Impoundment is among the most common hydrologic alterations with impacts on aquatic ecosystems that can include effects on mercury (Hg) cycling. However, landscape-scale differences in Hg bioaccumulation between reservoirs and other habitats are not well characterized nor are the processes driving these differences. We examined total Hg (THg) concentrations of Smallmouth Bass (Micropterus dolomieu) collected from reservoir, tailrace, and free-flowing reaches along an 863 km segment of the Snake River, USA, a semiarid river with 22 impoundments along its course. Across three size-classes (putative 1-year-old, first reproductive, and harvestable sized fish), THg concentrations in reservoirs and tailraces averaged 76% higher than those in free-flowing segments. Among reservoirs, THg concentrations were highest in reservoirs with inconsistent stratification patterns, 47% higher than annually stratified, and 144% higher than unstratified reservoirs. Fish THg concentrations in tailraces immediately downstream of stratified reservoirs were higher than those below unstratified (38–130%) or inconsistently stratified (32–79%) reservoirs. Stratification regimes influenced the exceedance of fish and human health benchmarks, with 52–80% of fish from stratifying reservoirs and downstream tailraces exceeding a human consumption benchmark, compared to 6–17% where stratification did not occur. These findings suggest that impoundment and stratification play important roles in determining the patterns of Hg exposure risk across the landscape.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c04646","usgsCitation":"Willacker, J., Eagles-Smith, C., Chandler, J., Naymik, J., Myers, R., and Krabbenhoft, D.P., 2023, Reservoir stratification modulates the influence of impoundments on fish mercury concentrations along an arid land river system: Environmental Science & Technology, v. 57, no. 30, p. 21313-21326, https://doi.org/10.1021/acs.est.3c04646.","productDescription":"14 p.","startPage":"21313","endPage":"21326","ipdsId":"IP-154496","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":441470,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c04646","text":"Publisher Index Page"},{"id":435112,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94VRPSL","text":"USGS data release","linkHelpText":"Mercury in smallmouth bass from the Snake River, USA, 2013-2022"},{"id":423092,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"30","noUsgsAuthors":false,"publicationDate":"2023-12-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":221744,"corporation":false,"usgs":true,"family":"Willacker","given":"James","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":889218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":889219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandler, Jim","contributorId":332006,"corporation":false,"usgs":false,"family":"Chandler","given":"Jim","email":"","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":889220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Naymik, Jesse","contributorId":229386,"corporation":false,"usgs":false,"family":"Naymik","given":"Jesse","affiliations":[{"id":41632,"text":"Idaho Power Company","active":true,"usgs":false}],"preferred":false,"id":889221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Myers, Ralph","contributorId":172701,"corporation":false,"usgs":false,"family":"Myers","given":"Ralph","email":"","affiliations":[{"id":12541,"text":"Idaho Power Company, P.O. Box 70, Boise ID 83707","active":true,"usgs":false}],"preferred":false,"id":889222,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":889223,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250427,"text":"70250427 - 2023 - Long-term mercury loading and trapping dynamics in a Western North America reservoir","interactions":[],"lastModifiedDate":"2023-12-08T12:37:38.711686","indexId":"70250427","displayToPublicDate":"2023-12-02T06:34:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Long-term mercury loading and trapping dynamics in a Western North America reservoir","docAbstract":"The Carson River including Lahontan Reservoir in Northwestern Nevada, USA
The discovery, mining, and processing of silver and gold from the Comstock Lode in northwestern Nevada heavily contaminated the Carson River system with mercury (Hg). The river now contains some of the highest recorded water column and bed sediment Hg concentrations reported in peer-reviewed literature. Acute Hg contamination in river and reservoir bed sediment has led to elevated methylmercury (MeHg) concentrations across all trophic levels of Lahontan Reservoir, culminating in significant health risks to humans. Lahontan Reservoir, located downstream from the mills that processed Comstock ore, has served as a Hg trap since the completion of the dam in 1915. Starting in 1997 and extending through 2021, the U.S. Geological Survey has collected and analyzed hundreds of discrete water samples entering and exiting Lahontan Reservoir for unfiltered total Hg (uf.THg), thereby providing a first-of-its-kind opportunity for studying long-term Hg trapping efficiencies within a western North American reservoir. Continuous time series of uf.THg concentration and flux above and below the reservoir were estimated using the weighted regressions on time, discharge, and season with the Kalman filtering (WRTDS-K) method employed with newly developed methods to account for non-natural (i.e., managed) hydrographs below a reservoir.
