Intertidal mudflats are morphodynamic features present in many estuaries worldwide. Often located between vegetated shores and deep channels they comprise valuable ecosystems and serve to protect the hinterland by attenuating waves. Although mudflats are persistently present on yearly to decadal time scales, little is known on their morphodynamic adaptation to short‐term variations in forcing such as storms, spring‐neap tidal cycles, and sediment supply. This study aims to explore the morphodynamic resilience of mudflats to seasonal variations in forcing. First, we compare transects observed in South Bay, California, at 3‐ to 6‐monthly intervals. Second, we present the results of a process‐based, morphodynamic profile model (Mflat). Mflat is an open source, Matlab code that describes both cross‐shore and alongshore tidal hydrodynamics as well as a stationary wave model. An advection‐diffusion equation solves sediment transport while bed level changes occur by the divergence of the sediment transport field. Mflat reproduces the observed South San Francisco Bay profile in equilibrium with significant skill. Short‐term variations in hydrodynamic forcing and sediment characteristics disturb the profile mainly at the channel‐shoal edge. The modeled profile disturbance is consistent with observations. The modeled profile is remarkably resilient since it recovers to the equilibrium profile within weeks to months. The model results suggest that 3‐monthly observation intervals are probably too long to discriminate processes responsible for the profile disturbance. These processes may include variations in sediment supply, mudflat erodibility, and wave action as well as the spring‐neap tidal cycle.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JC015492","usgsCitation":"Van der Wegen, M., Roelvink, D., and Jaffe, B.E., 2019, Morphodynamic resilience of intertidal mudflats on a seasonal time scale: JGR Oceans, v. 124, no. 11, p. 8290-8308, https://doi.org/10.1029/2019JC015492.","productDescription":"19 p.","startPage":"8290","endPage":"8308","ipdsId":"IP-110231","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459523,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jc015492","text":"External Repository"},{"id":379385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 37.125286284966805\n ],\n [\n -121.37695312499999,\n 37.125286284966805\n ],\n [\n -121.37695312499999,\n 38.30718056188316\n ],\n [\n -122.958984375,\n 38.30718056188316\n ],\n [\n -122.958984375,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Van der Wegen, Mick","contributorId":191095,"corporation":false,"usgs":false,"family":"Van der Wegen","given":"Mick","email":"","affiliations":[],"preferred":false,"id":801446,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roelvink, Dano","contributorId":139950,"corporation":false,"usgs":false,"family":"Roelvink","given":"Dano","email":"","affiliations":[{"id":13328,"text":"UNESCO-IHE","active":true,"usgs":false}],"preferred":false,"id":801447,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaffe, Bruce E. 0000-0002-8816-5920 bjaffe@usgs.gov","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":2049,"corporation":false,"usgs":true,"family":"Jaffe","given":"Bruce","email":"bjaffe@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":801448,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215282,"text":"70215282 - 2019 - Holocene Sea-Level Variability from Chesapeake Bay Tidal Marshes","interactions":[],"lastModifiedDate":"2020-10-14T19:46:59.518365","indexId":"70215282","displayToPublicDate":"2019-10-14T14:35:08","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1905,"text":"Holocene","active":true,"publicationSubtype":{"id":10}},"title":"Holocene Sea-Level Variability from Chesapeake Bay Tidal Marshes","docAbstract":"We reconstructed the last 10,000 years of Holocene relative sea-level rise (RSLR) from sediment core records in near Chesapeake Bay, eastern U.S.A., including new marsh records from the Potomac and Rappahannock Rivers, Virginia. Results show mean RSLR rates of 2.6 mm yr-1 from 10 to 8 kilo-annum (ka) due to combined final ice-sheet melting during deglaciation and glacio-isostatic adjustment (GIA subsidence). Mean RSLR rates from ~6 ka to present were 1.4 mm yr-1 due mainly to GIA, consistent with other east coast marsh records and geophysical models. However, a progressively slower mean rate (< 1.0 mm yr-1) characterized the last 1000 years when a multi-century-long period of tidal marsh development occurred during the Medieval Climate Anomaly (MCA) and Little Ice Age (LIA) in the Chesapeake Bay region and other East Coast marshes. This decrease was most likely due to climatic and glaciological processes and, correcting for GIA, represents a fall in global mean sea level (GMSL) at near the end of Holocene Neoglacial cooling. These pre-historical climate- and GIA-driven Chesapeake Bay sea-level changes contrast sharply with those based on Chesapeake Bay tide-gauge rates (3.1-4.5 mm yr-1) (back to 1903). After subtracting the GIA subsidence component, these rates can be attributed to long-term (millennial) global factors of accelerated ocean thermal expansion (~ 1.0 mm yr-1) and mass loss from alpine glaciers and Greenland and Antarctic Ice Sheets (1.5-2.0 mm yr-1).","language":"English","publisher":"SAGE Publications","doi":"10.1177/0959683619862028","usgsCitation":"Cronin, T.M., Clevenger, M.K., Tibert, N.E., Prescott, T., Toomey, M., Hubeny, J.B., Abbott, M.B., Seidenstein, J., Whitworth, H., Fisher, S., Wondolowski, N., and Ruefer, A., 2019, Holocene Sea-Level Variability from Chesapeake Bay Tidal Marshes: Holocene, v. 29, no. 11, p. 1979-1693, https://doi.org/10.1177/0959683619862028.","productDescription":"15 p.","startPage":"1979","endPage":"1693","ipdsId":"IP-102993","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":459524,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/0959683619862028","text":"Publisher Index Page"},{"id":379383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.6845703125,\n 36.88401445049676\n ],\n [\n -75.7562255859375,\n 36.88401445049676\n ],\n [\n -75.7562255859375,\n 39.06184913429154\n ],\n [\n -76.6845703125,\n 39.06184913429154\n ],\n [\n -76.6845703125,\n 36.88401445049676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":801637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clevenger, Megan K.","contributorId":243159,"corporation":false,"usgs":false,"family":"Clevenger","given":"Megan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":801638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tibert, Neil E.","contributorId":243160,"corporation":false,"usgs":false,"family":"Tibert","given":"Neil","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":801639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prescott, Tammy","contributorId":243161,"corporation":false,"usgs":false,"family":"Prescott","given":"Tammy","email":"","affiliations":[],"preferred":false,"id":801640,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Toomey, Michael 0000-0003-0167-9273 mtoomey@usgs.gov","orcid":"https://orcid.org/0000-0003-0167-9273","contributorId":184097,"corporation":false,"usgs":true,"family":"Toomey","given":"Michael","email":"mtoomey@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":801641,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hubeny, J. Bradford","contributorId":197080,"corporation":false,"usgs":false,"family":"Hubeny","given":"J.","email":"","middleInitial":"Bradford","affiliations":[],"preferred":false,"id":801642,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Abbott, Mark B.","contributorId":97733,"corporation":false,"usgs":true,"family":"Abbott","given":"Mark","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":801643,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Seidenstein, Julia","contributorId":243162,"corporation":false,"usgs":false,"family":"Seidenstein","given":"Julia","affiliations":[],"preferred":false,"id":801644,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Whitworth, Hannah","contributorId":243163,"corporation":false,"usgs":false,"family":"Whitworth","given":"Hannah","email":"","affiliations":[],"preferred":false,"id":801645,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fisher, Samuel R","contributorId":225265,"corporation":false,"usgs":false,"family":"Fisher","given":"Samuel R","affiliations":[{"id":41086,"text":"La Sierra University","active":true,"usgs":false}],"preferred":false,"id":801646,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wondolowski, Nick","contributorId":243164,"corporation":false,"usgs":false,"family":"Wondolowski","given":"Nick","email":"","affiliations":[],"preferred":false,"id":801647,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ruefer, Anna","contributorId":243165,"corporation":false,"usgs":false,"family":"Ruefer","given":"Anna","email":"","affiliations":[],"preferred":false,"id":801648,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70215273,"text":"70215273 - 2019 - River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model","interactions":[],"lastModifiedDate":"2020-10-15T13:33:08.421308","indexId":"70215273","displayToPublicDate":"2019-10-14T14:25:04","publicationYear":"2019","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":"River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model","docAbstract":"Accurate quantification of riverine water‐quality concentration and flux is challenging because monitoring programs typically collect concentration data at lower frequencies than discharge data. Statistical methods are often used to estimate concentration and flux on days without observations. One recently developed approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS), which has been shown to provide among the most accurate estimates compared to other common methods. The main objective of this work was to improve WRTDS estimation by accounting for the autocorrelation structure of model residuals using the first‐order autoregressive model (AR1). This modified approach, called WRTDS‐Kalman Filter (WRTDS‐K), was compared with WRTDS for six constituents including nitrate‐plus‐nitrite (NOx), total phosphorus, total Kjeldahl nitrogen, soluble reactive phosphorus, suspended sediment, and chloride. Near‐daily concentration records at nine sites were used to generate subsets through Monte Carlo sampling for five different sampling scenarios. Results show that WRTDS‐K provided generally better daily estimates of concentration and flux than WRTDS under these sampling scenarios for all constituents, especially NOx. The degree of improvement is strongly affected by the underlying sampling scenario, with WRTDS‐K gaining more advantage when more samples are available, and hence more residuals can be exploited. The performance of WRTDS‐K depends on the AR1 coefficient (ρ) and that relationship varies with constituents and sampling scenarios. These results provided recommendations on the optimal ρ for each constituent and sampling scenario. Overall, WRTDS‐K has the potential for broad applications to monitoring records elsewhere, as demonstrated by a pilot application to Chesapeake Bay tributaries.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019wr025338","usgsCitation":"Zhang, Q., and Hirsch, R.M., 2019, River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model: Water Resources Research, v. 55, no. 11, p. 9705-9723, https://doi.org/10.1029/2019wr025338.","productDescription":"19 p.","startPage":"9705","endPage":"9723","ipdsId":"IP-110106","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr025338","text":"Publisher Index Page"},{"id":379382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie and Ohio River tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.7705078125,\n 40.58058466412761\n ],\n [\n -84.48486328124999,\n 40.01078714046552\n ],\n [\n -83.9794921875,\n 39.53793974517628\n ],\n [\n -83.3642578125,\n 39.90973623453719\n ],\n [\n -82.72705078125,\n 39.2832938689385\n ],\n [\n -82.37548828125,\n 41.04621681452063\n ],\n [\n -83.408203125,\n 40.896905775860006\n ],\n [\n -83.84765625,\n 41.178653972331674\n ],\n [\n -83.7158203125,\n 41.82045509614034\n ],\n [\n -84.3310546875,\n 