726,45.2964],[-93.6065,45.2929],[-93.5728,45.2929],[-93.5683,45.2988],[-93.5547,45.2824],[-93.5534,45.2691],[-93.5404,45.2563],[-93.4847,45.2257],[-93.4252,45.2152],[-93.4142,45.2001],[-93.3579,45.1745],[-93.3399,45.1576],[-93.3024,45.1388],[-93.2915,45.1155],[-93.2786,45.1036],[-93.2814,45.0374],[-93.2059,45.0373],[-93.2058,44.9172],[-93.1955,44.904],[-93.1704,44.8975],[-93.2229,44.8315],[-93.2596,44.8096],[-93.2802,44.8078],[-93.3086,44.7942],[-93.3285,44.791],[-93.3261,44.7176],[-93.3203,44.7176],[-93.3193,44.6332],[-93.2782,44.6326],[-93.2798,44.546],[-93.5259,44.5466],[-93.5251,44.1983],[-93.768,44.1971],[-93.7679,43.85],[-94.3705,43.8501],[-94.3693,44.1098],[-95.1041,44.1089],[-95.0992,44.1964],[-95.5921,44.1964],[-95.5938,44.5434],[-95.3567,44.5437],[-95.3584,44.6993],[-95.3962,44.7165],[-95.3951,44.7248],[-95.4049,44.7315],[-95.4165,44.7322],[-95.4374,44.7438],[-95.4521,44.7404],[-95.4699,44.7543],[-95.4789,44.7532],[-95.4816,44.891],[-95.2471,44.8925],[-95.2456,45.1537],[-95.2471,45.2382],[-95.2549,45.2381],[-95.2549,45.4122],[-96.2412,45.4136],[-96.2425,45.5864],[-96.8363,45.5858],[-96.8528,45.6033],[-96.8484,45.6358],[-96.8303,45.6567],[-96.7797,45.6768],[-96.7315,45.7107],[-96.6674,45.735],[-96.6492,45.7522],[-96.6313,45.783],[-96.6021,45.8032],[-96.5818,45.826],[-96.5731,45.8515],[-96.5644,45.9291],[-96.5578,45.9462],[-96.569,45.9757],[-96.5772,46.0308],[-96.5552,46.0731],[-96.5688,46.1333],[-96.5785,46.1478],[-96.5763,46.1704],[-96.5883,46.1795],[-96.583,46.1928],[-96.5882,46.1944],[-96.5857,46.2085],[-96.5992,46.2302],[-96.5971,46.2432],[-96.585,46.2484],[-96.5949,46.2513],[-96.6031,46.2704],[-96.5968,46.2907],[-96.6042,46.2937],[-96.5983,46.3134],[-96.6022,46.3298],[-96.6279,46.351],[-96.646,46.3528],[-96.6453,46.3614],[-96.6682,46.3794],[-96.6683,46.3901],[-96.6806,46.4051],[-96.6955,46.4133],[-96.6945,46.4205],[-96.7037,46.4189],[-96.7011,46.4268],[-96.7059,46.4356],[-96.7184,46.4369],[-96.7194,46.4411],[-96.714,46.4416],[-96.7209,46.4474],[-96.7145,46.4613],[-96.7215,46.4729],[-96.7376,46.4801],[-96.7337,46.4925],[-96.7411,46.5021],[-96.7337,46.5046],[-96.7429,46.5198],[-96.7463,46.5339],[-96.7418,46.5374],[-96.7487,46.543],[-96.7438,46.5509],[-96.7504,46.5607],[-96.7459,46.5669],[-96.7517,46.5774],[-96.7563,46.5763],[-96.7519,46.581],[-96.7599,46.5831],[-96.7535,46.5873],[-96.762,46.5898],[-96.7585,46.5932],[-96.7639,46.5996],[-96.7745,46.5975],[-96.7717,46.6062],[-96.7763,46.6075],[-96.779,46.6207],[-96.7882,46.6201],[-96.7817,46.623],[-96.7891,46.6245],[-96.7859,46.6314],[-96.7954,46.6309],[-96.7896,46.6419],[-96.7967,46.6451],[-96.7936,46.6487],[-96.8017,46.6582],[-96.793,46.6764],[-96.7958,46.6791],[-96.7878,46.686],[-96.7917,46.6936],[-96.7863,46.7009],[-96.7905,46.7032],[-96.7823,46.7282],[-96.7857,46.7555],[-96.7814,46.7616],[-96.7899,46.7742],[-96.786,46.7757],[-96.7922,46.7776],[-96.7879,46.7839],[-96.7976,46.794],[-96.8012,46.8058],[-96.7974,46.809],[-96.8018,46.8125],[-96.7915,46.8248],[-96.7832,46.8258],[-96.793,46.8345],[-96.7755,46.8413],[-96.7766,46.8512],[-96.7844,46.854],[-96.7789,46.8585],[-96.784,46.8685],[-96.7792,46.8773],[-96.7667,46.8804],[-96.7746,46.8969],[-96.7625,46.9149],[-96.7565,46.9155],[-96.7615,46.9235],[-96.756,46.9249],[-96.7624,46.9283],[-96.7595,46.9356],[-96.781,46.9268],[-96.7918,46.9303],[-96.7912,46.9445],[-96.8035,46.9484],[-96.7987,46.9692],[-96.8062,46.9653],[-96.8183,46.9682],[-96.8213,47.0012],[-96.8383,47.0095],[-96.8282,47.0238],[-96.8184,47.0282],[-96.8257,47.0325],[-96.8119,47.0382],[-96.8269,47.0481],[-96.8207,47.055],[-96.8261,47.0605],[-96.8206,47.0669],[-96.8234,47.0742],[-96.8285,47.0741],[-96.8177,47.0872],[-96.8265,47.0895],[-96.8169,47.1062],[-96.8249,47.1184],[-96.8354,47.1217],[-96.823,47.1289],[-96.8364,47.1519],[-96.0683,47.1542]]]},\"properties\":{\"name\":\"Becker\",\"state\":\"MN\"}}]}","volume":"54","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Melinda L. 0000-0002-1117-2866 merickso@usgs.gov","orcid":"https://orcid.org/0000-0002-1117-2866","contributorId":3671,"corporation":false,"usgs":true,"family":"Erickson","given":"Melinda L.","email":"merickso@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christenson, Catherine 0000-0001-5944-2186 cchristenson@usgs.gov","orcid":"https://orcid.org/0000-0001-5944-2186","contributorId":200263,"corporation":false,"usgs":true,"family":"Christenson","given":"Catherine","email":"cchristenson@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756558,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krall, Aliesha L. 0000-0003-2521-5043 adiekoff@usgs.gov","orcid":"https://orcid.org/0000-0003-2521-5043","contributorId":176545,"corporation":false,"usgs":true,"family":"Krall","given":"Aliesha","email":"adiekoff@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":756557,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202028,"text":"70202028 - 2018 - Groundwater modeling","interactions":[],"lastModifiedDate":"2019-02-07T10:45:21","indexId":"70202028","displayToPublicDate":"2019-01-01T10:45:06","publicationYear":"2018","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Groundwater modeling","docAbstract":"The state of the science and practice in groundwater modeling brings to mind highly sophisticated computer models that are running in parallel on many multi-processor machines. These models are expected to incorporate many different processes of both saturated and unsaturated groundwater flow and transport and possibly the media to which it connects, like surface waters and the atmosphere. We are increasingly aware we cannot study groundwater flow in isolation if we are to make useful predictions of, for instance, the impacts of climate change on the groundwater regime. We have come a long way.