An estimated 31,650 kg (34.89 US tons) of uf.THg entered Lahontan Reservoir during the 25 year period of analysis, accounting for approximately 0.5% of the total uf.THg estimated to have been released to the Carson River system [6.8 million kg (7500 US tons)] over a multi-decade mining boom. Moreover, approximately 92% of the estimated uf.THg entering Lahontan Reservoir was trapped. On an annual basis, however, trapping efficiencies range between 34% and 98%, and are closely related to the total annual discharge. Results also indicate that flow-normalized uf.THg concentrations and loads above and below the reservoir are trending down.
A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad-scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a (Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.
","language":"English","publisher":"Wiley","doi":"10.1002/lno.12457","usgsCitation":"Lapierre, J., Webster, K.E., Hanks, E., Wagner, T., Soranno, P.A., McCullough, I., Reinl, K.L., Domka, M., and Lotting, N.R., 2023, A continuous classification of the 476,697 lakes of the conterminous US based on geographic archetypes: Limnology and Oceanography, v. 69, no. 12, p. 2759-2773, https://doi.org/10.1002/lno.12457.","productDescription":"15 p.","startPage":"2759","endPage":"2773","ipdsId":"IP-145952","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441521,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.12457","text":"Publisher Index Page"},{"id":433110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"69","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-11-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Lapierre, Jean-Francois","contributorId":264522,"corporation":false,"usgs":false,"family":"Lapierre","given":"Jean-Francois","affiliations":[{"id":54487,"text":"University of Montreal","active":true,"usgs":false}],"preferred":false,"id":910126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":910127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim","contributorId":332094,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soranno, Patricia A.","contributorId":264518,"corporation":false,"usgs":false,"family":"Soranno","given":"Patricia","email":"","middleInitial":"A.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":910130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCullough, Ian M.","contributorId":340909,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":910131,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reinl, Kaitlin L.","contributorId":342483,"corporation":false,"usgs":false,"family":"Reinl","given":"Kaitlin","email":"","middleInitial":"L.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":910132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Domka, Marcella","contributorId":342486,"corporation":false,"usgs":false,"family":"Domka","given":"Marcella","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":910133,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lotting, Noah R.","contributorId":172183,"corporation":false,"usgs":false,"family":"Lotting","given":"Noah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":910134,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250206,"text":"70250206 - 2023 - At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution","interactions":[],"lastModifiedDate":"2023-11-28T12:55:24.267682","indexId":"70250206","displayToPublicDate":"2023-11-28T06:52:19","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution","docAbstract":"Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a longstanding challenge and a particular impediment to automated analysis. This paper demonstrates that sinuosity can vary with the magnitude of units at which it is measured (measurement scales). The paper derives a scale-specific sinuosity (S3) metric intended to measure stream bend geometry across a range of measurement scales. The metric is warranted for analysis and modeling at measurement scales equal or similar to the spatial process and landscape conditions under investigation. Derived from the Richardson (1961) plot, the S3 metric quantifies a frequency signature of planform bend sizes spanning a range of measurement units, enabling visual and quantitative analysis of bend geometry in linear stream features. Derivation of the metric makes evident a systematic relationship between stream bend size, sinuosity, and measurement scale, formalizing a relationship between sinuosity and fractal dimension. The paper shows the utility of the S3 metric in examining bend patterns for synthetic and real-world linear stream data.