41.918628865183045\n ],\n [\n -84.6826171875,\n 41.393294288784865\n ],\n [\n -85.1220703125,\n 41.16211393939692\n ],\n [\n -85.0341796875,\n 40.54720023441049\n ],\n [\n -84.7705078125,\n 40.58058466412761\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.9912109375,\n 41.72213058512578\n ],\n [\n -81.36474609375,\n 41.343824581185686\n ],\n [\n -81.82617187499999,\n 41.19518982948959\n ],\n [\n -81.5185546875,\n 40.93011520598305\n ],\n [\n -80.96923828125,\n 41.04621681452063\n ],\n [\n -80.6396484375,\n 41.45919537950706\n ],\n [\n -80.6396484375,\n 41.78769700539063\n ],\n [\n -80.9912109375,\n 41.72213058512578\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":801435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":801436,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218246,"text":"70218246 - 2019 - Infrasound from giant bubbles during explosive submarine eruptions","interactions":[],"lastModifiedDate":"2021-02-19T20:15:54.51462","indexId":"70218246","displayToPublicDate":"2019-10-14T14:11:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Infrasound from giant bubbles during explosive submarine eruptions","docAbstract":"Shallow submarine volcanoes pose unique scientific and monitoring challenges. The interaction between water and magma can create violent explosions just below the surface, but the inaccessibility of submerged volcanoes means they are typically not instrumented. This both increases the risk to marine and aviation traffic and leaves the underlying eruption physics poorly understood. Here we use low-frequency sound in the atmosphere (infrasound) to examine the source mechanics of shallow submarine explosions from Bogoslof volcano, Alaska. We show that the infrasound originates from the oscillation and rupture of magmatic gas bubbles that initially formed from submerged vents, but that grew and burst above sea level. We model the low-frequency signals as overpressurized gas bubbles that grow near the water–air interface, which require bubble radii of 50–220 m. Bubbles of this size and larger have been described in explosive subaqueous eruptions for more than a century, but we present a unique geophysical record of this phenomenon. We propose that the dominant role of seawater during the effusion of gas-rich magma into shallow water is to repeatedly produce a gas-tight seal near the vent. This resealing mechanism leads to sequences of violent explosions and the release of large, bubble-forming volumes of gas—activity we describe as hydrovulcanian.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41561-019-0461-0","usgsCitation":"Lyons, J.J., Haney, M.M., Fee, D., Wech, A., and Waythomas, C.F., 2019, Infrasound from giant bubbles during explosive submarine eruptions: Nature Geoscience, v. 12, p. 952-958, https://doi.org/10.1038/s41561-019-0461-0.","productDescription":"7 p.","startPage":"952","endPage":"958","ipdsId":"IP-104588","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":383394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.78295898437497,\n 53.1928702436326\n ],\n [\n -166.1737060546875,\n 53.1928702436326\n ],\n [\n -166.1737060546875,\n 54.08517342088679\n ],\n [\n -168.78295898437497,\n 54.08517342088679\n ],\n [\n -168.78295898437497,\n 53.1928702436326\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2019-10-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":810690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David","contributorId":251816,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":810692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810694,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208993,"text":"70208993 - 2019 - Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations","interactions":[],"lastModifiedDate":"2020-03-10T14:20:54","indexId":"70208993","displayToPublicDate":"2019-10-14T13:57:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations","docAbstract":"In the absence of measured streamflow, statistically simulated daily streamflow can be used to support the ability of physical models to represent hydrologic processes at ungauged locations. The objective of this study was to determine the feasibility of using statistical simulations in place of measured streamflow to calibrate physical models in ungauged basins. Daily streamflow was simulated at each of the 1,410 gauged watersheds using a cross-validated implementation of pooled ordinary kriging (POK). In this manner, the streamflow at each gauge was simulated as if no at-site streamflow information were available. The National Hydrologic Model application of the Precipitation-Runoff Modeling System was then calibrated through two separate procedures: (1) with measured streamflow, and (2) with statistically simulated streamflow in lieu of measured streamflow. Calibrating with statistically simulated streamflow produced performances within 23% of applications with knowledge of at-site measurements. 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region","title":"Large loss of CO2 in winter observed across pan-arctic permafrost region","docAbstract":"Recent warming in the Arctic, which has been amplified during the winter1,2,3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux from Arctic and boreal soils to assess current and future winter carbon losses from the northern permafrost domain. We estimate a contemporary loss of 1,662 TgC per year from the permafrost region during the winter season (October–April). This loss is greater than the average growing season carbon uptake for this region estimated from process models (−1,032 TgC per year). Extending model predictions to warmer conditions up to 2100 indicates that winter CO2 emissions will increase 17% under a moderate mitigation scenario—Representative Concentration Pathway 4.5—and 41% under business-as-usual emissions scenario—Representative Concentration Pathway 8.5. Our results provide a baseline for winter CO2 emissions from northern terrestrial regions and indicate that enhanced soil CO2 loss due to winter warming may offset growing season carbon uptake under future climatic conditions.