Today we are no longer limited to equations for flow toward a well, perhaps near an infinitely long straight canal (method of images), to sandbox models in the laboratory, or to simple steady state models of flow in a single aquifer. We now have computer models that solve groundwater flow and transport in multi-aquifer settings under transient conditions and with a user-friendly graphical user interface that allows widespread use. Additionally, multi-media models are now leaving the research environment and becoming available to mainstream consultants. So in that sense the science of groundwater modeling has matured.
The practice of groundwater modeling, however, has also matured. We have come to realize that model output, being a necessary simplification of an unknowably complex natural world, has inherent limitations. That is, a model of reality is not reality itself. There is uncertainty associated with all facets of our model—parameterization, aquifer geometry and discretization, boundary conditions, and future hydrologic drivers such as future pumping regimes and climates. Today a model is now more appropriately seen as a tool that provides a quantitative framework to make supportable forecasts rather than an oracle that gives us all the answers.
In this chapter we set out to briefly review the state of the science and practice in modeling. In doing so, we augment existing assessments from the journal Groundwater (e.g., Hunt and Zheng 2012; Langevin and Panday 2012; Molz 2017a,b; White 2017), specifically in terms of modeling approach. An effective modeling approach is critical. If a modeler does not decompose the societal problem correctly, the model will not be fit-for-purpose, no matter how sophisticated the code’s capabilities. Moreover, capabilities of codes will be ever improving; good modeling practices have a timelessness that is more robust.
How best to decompose the problem and provide models that are accepted? We lay out here some approaches for today’s applied groundwater modeling. Specifically, we suggest: (1) a step-wise modeling process; (2) including a two-dimensional areal model within this process; (3) keeping abreast of industry standards; and (4) ways to increase acceptance of the models we produce.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Groundwater: State of the science and practice","language":"English","publisher":"National Groundwater Association","isbn":"1-56034-047-9","usgsCitation":"Haitjema, H.M., and Hunt, R., 2018, Groundwater modeling, chap. of Groundwater: State of the science and practice, p. 41-46.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-101055","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":361067,"type":{"id":15,"text":"Index Page"},"url":"https://groundwatersolutionsgroup.com/wp-content/uploads/2018/12/Science-and-Practice_10.17_FINAL.pdf#page=45"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haitjema, Henk M.","contributorId":74678,"corporation":false,"usgs":true,"family":"Haitjema","given":"Henk","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":756765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":208800,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[],"preferred":true,"id":756764,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198585,"text":"70198585 - 2018 - Streams do work: Measuring the work of low-order streams on the landscape using point clouds","interactions":[],"lastModifiedDate":"2019-06-26T14:56:53","indexId":"70198585","displayToPublicDate":"2018-12-31T14:42:02","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Streams do work: Measuring the work of low-order streams on the landscape using point clouds","docAbstract":"The mutable nature of low-order streams makes regular updating of surface water maps necessary for accurate representation. Low-order streams make up roughly half the streams in the conterminous United States by length, and small inaccuracies in stream head location can result in significant error in stream reach, order, and density. Reliable maps of stream features are vital for hydrologic modeling, ecosystem research, and boundary monitoring. High resolution digital elevation models derived from lidar data have shown promise in low order stream modeling yet forested high relief landscapes and low relief agricultural areas remain challenging. Here we present early results from research analyzing lidar point clouds to identify features and patterns that may be used in low-order stream identification and classification in challenging geographic conditions. This work has identified characteristics derived from point clouds that correlate with the presence of streams and stream heads and show promise for mapping small streams. In low topographic relief agricultural areas, cross sections collected at regular intervals along drainage channels extracted as 3D lines show a significant jump in value and variance of profile curvature standard deviation at stream heads. In high relief areas, observations show potential for stream mapping by identifying trends in riparian zone structure. Lidar return point density from riparian vegetation under 30 feet tall dips in the vicinity of intermittent stream heads. Also seen is an increase in point density above 60 feet downstream of stream heads. The trends found here likely reflect a change in vegetation structure relative to the presence of streams.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”","conferenceDate":"1-5 October 2018","conferenceLocation":"Delft, the Netherlands","language":"English","publisher":"ISPRS","doi":"10.5194/isprs-archives-XLII-4-573-2018","usgsCitation":"Shavers, E.J., and Stanislawski, L.V., 2018, Streams do work: Measuring the work of low-order streams on the landscape using point clouds, in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v. 42, no. 4, Delft, the Netherlands, 1-5 October 2018, p. 573-578, https://doi.org/10.5194/isprs-archives-XLII-4-573-2018.","productDescription":"6 p.","startPage":"573","endPage":"578","ipdsId":"IP-099680","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":468169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xlii-4-573-2018","text":"Publisher Index Page"},{"id":365087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, North Carolina","volume":"42","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":742040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanislawski, Larry V. 0000-0002-9437-0576 lstan@usgs.gov","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":3386,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","email":"lstan@usgs.gov","middleInitial":"V.