Northern high-latitude lakes are critical sites for carbon processing and serve as potential conduits for the emission of permafrost-derived carbon and greenhouse gases. However, the fate and emission pathways of permafrost carbon in these systems remain uncertain. Here, we used the natural abundance of radiocarbon to identify and trace the predominant sources of methane, carbon dioxide, dissolved inorganic and organic carbon in nine lakes within the Yukon Flats National Wildlife Refuge in interior Alaska, a discontinuous permafrost region with high landscape heterogeneity and susceptibility to climate, permafrost, and hydrological changes. We find that although Yukon Flats lakes primarily process young carbon (modern to 1290 ± 60 years before present), permafrost-derived carbon is present in some of the sampled lakes and contributes, at most, 30 ± 10% of the dissolved carbon in lake surface waters. Apportionment of young carbon and legacy carbon (carbon with radiocarbon age ⩾5000 years before present) is decoupled among the dissolved inorganic and organic carbon species, with methane showing a stronger legacy signature. Our observations suggest that permafrost-thaw-related transport of carbon through Yukon Flats lacustrine ecosystems and into the atmosphere is small, and likely regulated by surficial sediments, permafrost distribution, wildfire occurrence, or masked by contemporary carbon processes. The heterogeneity of lakes across our study area and northern landscapes more broadly cautions against using any one region (e.g. Yedoma permafrost lakes) to upscale their contribution across the pan-Arctic.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ad0993","usgsCitation":"Garcia-Tigreros, F., Elder, C.D., Kurek, M.R., Miller, B.L., Xu, X., Wickland, K., Czimczik, C.I., Dornblaser, M.M., Striegl, R.G., Kyzivat, E.D., Smith, L., Spencer, R., Miller, C.E., and Butman, D., 2023, Arctic-boreal lakes of interior Alaska dominated by contemporary carbon: Environmental Research Letters, v. 18, no. 12, 124024, 11 p., https://doi.org/10.1088/1748-9326/ad0993.","productDescription":"124024, 11 p.","ipdsId":"IP-148398","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":441558,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0993","text":"Publisher Index Page"},{"id":422881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Boot Lake, Canvasback Lake, Greenpepper Lake, Shack Lake, Thumb Lake, Twelvemile Lake, Twin Lake, West Crazy Lake, YF18 Lake, Yukon Flats National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.32495243819622,\n 65.09586209258472\n ],\n [\n -149.03799309568313,\n 64.59463623905711\n ],\n [\n -143.9761424480567,\n 65.63219990838482\n ],\n [\n -141.00690728046249,\n 66.43516910407672\n ],\n [\n -140.9727162057442,\n 68.73213031511489\n ],\n [\n -148.19579980608407,\n 67.27696483824582\n ],\n [\n -153.60488278174276,\n 67.04326208119708\n ],\n [\n -152.32495243819622,\n 65.09586209258472\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia-Tigreros, Fenix 0000-0001-8694-9046","orcid":"https://orcid.org/0000-0001-8694-9046","contributorId":194744,"corporation":false,"usgs":false,"family":"Garcia-Tigreros","given":"Fenix","email":"","affiliations":[],"preferred":false,"id":888627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elder, Clayton D.","contributorId":201542,"corporation":false,"usgs":false,"family":"Elder","given":"Clayton","email":"","middleInitial":"D.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":888628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kurek, Martin R.","contributorId":300567,"corporation":false,"usgs":false,"family":"Kurek","given":"Martin","email":"","middleInitial":"R.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":888629,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Benjamin L.","contributorId":331727,"corporation":false,"usgs":false,"family":"Miller","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":888630,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":888631,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly 0000-0002-6400-0590","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":208471,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":888632,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Czimczik, Cluadia I.","contributorId":331728,"corporation":false,"usgs":false,"family":"Czimczik","given":"Cluadia","email":"","middleInitial":"I.","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":888633,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dornblaser, Mark M.","contributorId":300296,"corporation":false,"usgs":false,"family":"Dornblaser","given":"Mark","email":"","middleInitial":"M.