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Relative to pre‐settlement (1650), the 1992 LU produces colder mean annual air temperature (−0.09 ± 0.16 °C) and increased precipitation (0.08 ± 0.09 mm day−1) over the United States, warmer (0.08 °C) and wetter (0.03 mm day−1) conditions over Florida, and warming (0.32 °C) and drying (−0.03 mm day−1) over Cuba, indicating that LUC has played a varying role in climate change over the region.
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We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios.
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,{"id":70212818,"text":"70212818 - 2019 - Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","interactions":[],"lastModifiedDate":"2024-05-16T14:56:12.436485","indexId":"70212818","displayToPublicDate":"2019-10-14T08:20:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","docAbstract":"Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.
Estimates of the magnitudes of annual peak streamflows with annual exceedance probabilities of 0.5, 0.2, 0.1, 0.04, 0.02, 0.01, and 0.002 (equivalent to recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively) were computed for 391 streamgages in Ohio and adjacent states based on data collected through the 2015 water year. The flood-frequency estimates were computed following guidance outlined in Bulletin 17C, developed by the Advisory Committee on Water Information. The Bulletin 17C guidelines retain the basic statistical framework of the superseded Bulletin 17B guidelines; however, the Bulletin 17C guidelines add several enhancements including an improved method of moments approach for fitting the log-Pearson Type III (LPIII) distribution to the flood peaks (called the expected moments algorithm), a generalization of the Grubbs Beck low-outlier test (called the Multiple Grubbs Beck test) that permits identification of multiple potentially influential low floods, and new methods for estimating regional skew and uncertainty.
Equations for estimating flood-frequency characteristics at ungaged sites on rural, unregulated streams in Ohio were developed with a two-step process involving ordinary least-squares and generalized least-squares regression techniques. Data from 333 streamgages with 10 or more years of unregulated record were screened for redundancy and a regression dataset was selected that was composed of flood-frequency and basin-characteristic data for 275 streamgages in Ohio and adjacent states. Two sets of equations were developed—one set, referred to as the “simple model,” uses regression region and drainage area as regressor variables, and a second set, referred to as the “full model,” uses regression region, drainage area, main-channel slope, and the percentage of the watershed covered by water and wetlands as regressor variables.
The average standard errors of prediction ranged from about 40.5 to 46.5 percent for the simple-model equations and from about 37.2 to 40.3 percent for the full-model equations. For sites meeting the rural, unregulated criteria, flood-frequency estimates determined by means of LPIII analyses are reported along with weighted flood-frequency estimates, computed as a function of the LPIII estimates and the regression estimates. For sites with homogenous periods of regulation, flood-frequency estimates determined by means of LPIII analyses are reported. Ninety-five percent confidence limits are reported for all estimates.
Values of regressor variables were determined from digital spatial datasets by means of a geographic information system (GIS). The GIS datasets and the new full-model equations have been incorporated into Ohio’s StreamStats application, a web-based, GIS-backed system designed to facilitate the estimation of streamflow statistics at ungaged locations on streams.
Seasonal patterns in peak flows were assessed for 295 streamgages in Ohio. Annual peak flows occurred most frequently between January and April, with March having the highest frequency of occurrence. The month with the fewest number of annual peaks was October. Peak-of-record flows occurred most frequently in March, followed by January (months in which two of Ohio’s most severe widespread floods in recent history occurred). None of the peak-of-record flows occurred in October and only two occurred in November.