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":742041,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217691,"text":"70217691 - 2018 - Multi-scale geophysical mapping of deep permafrost change after disturbance in interior Alaska, USA","interactions":[],"lastModifiedDate":"2021-02-09T12:34:12.316556","indexId":"70217691","displayToPublicDate":"2018-12-31T11:52:17","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Multi-scale geophysical mapping of deep permafrost change after disturbance in interior Alaska, USA","docAbstract":"Disturbance related to fire or hydrologic processes can cause degradation of deep (greater than 1 m) permafrost. These changes in deep permafrost have the potential to impact landscapes and infrastructure, alter the routing and distribution of surface water or groundwater, and may contribute to the flux of carbon to terrestrial and aquatic ecosystems. However, characterization of deep permafrost over large areas and with high spatial resolution is not possible with traditional remote sensing or surface observations. We make use of multiple ground-based and airborne geophysical methods, as well as numerical simulations, to better understand the distribution of permafrost and how it has changed after disturbance. Together, these geophysical datasets help to fill a critical gap in understanding permafrost landscapes and their response to disturbance.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"5th European conference on permafrost, book of abstracts","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"5th European Conference on Permafrost","conferenceDate":"June 23-July 1, 2018","conferenceLocation":"Chamonix, France","language":"English","publisher":"Laboratoire EDYTEM","usgsCitation":"Minsley, B.J., Bloss, B.R., Ebel, B., Rey, D.M., Walvoord, M.A., Brown, D., Daanen, R., Emond, A.M., Kass, M., Pastick, N.J., and Wylie, B., 2018, Multi-scale geophysical mapping of deep permafrost change after disturbance in interior Alaska, USA, in 5th European conference on permafrost, book of abstracts, v. 2, Chamonix, France, June 23-July 1, 2018, p. 896-897.","productDescription":"2 p.","startPage":"896","endPage":"897","ipdsId":"IP-093541","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":383105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383104,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://hal.archives-ouvertes.fr/hal-01816115/"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.65429687499997,\n 61.10078883158897\n ],\n [\n -141.1083984375,\n 61.10078883158897\n ],\n [\n -141.1083984375,\n 66.99025646736109\n ],\n [\n -155.65429687499997,\n 66.99025646736109\n ],\n [\n -155.65429687499997,\n 61.10078883158897\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bloss, Benjamin R. 0000-0002-1678-8571 bbloss@usgs.gov","orcid":"https://orcid.org/0000-0002-1678-8571","contributorId":139981,"corporation":false,"usgs":true,"family":"Bloss","given":"Benjamin","email":"bbloss@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":809267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David Matthew 0000-0002-9737-7239","orcid":"https://orcid.org/0000-0002-9737-7239","contributorId":248499,"corporation":false,"usgs":true,"family":"Rey","given":"David","email":"","middleInitial":"Matthew","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":809268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":809269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Dana R.N.","contributorId":187502,"corporation":false,"usgs":false,"family":"Brown","given":"Dana R.N.","affiliations":[],"preferred":false,"id":809270,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Daanen, Ronald","contributorId":191060,"corporation":false,"usgs":false,"family":"Daanen","given":"Ronald","email":"","affiliations":[],"preferred":false,"id":809271,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Emond, Abraham M.","contributorId":216313,"corporation":false,"usgs":false,"family":"Emond","given":"Abraham","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":809272,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kass, M. Andy","contributorId":248501,"corporation":false,"usgs":false,"family":"Kass","given":"M. Andy","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":809273,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":809274,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wylie, Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":201929,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":809275,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70217704,"text":"70217704 - 2018 - Airborne electromagnetic imaging of permafrost for hydrologic and infrastructure studies","interactions":[],"lastModifiedDate":"2021-02-08T17:30:57.857317","indexId":"70217704","displayToPublicDate":"2018-12-31T11:28:17","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Airborne electromagnetic imaging of permafrost for hydrologic and infrastructure studies","docAbstract":"Permafrost is found throughout northern latitudes, and hasfar reaching implications for natural and man-made environments including hydrologic processes, landscape dynamics, ecosystems, and infrastructure. While maps of near-surface permafrost characteristics are available, relatively little is known about permafrost distributions at depth over large areas. Here, we summarize several frequency domain airborne electromagnetic (AEM) surveys acquired within interior Alaska from 2006 –2016 that were collected to improveunderstanding ofpermafrost and geological controls on hydrologic processes and infrastructure. Results of the AEM surveys are supported by both hydrogeophysical numerical models and ground-based geophysical observations.
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Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Emond, Abraham M.","contributorId":216313,"corporation":false,"usgs":false,"family":"Emond","given":"Abraham","email":"","middleInitial":"M.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":809290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":809291,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daanen, Ronald","contributorId":191060,"corporation":false,"usgs":false,"family":"Daanen","given":"Ronald","email":"","affiliations":[],"preferred":false,"id":809292,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199962,"text":"ds1098 - 2018 - Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery","interactions":[],"lastModifiedDate":"2019-01-28T10:50:50","indexId":"ds1098","displayToPublicDate":"2018-12-21T17:19:46","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1098","displayTitle":"Interior Least Tern Sandbar Nesting Habitat Measurements from Landsat Thematic Mapper Imagery","title":"Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery","docAbstract":"Sandbars of large sand-bedded rivers of the central United States serve important ecological functions to many species, including the endangered Interior Least Tern (Sternula antillarum, ILT). The ILT is a colonial bird that feeds on fish and nests primarily on riverine sandbars during its annual breeding season of around May through July, depending on region. During this time, ILTs require bare sand of sufficient elevation so as not to be inundated between nest initiation and fledging of hatchlings. Partly because of decreases in available sandbar habitat from river channelization and impoundment, ILTs were listed as endangered in 1985.