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":888634,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":888635,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kyzivat, Ethan D.","contributorId":300572,"corporation":false,"usgs":false,"family":"Kyzivat","given":"Ethan","email":"","middleInitial":"D.","affiliations":[{"id":16929,"text":"Brown University","active":true,"usgs":false}],"preferred":false,"id":888636,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Smith, Laurence C.","contributorId":169004,"corporation":false,"usgs":false,"family":"Smith","given":"Laurence C.","affiliations":[{"id":13022,"text":"Department of Geography, University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":888637,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Spencer, Robert G.M.","contributorId":173304,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert G.M.","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":888638,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Miller, Charles E.","contributorId":270237,"corporation":false,"usgs":false,"family":"Miller","given":"Charles","email":"","middleInitial":"E.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":888639,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Butman, David","contributorId":224754,"corporation":false,"usgs":false,"family":"Butman","given":"David","affiliations":[{"id":16962,"text":"U. Washington","active":true,"usgs":false}],"preferred":false,"id":888640,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70250186,"text":"70250186 - 2023 - Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","interactions":[],"lastModifiedDate":"2023-11-28T12:52:16.525451","indexId":"70250186","displayToPublicDate":"2023-11-23T06:48:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","docAbstract":"Sediment connectivity is a conceptualization for the transfer and storage of sediment among different geomorphic compartments across upland landscapes and channel networks. Sediment connectivity and dysconnectivity are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review current sediment connectivity and modeling approaches evaluating and quantifying water and sediment transfer in catchment systems. Many studies highlight the interaction between sediment and water in defining landscape connectivity, but many efforts to quantify and/or simulate sediment connectivity rely on the topographic/structural controls on sediment erosion and delivery. More recent modeling efforts integrate functional and structural connectivity to capture hydrologic properties influencing sediment delivery. Though the recent modeling development is encouraging, a comprehensive sediment connectivity framework, which integrates geomorphic and hydrologic processes across spatiotemporal scales, has not yet been accomplished. Such an effort requires understanding the hydrologic and geomorphic processes that control sediment source, storage, and transport at different spatiotemporal scales and across various geophysical conditions. We propose a path for developing this new understanding through an integrated hydrologic and sediment connectivity conceptual model that broadly categorizes dominant processes and patterns relevant to understanding sediment flux dynamics. The conceptual model describes hydrologic–sediment connectivity regimes through spatial-temporal feedback between hydrologic processes and geomorphic drivers. We propose that in combining hydrologic and sediment connectivity into a single conceptual model, patterns emerge such that catchments will exist in a single characteristic behavior at a particular instance, which would shift with space and time, and with landscape disturbances. Using the conceptual model as a “thinking” tool, we extract case studies from a multidisciplinary literature review—from hydrology, geomorphology, biogeochemistry, and watershed modeling to remote-sensing technology—that correspond to each of the dominant hydrologic–sediment connectivity regimes. Sediment and water interactions in real-world examples through various observational and modeling techniques illustrate the advancements in the spatial and temporal scales of landscape connectivity observations and simulations. The conceptual model and case studies provide a foundation for advancing the understanding and predictive capability of watershed sediment processes at multiple spatiotemporal scales. Plain language summary: Soil erosion and movement across the landscape are closely linked to rain events and flow pathways. Landscape connectivity is a way to consider how soil erosion from different parts of the landscape is connected to the streams. We explore where soil erosion occurs and how eroded soil moves across the landscape through the interaction with rainfall and drainage. The comprehensive understanding of sediment connectivity and its dependence on rainfall characteristics and watershed hydrology may help to inform the effective distribution of conservation funds and management actions to address water pollution from excess sediment.