Temporal trend in annual peak flows were assessed for 133 streamgages on unregulated streams in Ohio with 30 or more years of systematic record. Trends were assessed by computing the rank correlation (as measured with the two-sided Kendall’s tau statistic) between time and annual peak flows. Weak but statistically significant trends were indicated at 15 of the 133 streamgages. Of the 15 streamgages with significant trend in annual peak flows, 12 had an upward trend (positive tau) and 3 had a downward trend (negative tau). All 12 streamgages with positive tau values were at latitudes north of 40°33', and streamgages with negative tau values were at latitudes south of 40°33'.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195018","collaboration":"Prepared in cooperation with the Ohio Department of Transportation","usgsCitation":"Koltun, G.F., 2019, Flood-frequency estimates for Ohio streamgages based on data through water year 2015 and techniques for estimating flood-frequency characteristics of rural, unregulated Ohio streams: U.S. Geological Survey Scientific Investigations Report 2019–5018, 25 p., https://doi.org/10.3133/sir20195018.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendices 1.1-1.8; Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100946","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368118,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5018/sir20195018.pdf","text":"Report ","size":"6.45 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\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229–1737
Digital flood-inundation maps for a 4-mile reach of Nimishillen Creek near North Industry, Ohio, were created by the U.S. Geological Survey (USGS) in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping (FIM) Program 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 Nimishillen Creek at North Industry, Ohio (station number 03118500). Near-real-time stages at this streamgage can be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated to the current stage-discharge relation at the streamgage on Nimishillen Creek at North Industry and documented high-water marks from the flood of January 12, 2017.
The hydraulic model was then used to compute seven water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 8 to 14 ft, which is from “action stage” to above “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used in combination with a geographic information system (GIS) digital elevation model derived from light detection and ranging data to delineate the areas flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, 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. Forecasts for the USGS streamgage on Nimishillen Creek at North Industry, Ohio are issued as needed during times of high water, but are not routinely available (National Weather Service, 2017).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195083","collaboration":"Prepared in cooperation with the Muskingum Watershed Conservancy District, Ohio, and the Stark County Board of Commissioners","usgsCitation":"Whitehead, M.T., 2019, Flood-inundation maps for Nimishillen Creek near North Industry, Ohio, 2019: U.S. Geological Survey Scientific Investigations Report 2019–5083, 11 p., https://doi.org/10.3133/sir20195083.\n","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104812","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFOVN2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets and hydraulic model for flood-inundation maps of Nimishillen Creek near North Industry, Ohio:"},{"id":368075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5083/sir20195083.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5083"},{"id":368074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5083/coverthb.jpg"}],"country":"United States","state":"Ohio","county":"Stark County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.0864,40.9879],[-81.0865,40.9839],[-81.0866,40.978],[-81.0869,40.9013],[-81.0873,40.728],[-81.0922,40.7285],[-81.1001,40.7281],[-81.1989,40.7292],[-81.1991,40.7224],[-81.2373,40.7237],[-81.241,40.6507],[-81.2755,40.651],[-81.2791,40.6511],[-81.304,40.6518],[-81.3173,40.6519],[-81.4372,40.6529],[-81.4365,40.6584],[-81.4395,40.6625],[-81.4467,40.6657],[-81.4589,40.6654],[-81.4675,40.6555],[-81.6489,40.6346],[-81.6491,40.6681],[-81.6483,40.7371],[-81.648,40.9145],[-81.4201,40.9064],[-81.4164,40.9889],[-81.3932,40.9887],[-81.1059,40.9882],[-81.0925,40.988],[-81.0864,40.9879]]]},\"properties\":{\"name\":\"Stark\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard
Columbus OH 43229–1753
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Threats to our recreational and drinking waters include disease-causing (pathogenic) organisms from fecal contamination and toxins produced by some species of cyanobacteria (cyanotoxins) that can cause acute and (or) chronic illnesses. Because traditional laboratory methods for detecting these threats take too long for prompt public health protection, tools for real-time assessments are needed to protect public health. To address this need, the U.S. Geological Survey is collaborating with State and local partners to develop models that provide real-time estimates of Escherichia coli (E. coli) (for pathogens) and (or) microcystin (for freshwater cyanotoxins) levels at inland and Great Lakes beaches and drinking-water intakes. Model results are then used to inform the public of water-quality conditions in near-real time through the Great Lakes NowCast (https://ny.water.usgs.gov/maps/nowcast/). Behind the scenes, the NowCast provides speed and efficiency for managers by automating data management and standardizing methods among agencies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193061","collaboration":"Prepared in cooperation with U.S. Environmental Protection Agency, Great Lakes Restoration Initiative","usgsCitation":"Francy, D.S., Brady, A.M., and Zimmerman, T.M., 2019, Real-time assessments of water quality—A nowcast for Escherichia coli and cyanobacterial toxins: U.S. Geological Survey Fact Sheet 2019–3061, 4 p., https://doi.org/10.3133/fs20193061.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111133","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":368188,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3061/coverthb.jpg"},{"id":368189,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3061/fs20193061.pdf","text":"Report","size":"970 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3061"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Boulevard, Suite 100
Columbus, OH 43229
In 2007 the U.S. Geological Survey (USGS) completed an assessment of undiscovered, technically recoverable oil and gas resources in the East Greenland Rift Basins Province of Northeast Greenland. The province was selected as the prototype for the U.S. Geological Survey Circum-Arctic Resource Appraisal (CARA). In collaboration with the Geological Survey of Denmark and Greenland (GEUS), the province was subdivided into nine geologically distinctive areas. Seven of these were defined as Assessment Units (AUs), of which five were quantitatively assessed. These are: North Danmarkshavn Salt Basin, South Danmarkshavn Basin, Thetis Basin, Northeast Greenland Volcanic Province, and Liverpool Land Basin. Jameson Land Basin and the Jameson Land Basin Subvolcanic Extension were defined as AUs but were not quantitatively assessed.