Sandbars used by ILTs in central United States rivers are highly dynamic and undergo substantive changes across a wide range of temporal and spatial scales. River hydrology is the primary driver of sandbar morphodynamics in these systems. Better characterization of sandbar area with time, accounting for varying flow regimes, allows for a better understanding of landscape-scale ecology for sandbar-dependent species such as the ILT. This work uses remote-sensing techniques to quantify sandbar area that may be used by ILTs at the land-scape scale and how it has changed with time. The assessment of landscape-scale trends in sandbar area with time requires datasets with high temporal resolution and long record periods covering large geographic areas. Evaluation of remotely sensed datasets requires consideration of river stage fluctuations. To make this assessment, we developed land-cover classification datasets within active channel masks using all available images from the Landsat Thematic Mapper series of satellites meeting cloud-free (40 percent or less) and ice-free criteria. Landsat imagery was selected because of its long record period, spatial coverage, and regular reimaging cycle, making it well suited to monitor ILT sandbar habitat with time. We also attributed each scene with discharge or stage using a new database integrating U.S. Geological Survey and U.S. Army Corps of Engineers river data with Landsat metadata. This report documents development of these riverine classification datasets with a focus on applicability to the ILT. This framework may be used to continue monitoring the ILT sandbar nesting habitat or to evaluate other aquatic and terrestrial species whose life cycles are related to sandbars and channel complexity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1098","collaboration":"Prepared in cooperation with the American Bird Conservancy","usgsCitation":"Bulliner, E.A., Elliott, C.M., Jacobson, R.B., and Lott, C., 2018, Interior Least Tern sandbar nesting habitat measurements from Landsat Thematic Mapper imagery: U.S. Geological Survey Data Series 1098, 32 p., https://doi. org/10.3133/ds1098. ","productDescription":"Report: v, 32 p.; Tables 9–12; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066937","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":360602,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1098/ds1098_tables9-12.xlsx","text":"Tables 9–12","size":"28.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"Tables 9–12"},{"id":360653,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CV4GNG","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Interior least tern sandbar nesting habitat measurements from Landsat TM imagery"},{"id":360600,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1098/coverthb.jpg"},{"id":360601,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1098/ds1098.pdf","text":"Report","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1098"}],"contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
A SPARROW (SPAtially Related Regressions On Watershed attributes) model that was previously developed for western Oregon and northwestern California was updated using advancements in the SPARROW software and refinements to the input data. As was the case for the original model calibration, the updated models used the NHD Plus Version 2 as a hydrologic framework and relied on the same estimates of long-term mean suspended-sediment loads and watershed attributes. The updated calibration results indicated that two different SPARROW models were possible—one model from which sediment sources were represented by local lithology and one from which sediment sources were represented by generalized land-cover classes; precipitation, catchment slope, wildfire disturbance, and sediment loss in impoundments were significantly correlated with suspended-sediment loads in both models. The updated models also included a method to compensate for the bias introduced by using total suspended solids to represent suspended sediment in the calibration dataset—a feature that was not available during the original model calibration. The effect of this feature was an overall increase in estimated suspended-sediment loads. Although the lithology- and the land-cover based models used different landscape properties to describe sediment sources, each could be useful in specific applications. The lithology-based model provides more accurate estimates of suspended-sediment load, but the land-cover based model allows water-quality managers to estimate how much in-stream suspended-sediment load originates in areas with extensive development compared to the load that originates in areas with relatively little human impact.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185156","usgsCitation":"Wise, D.R., 2018, Updates to the suspended sediment SPARROW model developed for western Oregon and northeastern California: U.S. Geological Survey Scientific Investigations Report 2018–5156, 23 p., https://doi.org/10.3133/sir20185156.","productDescription":"Report: v, 23 p.; Appendix; Data Release","onlineOnly":"Y","ipdsId":"IP-093497","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":360706,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XVX2SM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Predictions from the updated SPARROW suspended sediment models developed for western Oregon and northwestern California"},{"id":360705,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5156/sir20185156_appendix01.xlsx","text":"Appendix 1","size":"34 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2018-5156 Appendix 1"},{"id":360704,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5156/sir20185156.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5156"},{"id":360703,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5156/coverthb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.62890625,\n 40\n ],\n [\n -120.5,\n 40\n ],\n [\n -120.5,\n 46.3\n ],\n [\n -124.62890625,\n 46.3\n ],\n [\n -124.62890625,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Hydrograph analysis tools using a master recession curve (MRC) can produce many types of hydrologically important watershed-response quantifications, including aquifer recharge and stormflow characterization. An MRC is the relation between the value of a measured response R and its rate of change with time, dR/dt, occurring on the falling limb when there is no infiltration or other water input. We have developed MRC and episodic hydrograph-evaluation methods for multiple purposes, utilizing both water table and streamflow data. The determination of a parameterized MRC through a structured procedure provides a basis for quantification of hydrologic variables and characteristics that can be validly compared among different events, sites, and periods of time. Application of the MRC to needed hydrologic quantifications is done with our revised episodic master recession (EMR) method. Expert-guided iterative procedures are used to quantify parameters needed in applying the MRC and EMR methods to a given site. Hydrologic judgments such as the significance threshold for response magnitude, and the time window within which the precipitation is assumed to be the cause of an observed response, inherently involve some elements of subjectivity. Our structured iterative approach, however, affords much flexibility in formulating expert judgments and serves to confine them to statements and procedures that can be quantified and documented. Parallel application to streamflow and water table hydrographs can produce new hydrologic insights and understanding, not least in the role of unsaturated zone processes in controlling exchanges among components of the water cycle.