In coastal aquifers, dynamic stresses such as climate forcings, groundwater withdrawals, and ocean tidal fluctuations cause nonlinear responses to groundwater levels. Such responses to the stresses impact groundwater resources and related flooding and infrastructure risks at multiple scales. We used time-series models such as transfer-function models and wavelet analysis to quantify the relative contribution of these stresses to groundwater-level fluctuation in wells from the unconfined and confined aquifers in an Atlantic coastal aquifer. Climate forcings, such as precipitation and temperature, explained most of the groundwater-level variation for wells in the unconfined aquifer, whereas groundwater withdrawals were the dominant driver of groundwater levels for wells in the confined aquifer. The impact of groundwater withdrawals also was detected in several wells in the unconfined aquifer. Although the influence of ocean tides on groundwater levels commonly is observed in coastal aquifers, we found that daily groundwater withdrawals can obscure the semi-diurnal coherence signal of the two series. The magnitude of groundwater-level fluctuation that could be explained solely by tides was minor compared to that explained by climate or withdrawal stresses. Transfer-function modeling showed seasonal withdrawals from wells in confined aquifers had a significant, yet heterogeneous influence on groundwater levels in coastal aquifers, which highlights climate and withdrawals as key compounding stresses in coastal hydrology. This study demonstrates the value of time-series approaches to advance characterization of groundwater systems in areas with limited hydrogeologic parameter information.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2023.130426","usgsCitation":"Yang, G., and McCoy, K., 2023, Modeling groundwater-level responses to multiple stresses using transfer-function models and wavelet analysis in a coastal aquifer system: Journal of Hydrology, v. 627, no. Part B, 130426, 12 p., https://doi.org/10.1016/j.jhydrol.2023.130426.","productDescription":"130426, 12 p.","ipdsId":"IP-150305","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":441582,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2023.130426","text":"Publisher Index Page"},{"id":424621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","city":"Virginia Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.26319664375336,\n 37.04695559476376\n ],\n [\n -76.26319664375336,\n 36.59981472352801\n ],\n [\n -75.83944187250752,\n 36.59981472352801\n ],\n [\n -75.83944187250752,\n 37.04695559476376\n ],\n [\n -76.26319664375336,\n 37.04695559476376\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"627","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yang, Guoxiang 0000-0001-5587-3683","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":267279,"corporation":false,"usgs":false,"family":"Yang","given":"Guoxiang","affiliations":[{"id":55459,"text":"NSA Contractor to USGS VA and WV WSC","active":true,"usgs":false}],"preferred":false,"id":892914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCoy, Kurt J. 0000-0002-9756-8238","orcid":"https://orcid.org/0000-0002-9756-8238","contributorId":216196,"corporation":false,"usgs":true,"family":"McCoy","given":"Kurt J.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892915,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250099,"text":"sir20235066 - 2023 - Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013","interactions":[],"lastModifiedDate":"2026-03-09T16:53:50.063749","indexId":"sir20235066","displayToPublicDate":"2023-11-17T13:55:00","publicationYear":"2023","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":"2023-5066","displayTitle":"Updates to the Regional Groundwater-Flow Model of the New Jersey Coastal Plain, 1980–2013","title":"Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013","docAbstract":"A 21-layer three-dimensional transient groundwater-flow model of the New Jersey Coastal Plain was developed and calibrated by the U.S. Geological Survey (USGS) in cooperation with the New Jersey Department of Environmental Protection to simulate groundwater-flow conditions during 1980–2013, incorporating average annual groundwater withdrawals and average annual groundwater recharge. This model is the third version of the New Jersey Coastal Plain regional groundwater-flow model that was initially developed as part of the USGS Regional Aquifer System Analysis (RASA) program. The model simulates groundwater flow in 11 aquifers and 10 intervening confining units of the New Jersey Coastal Plain to provide a regional overview of groundwater conditions. Averaged groundwater withdrawal data for 1980 to 2013 were used in the model. The 11 aquifers in New Jersey are, from shallowest to deepest, the Holly Beach water-bearing zone and the confined Cohansey aquifer in Cape May County; the Rio Grande water-bearing zone; the Atlantic City 800-foot sand; the Piney Point, Vincentown, and Wenonah-Mount Laurel aquifers; the Englishtown aquifer system; and the upper, middle, and lower aquifers of the Potomac-Raritan-Magothy (PRM) aquifer system.