Onshore studies by GEUS and other organizations suggest that at least four stratigraphic intervals may contain potential source rocks for petroleum. The geological history of related areas in western Norway and burial history modeling suggest that Upper Jurassic strata are most likely to contain petroleum source rocks. A wide variety of possible trapping mechanisms are expected within the province. Potential traps in the North Danmarkshavn Salt Basin AU are dominated by structures formed through salt tectonics; those in the South Danmarkshavn Basin and the Northeast Greenland Volcanic Province are characterized by extensional structures and by stratigraphic traps in submarine fan complexes. Prospective inversion structures of Tertiary age are present along the western margin of South Danmarkshavn Basin AU, and the large horst block structures that separate the Danmarkshavn and Thetis Basins may provide numerous opportunities for traps in fault blocks and along various facies-related permeability barriers. Possible reservoirs include shallow marine to nonmarine sandstones of Middle Jurassic age, sandstones in Upper Jurassic synrift deposits, Cretaceous sandstones in submarine fan complexes, sandstones in Paleogene progradational sequences, and in Upper Carboniferous to Lower Permian warm-water carbonate sequences, especially in northern Danmarkshavn Basin. Marine shales are expected to provide the main sealing lithologies in most AUs.
Most of the undiscovered oil, gas, and natural gas liquids are likely to be in the offshore areas of the province and are inferred to belong to an Upper Jurassic Composite Total Petroleum System. The USGS estimated that the East Greenland Rift Basins Province contains approximately (mean) 31,400 million barrels oil equivalent (MMBOE) of oil, natural gas, and natural gas liquids. Of the five assessed AUs, North Danmarkshavn Salt Basin and the South Danmarkshavn Basin are estimated to contain most of the undiscovered petroleum.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1824K","usgsCitation":"Gautier, D.L., 2019, Geology and assessment of undiscovered oil and gas resources of the East Greenland Rift Basins Province, 2008, chap. K of Moore, T.E., and Gautier, D.L., eds., The 2008 Circum-Arctic Resource Appraisal: U.S. Geological Survey Professional Paper 1824, 20 p., https://doi.org/10.3133/pp1824K.","productDescription":"Report: vii, 20 p.; Appendices 1-7","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051000","costCenters":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368214,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K"},{"id":368213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1824/k/coverthb.jpg"},{"id":368223,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appendixes.zip","text":"Appendixes","size":"150 KB","linkFileType":{"id":6,"text":"zip"},"description":"Professional Paper 1824 K"},{"id":368222,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx7.pdf","text":"Appendix 7","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Jameson Land Basin Subvolcanic Extension Assessment Unit"},{"id":368221,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx6.pdf","text":"Appendix 6","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Jameson Land Basin Assessment Unit"},{"id":368220,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx5.pdf","text":"Appendix 5","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Liverpool Land Basin Assessment Unit"},{"id":368218,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx3.pdf","text":"Appendix 3","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Northeast Greenland Volcanic Province Assessment Unit"},{"id":368217,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx2.pdf","text":"Appendix 2","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the South Danmarkshavn Basin Assessment Unit"},{"id":368216,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx1.pdf","text":"Appendix 1","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the North Danmarkshavn Salt Basin Assessment Unit"},{"id":368219,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/pp/1824/k/pp1824k_appx4.pdf","text":"Appendix 4","size":"30 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Professional Paper 1824 K","linkHelpText":"- Input data for the Thetis Basin Assessment Unit"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -21.181640624999996,\n 70.11048478105927\n ],\n [\n -31.9921875,\n 69.1312712296365\n ],\n [\n -43.505859375,\n 66.65297740055279\n ],\n [\n -46.7578125,\n 62.103882522897855\n ],\n [\n -43.33007812499999,\n 57.70414723434193\n ],\n [\n -36.03515625,\n 60.973107109199404\n ],\n [\n -21.357421875,\n 68.33437594128185\n ],\n [\n -21.181640624999996,\n 70.11048478105927\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
FAX 650-329-4936