","language":"English","publisher":"ACSESS","doi":"10.2136/vzj2018.03.0050","usgsCitation":"Nimmo, J.R., and Perkins, K., 2018, Episodic master recession evaluation of groundwater and streamflow hydrographs for water-resource estimation: Vadose Zone Journal, v. 17, no. 1, p. 1-25, https://doi.org/10.2136/vzj2018.03.0050.","productDescription":"25 p.","startPage":"1","endPage":"25","ipdsId":"IP-096220","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468177,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2136/vzj2018.03.0050","text":"Publisher Index Page"},{"id":360784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Kimberlie 0000-0001-8349-447X kperkins@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-447X","contributorId":138544,"corporation":false,"usgs":true,"family":"Perkins","given":"Kimberlie","email":"kperkins@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":755295,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70201673,"text":"70201673 - 2018 - Storm surge propagation and flooding in small tidal rivers during events of mixed coastal and fluvial influence","interactions":[],"lastModifiedDate":"2018-12-20T15:36:37","indexId":"70201673","displayToPublicDate":"2018-12-17T15:36:30","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Storm surge propagation and flooding in small tidal rivers during events of mixed coastal and fluvial influence","docAbstract":"The highly urbanized estuary of San Francisco Bay is an excellent example of a location susceptible to flooding from both coastal and fluvial influences. As part of developing a forecast model that integrates fluvial and oceanic drivers, a case study of the Napa River and its interactions with the San Francisco Bay was performed. For this application we utilize Delft3D-FM, a hydrodynamic model that computes conservation of mass and momentum on a flexible mesh grid, to calculate water levels that account for tidal forcing, storm surge generated by wind and pressure fields, and river flows. We simulated storms with realistic atmospheric pressure, river discharge, and tidal forcing to represent a realistic joint fluvial and coastal storm event. Storm conditions were applied to both a realistic field-scale Napa river drainage as well as an idealized geometry. With these scenarios, we determine how the extent, level, and duration of flooding is dependent on these atmospheric and hydrologic parameters. Unsurprisingly, the model indicates that maximal water levels will occur in a tidal river when high tides, storm surge, and large fluvial discharge events are coincident. Model results also show that large tidal amplitudes diminish storm surge propagation upstream and that phasing between peak fluvial discharges and high tide is important for predicting when and where the highest water levels will occur. The interactions between tides, river discharge, and storm surge are not simple, indicating the need for more integrated flood forecasting models in the future.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse6040158","usgsCitation":"Herdman, L.M., Erikson, L.H., and Barnard, P., 2018, Storm surge propagation and flooding in small tidal rivers during events of mixed coastal and fluvial influence: Journal of Marine Science and Engineering, v. 6, no. 4, p. 1-26, https://doi.org/10.3390/jmse6040158.","productDescription":"Article 158; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-100898","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468183,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse6040158","text":"Publisher Index Page"},{"id":360648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay, Napa River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.5,\n 36.5\n ],\n [\n -121,\n 36.5\n ],\n [\n -121,\n 39\n ],\n [\n -124.5,\n 39\n ],\n [\n -124.5,\n 36.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-17","publicationStatus":"PW","scienceBaseUri":"5c1cb860e4b0708288c83827","contributors":{"authors":[{"text":"Herdman, Liv M. 0000-0002-5444-6441 lherdman@usgs.gov","orcid":"https://orcid.org/0000-0002-5444-6441","contributorId":149964,"corporation":false,"usgs":true,"family":"Herdman","given":"Liv","email":"lherdman@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":754833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":754832,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200671,"text":"sir20185146 - 2018 - Methods used to estimate daily streamflow and water availability in the Massachusetts Sustainable-Yield Estimator version 2.0","interactions":[],"lastModifiedDate":"2018-12-17T13:23:00","indexId":"sir20185146","displayToPublicDate":"2018-12-17T10:30:00","publicationYear":"2018","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":"2018-5146","displayTitle":"Methods Used to Estimate Daily Streamflow and Water Availability in the Massachusetts Sustainable-Yield Estimator Version 2.0","title":"Methods used to estimate daily streamflow and water availability in the Massachusetts Sustainable-Yield Estimator version 2.0","docAbstract":"The Massachusetts Sustainable-Yield Estimator is a decision support tool that provides estimates of daily unaltered streamflow, water-use-adjusted streamflow, and water availability for ungaged, user-defined basins in Massachusetts. Daily streamflow at the ungaged site is estimated for unaltered (no water use) and water-use scenarios. The procedure for estimating streamflow was developed previously and has been implemented with minor changes and updated water-use data in version 2.0 of the Massachusetts Sustainable-Yield Estimator. Unaltered streamflow at selected exceedance probabilities is estimated by previously published regression equations. Streamflow is interpolated between the regressed quantiles to produce a continuous flow duration curve. A daily streamflow time series is produced for the ungaged site by relating the estimated flow duration curve at the ungaged site to a flow duration curve at a gaged reference site and then transferring the dates from the reference site to the ungaged site.
Minor refinements were made to the previously published methods to estimate unaltered and water-use-adjusted streamflow, including a procedure to enforce the monotonic structure of the regression-based unaltered flow quantiles, improvements to the interpolation method used for computing the estimated flow duration curve, and updates to the methods used to compute time-lagged stream alterations from groundwater pumping or discharges. Additionally, a procedure was developed to estimate prediction intervals for daily and monthly unregulated streamflow time series at an ungaged site.
The Massachusetts Sustainable-Yield Estimator computes water-use-adjusted streamflow using water-use data provided by the Massachusetts Department of Environmental Protection. Available water-use data included monthly withdrawal and wastewater discharge volumes from 2010 to 2014 for surface-water and groundwater sources. Water-use-adjusted streamflow represents the potential effect of current water use on natural streamflow in the basin over the range of historical hydrologic conditions. Georeferenced water withdrawal and discharge volumes were incorporated into the Massachusetts StreamStats web application for use in version 2.0 of the Massachusetts Sustainable-Yield Estimator. To compute water-use-adjusted streamflow, mean daily withdrawals and discharges within a user-defined basin are subtracted and added to the unaltered time series, respectively. Surface-water volumes are applied directly to the equation. Time-lagged streamflow alterations from groundwater withdrawal or wastewater discharge sources are estimated by using a response-coefficient method developed from results of previously published, calibrated groundwater models in Massachusetts.
The Massachusetts Sustainable-Yield Estimator was updated to version 2.0 to improve software stability and usability. The version 2.0 software application was developed in Microsoft Access with a graphical user interface. All geoprocessing steps, including basin delineation and compilation of basin characteristics and water use within the basin, were completed in the Massachusetts StreamStats web application and exported for use by the Massachusetts Sustainable-Yield Estimator version 2.0.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185146","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Levin, S.B., and Granato, G.E., 2018, Methods used to estimate daily streamflow and water availability in the Massachusetts Sustainable-Yield Estimator version 2.0: U.S. Geological Survey Scientific Investigations Report 2018–5146, 16 p., https://doi.org/10.3133/sir20185146.","productDescription":"Report: vi, 16 p.; Software release","ipdsId":"IP-087736","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":360268,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5146/coverthb.jpg"},{"id":360271,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95VX5AX ","text":"USGS software release","description":"USGS software 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In 2011 and 2012, the sedimentary basins in the Fort Irwin National Training Center, California, were evaluated for groundwater resources using a variety of techniques, including drilling of boreholes. This study summarizes lithostratigraphic features and deposits in 8 of 10 boreholes drilled in 2 basins located in the western part of Fort Irwin. The western part of Fort Irwin straddles the eastern edge of the Miocene Eagle Crags volcanic field; therefore, many of the rocks penetrated in the boreholes are primary volcanic deposits (lava flow, pyroclastic flow, and fallout tephra deposits) and tuffaceous or lithic-rich sedimentary rocks (siltstone to cobble conglomerates) deposited in alluvial, fluvial, lacustrine, and possibly groundwater discharge environments. Boreholes were drilled with mud-rotary techniques that result in cuttings samples, and only two to four cores ranging in length from 3 to 5 feet (ft) were collected in each borehole.
Correlation of lithostratigraphic features to borehole geophysical logs (especially gamma and resistivity) helps to establish properties of the rock and enables identification of depositional sequences of similar rock types. Lithostratigraphic features and resistivity in boreholes compare reasonably well to nearby time-domain electromagnetic sounding (resistivity) model results.
There is no direct age control on the rocks penetrated in the boreholes. The abundance of tuffaceous material as primary or slightly redeposited matrix is used to identify rocks deposited during the activity of the Eagle Crags volcanic field in the Miocene. In contrast, sedimentary rocks composed of detrital and epiclastic grains (only a few of which are tuffaceous rocks as clasts) are inferred to have been deposited during the Quaternary or Pliocene(?). The lithostratigraphic-based estimates of relative age indicate the typical thickness of the Quaternary or Pliocene(?) deposits is 70–170 ft, and that several water-bearing horizons are probable in the Miocene(?) section.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Geology and geophysics applied to groundwater hydrology at Fort Irwin, California","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131024D","usgsCitation":"Buesch, D.C., 2018, Lithostratigraphic framework in boreholes from Goldstone Lake and Nelson Lake Basins, Fort Irwin, California, chap. D of Buesch, D.C., ed., Geology and geophysics applied to groundwater hydrology at Fort Irwin, California: U.S. Geological Survey Open-file Report 2013–1024–D, 133 p., https://doi.org/10.3133/ofr20131024D.","productDescription":"vi, 133 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079918","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362164,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2013/1024/d/ofr20131024d_table2.1.xls","text":"Table 2.1","size":"56 KB xls","description":"OFR 2013-1024 Chapter D Table 2.1"},{"id":360342,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1024/d/ofr20131024d.pdf","text":"Report","size":"8.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2013-1024 Chapter D"},{"id":360343,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2013/1024/d/coverthb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County","city":"Fort Irwin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 35\n ],\n [\n -116,\n 35\n ],\n [\n -116,\n 35.67\n ],\n [\n -117,\n 35.67\n ],\n [\n -117,\n 35\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
One important component of continental-scale hydrologic modeling is quantifying the level of uncertainty in long-term hydrologic simulations and providing a range of possible simulated streamflow and/or runoff values for gaged and ungaged locations. In this paper, uncertainty was quantified for simulated streamflow and runoff generated from a monthly water balance model (MWBM) at 1575 streamgages and 109,951 hydrologic response units (HRUs), which span the conterminous United States (CONUS). A stochastic-approach, which incorporated the properties of modeled streamflow residuals back into the simulated model output, was used to create time series of upper and lower uncertainty intervals (UIs) around the simulated monthly time series. This approach was applied to an existing hydrologic regionalization implementation. Metrics used to evaluate the UIs across the CONUS (the coverage ratio, average width index, and interval skill score) indicated that on average the UIs were reliable, skillful, and sharp in being able to both contain measured streamflow observations and reduce estimates of uncertainty based on expected model predictions. These uncertainty evaluation metrics can complement each other in characterizing model skill and uncertainty over large-scale domains.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.advwatres.2018.10.005","usgsCitation":"Bock, A.R., Farmer, W.H., and Hay, L., 2018, Quantifying uncertainty in simulated streamflow and runoff from a continental-scale monthly water balance model: Advances in Water Resources, v. 122, p. 166-175, https://doi.org/10.1016/j.advwatres.2018.10.005.","productDescription":"10 p.","startPage":"166","endPage":"175","ipdsId":"IP-094722","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":360293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c14cfb5e4b006c4f8545d26","contributors":{"authors":[{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":754253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren E. 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":211478,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":754252,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199284,"text":"ofr20131024C - 2018 - Cenozoic geology of Fort Irwin and vicinity, California","interactions":[{"subject":{"id":70199284,"text":"ofr20131024C - 2018 - Cenozoic geology of Fort Irwin and vicinity, California","indexId":"ofr20131024C","publicationYear":"2018","noYear":false,"chapter":"C","displayTitle":"Cenozoic Geology of Fort Irwin and Vicinity, California","title":"Cenozoic geology of Fort Irwin and vicinity, California"},"predicate":"IS_PART_OF","object":{"id":70201192,"text":"ofr20131024 - 2014 - Geology and geophysics applied to groundwater hydrology at Fort Irwin, California","indexId":"ofr20131024","publicationYear":"2014","noYear":false,"title":"Geology and geophysics applied to groundwater hydrology at Fort Irwin, California"},"id":1}],"isPartOf":{"id":70201192,"text":"ofr20131024 - 2014 - Geology and geophysics applied to groundwater hydrology at Fort Irwin, California","indexId":"ofr20131024","publicationYear":"2014","noYear":false,"title":"Geology and geophysics applied to groundwater hydrology at Fort Irwin, California"},"lastModifiedDate":"2019-03-18T18:19:25","indexId":"ofr20131024C","displayToPublicDate":"2018-12-14T10:31:31","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1024","chapter":"C","displayTitle":"Cenozoic Geology of Fort Irwin and Vicinity, California","title":"Cenozoic geology of Fort Irwin and vicinity, California","docAbstract":"The geology of the Fort Irwin National Training Center in the north-central Mojave Desert, California, provides insights into the hydrology and water resources of the area. The Fort Irwin area is underlain by rocks ranging in age from Proterozoic to Quaternary that have been deformed by faults as young as Quaternary. Pre-Tertiary sedimentary, igneous, and metamorphic bedrock and Miocene volcanic and sedimentary rocks are exposed in the mountains and ridges, between which are basins containing Quaternary to Pliocene deposits. During the Miocene, in the western part of Fort Irwin, development of the Eagle Crags volcanic field resulted in a complex assemblage of lava flows, pyroclastic flow and fallout tephra deposits, and volcaniclastic sedimentary rocks that were deposited in alluvial, fluvial, and locally lacustrine environments; in the eastern part of Fort Irwin, epiclastic sedimentary rocks and minor tuffaceous rocks were deposited in alluvial, fluvial, and locally lacustrine environments. In the Pliocene and Quaternary, sandstone and conglomerate were deposited in alluvial and fluvial environments, and locally fine-grained materials were deposited in lacustrine, eolian, playa, and groundwater discharge environments. The Fort Irwin area is transected by Neogene to Holocene northwest- and east-striking (and fewer northeast-striking) strike-slip, normal, and locally thrust faults. Structural blocks between faults are broadly warped, and locally rocks adjacent to the faults are folded and sheared. Many of these faults influenced the formation or modification of basins, especially after about 11 million years, when the Eastern California Shear Zone developed in this area. The three-dimensional geologic framework produced by the late Cenozoic stratigraphic and structural history is represented by the continuity or spatial limitations of lithostratigraphic and correlative hydrogeologic properties. The continuity or limitations of rocks and properties influence how water moved (and moves) through the hydrogeologic system.