Temporary wetlands are characterized by frequent drying resulting in a unique, highly specialized assemblage of often rare or specialized plant and animal species. They are found on all continents and in a variety of landscape settings. Although accurate estimates of the abundance of temporary wetlands are available in only a few countries, global estimations identify a decline in number and quality. The key environmental factors driving the structure of ecological communities in temporary wetlands are the duration, timing, frequency and predictability of the aquatic and dry phases, which varies greatly with region and hydrogeomorphic setting. Temporary wetlands have been historically neglected, but improved social awareness of the functions and values of, and increases in scientific interest, suggest that this is changing. They play an ecological role in both global cycles (i.e., CO2 emissions) and biodiversity (in proportion to their size, they contribute disproportionately to regional and global biodiversity). Moreover, they provide valuable ecosystem services including wildlife habitat, nutrient flux to adjacent ecosystems, flood control, water filtration, and cultural services. Effective conservation of temporary wetlands requires addressing threats (i.e., inconsistent and inadequate regulatory protections; climate change; changes in land use) and management challenges (i.e., management at both local and landscape scales; incomplete understanding of the ecosystem services provided by them; the need to enhance inventories). The most suitable approaches for conserving temporary wetlands include (1) regulations or other forms of protection; (2) sustainable management; (3) restoration and creation; and (4) collaborative conservation.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.12003-2","usgsCitation":"Boix, D., Calhoun, A.J., Mushet, D.M., Bell, K.P., Fitzsimons, J.A., and Isselin-Nondedeu, F., 2019, Conservation of temporary wetlands, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.12003-2.","ipdsId":"IP-109438","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":502628,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/chapter/Conservation_of_Temporary_Wetlands/20674152","text":"External Repository"},{"id":369820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boix, Dani","contributorId":177733,"corporation":false,"usgs":false,"family":"Boix","given":"Dani","affiliations":[],"preferred":false,"id":774855,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Aram J.K.","contributorId":177732,"corporation":false,"usgs":false,"family":"Calhoun","given":"Aram","email":"","middleInitial":"J.K.","affiliations":[{"id":13065,"text":"Department of Wildlife, Fisheries, and Conservation Biology, University of Maine","active":true,"usgs":false}],"preferred":false,"id":774856,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":774854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bell, Kathleen P.","contributorId":171584,"corporation":false,"usgs":false,"family":"Bell","given":"Kathleen","email":"","middleInitial":"P.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":774857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzsimons, James A.","contributorId":177734,"corporation":false,"usgs":false,"family":"Fitzsimons","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":774858,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isselin-Nondedeu, Francis","contributorId":177735,"corporation":false,"usgs":false,"family":"Isselin-Nondedeu","given":"Francis","email":"","affiliations":[],"preferred":false,"id":774859,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207517,"text":"70207517 - 2019 - Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system","interactions":[],"lastModifiedDate":"2019-12-21T10:37:06","indexId":"70207517","displayToPublicDate":"2019-10-09T10:35:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system","docAbstract":"The introduction of Common Carp (Cyprinus carpio) into North American waterways has led to widespread alteration of aquatic ecosystems. Control of this invader has proven extremely difficult due to its capacity for rapid population growth. To help understand how Common Carp can potentially be controlled we developed a population dynamics model (CarpMOD) to explore the efficacy of active and passive control measures that impose mortality on multiple life stages (embryos, juveniles and adults). We applied CarpMOD to Common Carp in Malheur Lake, a large shallow lake in Southeast Oregon, USA. Simulated control measures included commercial harvest of adults, trapping of juveniles, embryo electroshocking, and passive removal imposed via avian predation. Results from CarpMOD suggest that no single active removal method would decrease Common Carp biomass below the targeted 50 kg/ha threshold. Combinations of two or all three active removal methods could, however, reduce biomass below the desired threshold due to cumulative mortality on multiple life stages. CarpMOD simulations suggest that the level of carp removal necessary to reach the desired biomass threshold is approximately 40% at each life-stage, which may be unrealistic to maintain over longer time scales. Passive removal via avian predation may also contribute to suppression of Common Carp, but was not sufficient in isolation to reduce biomass below the desired threshold. Collectively, our results indicate control of Common Carp as a sole means of ecosystem restoration is unlikely to be effective in the system we modeled. This suggests additional means of restoration may be warranted, perhaps in combination with control of Common Carp, or development of more effective control measures.","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09685-0","usgsCitation":"Pearson, J.B., Dunham, J.B., Bellmore, J., and Lyons, D.E., 2019, Modeling control of Common Carp (Cyprinus carpio) in a shallow lake–wetland system: Wetlands Ecology and Management, v. 27, no. 5-6, p. 663-682, https://doi.org/10.1007/s11273-019-09685-0.","productDescription":"20 p.","startPage":"663","endPage":"682","ipdsId":"IP-106782","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":370603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Malheur Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35272216796874,\n 43.135065496929165\n ],\n [\n -118.51776123046875,\n 43.135065496929165\n ],\n [\n -118.51776123046875,\n 