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Geology and geophysics applied to groundwater hydrology at Fort Irwin, California","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131024C","collaboration":"Prepared in cooperation with the U.S. Army, Fort Irwin National Training Center","usgsCitation":"Buesch, D.C., Miller, D.M., and Menges, C.M., 2018, Cenozoic geology of Fort Irwin and vicinity, California, chap. C of Buesch, D.C., ed., Geology and geophysics applied to groundwater hydrology at Fort Irwin, California: U.S. Geological Survey Open-File Report 2013–1024–C, 39 p., https://doi.org/10.3133/ofr20131024C.","productDescription":"Report: iv, 39 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-079524","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":359938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1024/c/ofr20131024c.pdf","text":"Report","size":"7.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2013-1024"},{"id":359937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2013/1024/c/coverthb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County","city":"Fort Irwin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 35\n ],\n [\n -116,\n 35\n ],\n [\n -116,\n 35.67\n ],\n [\n -117,\n 35.67\n ],\n [\n -117,\n 35\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information,
Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Karst subterranean estuaries (KSEs) extend into carbonate platforms along 12% of all coastlines. A recent study has shown that microbial methane (CH4) consumption is an important component of the carbon cycle and food web dynamics within flooded caves that permeate KSEs. In this study, we obtained high‐resolution (~2.5‐day) temporal records of dissolved methane concentrations and its stable isotopic content (δ13C) to evaluate how regional meteorology and hydrology control methane dynamics in KSEs. Our records show that less methane was present in the anoxic fresh water during the wet season (4,361 ± 89 nM) than during the dry season (5,949 ± 132 nM), suggesting that the wet season hydrologic regime enhances mixing of methane and other constituents into the underlying brackish water. The δ13C of the methane (−38.1 ± 1.7‰) in the brackish water was consistently more 13C‐enriched than fresh water methane (−65.4 ± 0.4‰), implying persistent methane oxidation in the cave. Using a hydrologically based mass balance model, we calculate that methane consumption in the KSE was 21–28 mg CH4·m−2·year−1 during the 6‐month dry period, which equates to ~1.4 t of methane consumed within the 102‐ to 138‐km2 catchment basin for the cave. Unless wet season methane consumption is much greater, the magnitude of methane oxidized within KSEs is not likely to affect the global methane budget. However, our estimates constrain the contribution of a critical resource for this widely distributed subterranean ecosystem.
","language":"English","publisher":"AGU","doi":"10.1029/2018GB006026","usgsCitation":"Brankovits, D., Pohlman, J.W., Ganju, N., Iliffe, T., Lowell, N., Roth, E., Sylva, S., Emmert, J., and Lapham, L.L., 2018, Hydrologic controls of methane dynamics in karst subterranean estuaries: Global Biogeochemical Cycles, v. 32, no. 12, p. 1759-1775, https://doi.org/10.1029/2018GB006026.","productDescription":"17 p.","startPage":"1759","endPage":"1775","ipdsId":"IP-102700","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018gb006026","text":"Publisher Index Page"},{"id":360248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"12","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-07","publicationStatus":"PW","scienceBaseUri":"5c137dd2e4b006c4f8514874","contributors":{"authors":[{"text":"Brankovits, David 0000-0002-0863-5698","orcid":"https://orcid.org/0000-0002-0863-5698","contributorId":210617,"corporation":false,"usgs":true,"family":"Brankovits","given":"David","email":"","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pohlman, John W. 0000-0002-3563-4586 jpohlman@usgs.gov","orcid":"https://orcid.org/0000-0002-3563-4586","contributorId":145771,"corporation":false,"usgs":true,"family":"Pohlman","given":"John","email":"jpohlman@usgs.gov","middleInitial":"W.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":753872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iliffe, T.M.","contributorId":201287,"corporation":false,"usgs":false,"family":"Iliffe","given":"T.M.","email":"","affiliations":[],"preferred":false,"id":753873,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lowell, N.","contributorId":211383,"corporation":false,"usgs":false,"family":"Lowell","given":"N.","email":"","affiliations":[{"id":38240,"text":"Lowell Instruments","active":true,"usgs":false}],"preferred":false,"id":753874,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roth, E.","contributorId":90499,"corporation":false,"usgs":true,"family":"Roth","given":"E.","affiliations":[],"preferred":false,"id":753875,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sylva, S.P.","contributorId":211384,"corporation":false,"usgs":false,"family":"Sylva","given":"S.P.","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":753876,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Emmert, J.A.","contributorId":211385,"corporation":false,"usgs":false,"family":"Emmert","given":"J.A.","email":"","affiliations":[{"id":38241,"text":"Moody Gardens Aquarium","active":true,"usgs":false}],"preferred":false,"id":753877,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lapham, L. L.","contributorId":140085,"corporation":false,"usgs":false,"family":"Lapham","given":"L.","email":"","middleInitial":"L.","affiliations":[{"id":13383,"text":"University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, 6 Solomons, Maryland 20688","active":true,"usgs":false}],"preferred":false,"id":753878,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70199036,"text":"ofr20181143 - 2018 - Indicators of ecosystem structure and function for the Upper Mississippi River System","interactions":[],"lastModifiedDate":"2018-12-13T16:01:11","indexId":"ofr20181143","displayToPublicDate":"2018-12-13T06:50:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1143","displayTitle":"Indicators of Ecosystem Structure and Function for the Upper Mississippi River System","title":"Indicators of ecosystem structure and function for the Upper Mississippi River System","docAbstract":"This report documents the development of quantitative measures (indicators) of ecosystem structure and function for use in a Habitat Needs Assessment (HNA) for the Upper Mississippi River System (UMRS). HNAs are led periodically by the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration (UMRR) Program, which is the primary habitat restoration program on the UMRS. The UMRR Program helps determine how Federal, State and nongovernmental agencies can best address environmental issues on one of the world’s largest and most diverse river systems. Each indicator in this report represents at least one management objective developed for the river system. These objectives were developed in a previous planning effort using an ecosystem management conceptual framework (USACE, 2011). The objectives represent five essential ecosystem characteristics: hydraulics and hydrology, biogeochemistry, geomorphology, habitat, and biota. Subsequent to the 2011 planning effort, the UMRR increased its focus on improving the health and resilience of the UMRS. The indicators presented here are based on the five essential ecosystem characteristics and four aspects of ecosystems thought to support general ecosystem resilience (the ability of an ecosystem to adapt and respond to disturbances): (1) connectivity, (2) diversity and redundancy, (3) controlling variables, and (4) slow processes. Thus, we developed indicators that quantify both essential ecosystem characteristics and characteristics of a resilient river system. The indicators documented in this report focus on important aspects of river floodplain hydrogeomorphology, given the fundamental role hydrogeomorphology plays in determining habitat conditions and ecosystem health and resilience at broad geographic scales. The information contained within this report provides a broader scale (for example, system-wide) context for management decisions made at finer scales (for example, within river reaches or at project sites) and is designed for use in the formal system-wide Habitat Needs Assessment II (HNA–II) led by the UMRR Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181143","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program","usgsCitation":"De Jager, N.R., Rogala, J.T., Rohweder, J.J., Van Appledorn, M., Bouska, K.L., Houser, Jeffrey, N., and Jankowski, K.J., 2018, Indicators of ecosystem structure and function for the Upper Mississippi River System: U.S. Geological Survey Open-File Report 2018–1143, 115 p., including 4 appendixes, https://doi.org/10.3133/ofr20181143.","productDescription":"xiii, 115 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-092913","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":360203,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1143/coverthb2.jpg"},{"id":360204,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1143/ofr20181143.pdf","text":"Report","size":"45.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1143"}],"country":"United States","otherGeospatial":"Upper Mississippi River System","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 5460
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
As human populations increase, so does their influence over the environment. Altered terrain, degraded water quality, and threatened or endangered species are all-too-common consequences of a growing anthropogenic influence on the landscape. To help manage these effects, researchers have developed new ways to characterize current environmental conditions and help resource managers seek solutions to bring affected areas back to their best attainable health. Before an ecosystem can be improved, however, its current level of ecological stress must be determined. Characterizing environmental conditions at many sites across a landscape helps managers understand the range of current conditions and prioritize where they might focus restoration and protection efforts.
This report details the development of a prioritization framework to score riverine ecosystem stressors in a watershed based on example sites from the Tualatin River Basin in northwestern Oregon. The framework incorporated the most influential site-specific stressors throughout the basin built on a long history of data collection. These stressors were characterized with 13 metrics that were organized into 4 groups: hydrologic, water quality, physical habitat, and biological. Each stressor metric used readily accessible data and was translated to a score between 0 and 10. The higher the score, the healthier the site. This initial application of the framework used field observations and measurements to rank site conditions at two Tualatin River sites and four Tualatin River tributary sites. Given the versatility of this framework, it easily could be expanded to include more sites or new metrics, if necessary. Because stressors varied by season, all metrics for the tributary sites were scored separately during the wet season (November through April) and dry season (May through October). Water-quality data were available over a prolonged period; therefore, water-quality metrics were assessed by season and by decade (1990–99 compared to 2000–12) to evaluate long-term stressor trends.
Results for the Tualatin River Basin prioritization framework indicated that the urban tributaries demonstrated the greatest stress throughout the year, especially during the dry summer months. Spatially, the upper Tualatin River was healthier than the lower reaches of the river. Water-quality has improved in the last 10 years, mostly due to improvements in the dry period contaminant scores, but challenges remain with high water temperatures and low dissolved-oxygen conditions.
The biggest challenge with this type of research derived from inconsistencies within the available data. Both spatial and temporal data gaps must be addressed to improve the prioritization. Incorporating both discrete and continuous datasets into the prioritization framework remains a challenge because the datasets have slightly different information and criteria and are not always comparable. Regardless, this report provides guidelines for developing a prioritization framework that ranks the ecological health of sites in a watershed and provides guidance on management actions for improving conditions by targeting factors that greatly affect the health of river ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185153","collaboration":"Prepared in cooperation with Clean Water Services","usgsCitation":"Sobieszczyk, S., Jones, K.L., Rounds, S.A., Nilsen, E.B., and Morace, J.L., 2018, Prioritization framework for ranking riverine ecosystem stressors using example sites from the Tualatin River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2018-5153, 40 p., https://doi.org/10.3133/sir20185153.","productDescription":"vii, 40 p.","onlineOnly":"Y","ipdsId":"IP-060830","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359875,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5153/sir20185153.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5153"},{"id":359874,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5153/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tualatin River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5,\n 45.3\n ],\n [\n -122.5,\n 45.3\n ],\n [\n -122.5,\n 45.75\n ],\n [\n -123.5,\n 45.75\n ],\n [\n -123.5,\n 45.3\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Digital flood-inundation maps for a 6.5-mile reach of the Salamonie River at Portland, Indiana, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Salamonie River at Portland, Ind. (station 03324200). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System web interface at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service (site PORI3) at https:/water.weather.gov/ahps/.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the current (2018) stage-discharge relation at the Salamonie River at Portland, Ind., streamgage.
The hydraulic model then was used to compute nine water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 10.7 ft or near bankfull to 18.7 ft, which equals the highest point on the streamgage rating curve. The simulated water-surface profiles then were combined with a geographic information system digital elevation model derived from light detection and ranging data having a 0.49-ft root mean square error and 4.9-ft horizontal resolution resampled to a 10-ft grid to delineate the area flooded at each stage. The availability of these maps, along with information regarding current stage from the USGS streamgage, will 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/sir20185132","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Strauch, K.R., 2018, Flood-inundation maps for the Salamonie River at Portland, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5132, 9 p., https://doi.org/10.3133/sir20185132.","productDescription":"Report: vi, 9 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-089966","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":359800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VM4BJD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Flood-inundation geospatial datasets for the Salamonie River at Portland, Indiana"},{"id":359798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5132/coverthb.jpg"},{"id":359799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5132/sir20185132.pdf","text":"Report","size":"996 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5132"}],"country":"United States","state":"Indiana","city":"Portland","otherGeospatial":"Salamonie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.0587272644043,\n 40.38813537489036\n ],\n [\n -84.93925094604492,\n 40.38813537489036\n ],\n [\n -84.93925094604492,\n 40.44877593183776\n ],\n [\n -85.0587272644043,\n 40.44877593183776\n ],\n [\n -85.0587272644043,\n 40.38813537489036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio Kentucky Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278