43.50274467820439\n ],\n [\n -119.35272216796874,\n 43.50274467820439\n ],\n [\n -119.35272216796874,\n 43.135065496929165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"5-6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearson, James B","contributorId":221480,"corporation":false,"usgs":false,"family":"Pearson","given":"James","email":"","middleInitial":"B","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":778338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":778339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bellmore, J Ryan","contributorId":178561,"corporation":false,"usgs":false,"family":"Bellmore","given":"J Ryan","affiliations":[],"preferred":false,"id":778340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, Donald E.","contributorId":204663,"corporation":false,"usgs":false,"family":"Lyons","given":"Donald","email":"","middleInitial":"E.","affiliations":[{"id":13016,"text":"Department of Fisheries and Wildlife, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":778341,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215328,"text":"70215328 - 2019 - Foraging ecology mediates response to ecological mismatch during migratory stopover","interactions":[],"lastModifiedDate":"2020-10-16T14:09:17.600138","indexId":"70215328","displayToPublicDate":"2019-10-09T09:04:15","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Foraging ecology mediates response to ecological mismatch during migratory stopover","docAbstract":"Impacts of ecological mismatches should be most pronounced at points of the annual cycle when populations depend on a predictable, abundant, and aggregated food resource that changes in timing or distribution. The degree to which species specialize on a key prey item, therefore, should determine their sensitivity to mismatches. We evaluated the hypothesis that the effects of ecological mismatch during migratory stopover are mediated by a species’ foraging ecology by comparing two similar long‐distance migratory species that differ in their foraging strategies during stopover. We predicted that a specialist foraging strategy would make species more sensitive to effects of mismatch with a historically abundant prey, while an active, generalist foraging strategy should help buffer against changing local conditions. We estimated arrival times, start of mass gain, and rate of mass gain during spring stopover in Delaware Bay, USA. At this site, shorebirds feed on a temporally aggregated food resource (horseshoe crab Limulus polyphemus eggs), the timing of which is linked to water temperature; red knot (Calidris canutus rufa) specializes on these while the ruddy turnstone (Arenaria interpres) feeds more generally. We used a hierarchical nonlinear model to estimate the effect of mismatch between shorebird arrivals and timing of crab spawning on the timing and rate of mass gain over 22 yr. In years with cooler water temperature, crabs spawned later, which was associated with later and faster mass gain for the knots. Turnstones exhibited less inter‐annual variation in the timing and rate of mass gain than knots, and we found no relationship between mass gain dynamics and the availability of horseshoe crab eggs for this generalist species. Long‐distance migrants rely on predictable resources en route and even when these linkages are simple and predictable, populations can be vulnerable to change; these results suggest that generalist foraging strategies may buffer migratory species against phenological mismatch. We provide a framework to evaluate population responses to changes in prey phenology at sites vulnerable to climatic change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2898","usgsCitation":"Tucker, A., McGowan, C.P., Catalano, M., Derose-Wilson, A., Robinson, R., and Zimmerman, J., 2019, Foraging ecology mediates response to ecological mismatch during migratory stopover: Ecosphere, v. 10, no. 10, e02898, 17 p., https://doi.org/10.1002/ecs2.2898.","productDescription":"e02898, 17 p.","ipdsId":"IP-104145","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2898","text":"Publisher Index Page"},{"id":379464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The models produced plant-level withdrawal and consumption estimates using consistent methods for 1,122 water-using, utility-scale thermoelectric power plants in the United States for 2015. Total estimated withdrawal for 2015 was about 103 billion gallons per day (Bgal/d), and total estimated consumption was about 2.7 Bgal/d. Model-estimated withdrawals decreased approximately 26 Bgal/d, or 20 percent, since 2010, and consumption decreased approximately 734 million gallons per day, or 21 percent. The decrease in thermoelectric water use between 2010 and 2015 can be attributed in part to a 7-percent decrease in total thermoelectric utility-scale electricity production, a combination of decreased electricity production and closure of coal-fired plants with once-through cooling systems, and the increase of electricity production at natural gas combined-cycle plants, which are more energy- and water-efficient than conventional thermoelectric plants.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195103","collaboration":"U.S. Geological Survey Water Availability and Use Science Program","usgsCitation":"Harris, M.A., and Diehl, T.H., 2019, Withdrawal and consumption of water by thermoelectric power plants in the United States, 2015: U.S. Geological Survey Scientific Investigations Report 2019–5103, 15 p., https://doi.org/10.3133/sir20195103.","productDescription":"Report: iv, 15 p.; Data 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