The model was developed with the MODFLOW–2005 numerical code and the UCODE parameter estimation technique and calibrated using water-level and base-flow observations. A total of 3,453 water-level observations from 392 wells in New Jersey and 48 wells in Delaware from 1983 to 2013 were used in model calibration, which includes historical water-level trends for 29 wells in New Jersey during 1980–2013 presented in time-series hydrographs. In addition, derived observations also were included by calculating the vertical gradient at 33 pairs of nested observation wells in New Jersey, for a total of 210 observations. Changes in water levels over time were calculated for 134 wells in New Jersey and four wells in Delaware where water levels had varied substantially (approximately 10 ft) over the 30-year span of synoptic water-level measurements, for a total of 767 observations. A total of 1,485 base-flow observations in 47 surface-water basins in New Jersey from 1980 to 2013 were used in model calibration.
Updates to the groundwater-flow model include the conversion to a fully three-dimensional model from the previous quasi-three-dimensional model. The new model will allow for potential future uses such as particle tracking or simulation of variable-density groundwater flow that could not be accomplished with earlier versions of the model. Spatially and temporally variable recharge estimated by using a soil-water balance model resulted in a spatially and temporally finer discretization. The Rio Grande water-bearing zone was added to the model as an aquifer layer to refine estimates of simulated flow in Atlantic and Cape May Counties, New Jersey. Hydrogeologic parameters were updated to include the confining units in New Jersey and corresponding hydrogeologic units in Delaware and eastern Maryland.
The simulated water levels for the New Jersey Coastal Plain aquifers were compared to water-level measurements made during 1980–2013. The average residual for 4,243 water-level observations for New Jersey (simulated water levels minus measured water levels) is 1.5 feet. The simulated water-level contours for the confined aquifers for 2013 were compared to potentiometric surfaces produced from water levels measured during 2013. Simulated water levels generally matched the 2013 potentiometric surfaces of the confined aquifers in the areas of large withdrawals. Hydrographs of wells in the confined Coastal Plain aquifers of New Jersey show that simulated water levels generally match the magnitude and seasonal variation of the observed water levels. Hydrographs of base flow for the 47 streamgaging stations in New Jersey indicate that most of the simulated and estimated data match reasonably well.
Groundwater withdrawals are an important resource for water supply, agricultural, industrial, and commercial needs in the New Jersey Coastal Plain. Groundwater withdrawals from the New Jersey Coastal Plain aquifers have resulted in persistent, regionally extensive cones of depression in the Englishtown aquifer system and Wenonah-Mount Laurel aquifer in Ocean and Monmouth Counties; Wenonah-Mount Laurel and upper, middle, and lower PRM aquifers in Camden County; and Atlantic City 800-foot sand in Atlantic County. Because hydrologic stresses and water-management needs change with time, periodic updates to the groundwater-flow model are required to provide current information about hydrologic conditions in the New Jersey Coastal Plain and to maintain its usefulness as a tool to manage water resources and develop water-resource strategies. The current updates will support the continued application of this model as a tool for evaluating the regional effects of changes in groundwater withdrawals and of current and potential future water-management strategies on groundwater levels in the New Jersey Coastal Plain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235066","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Gordon, A.D., and Carleton, G.B., 2023, Updates to the regional groundwater-flow model of the New Jersey Coastal Plain, 1980–2013: U.S. Geological Survey Scientific Investigations Report 2023–5066, 116 p., https://doi.org/10.3133/sir20235066","productDescription":"Report: xii, 116 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127396","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500947,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115639.htm","linkFileType":{"id":5,"text":"html"}},{"id":422695,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5066/images/"},{"id":422693,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235066/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5066"},{"id":422696,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6RXFC","text":"USGS data release","linkHelpText":"MODFLOW-2005 model used to simulate the regional groundwater flow system in the updated New Jersey Coastal Plain model, 1980-2013"},{"id":422694,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.XML"},{"id":422692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5066/sir20235066.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5066"},{"id":422691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5066/coverthb.jpg"}],"country":"United States","otherGeospatial":"New Jersey Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.49018324613056,\n 41.03712838002892\n ],\n [\n -75.25922621488034,\n 41.417217443631785\n ],\n [\n -77.41254652738019,\n 39.17183412365296\n ],\n [\n -75.22626723050551,\n 37.8132834585617\n ],\n [\n -72.98505629300531,\n 40.4043207917766\n ],\n [\n -74.49018324613056,\n 41.03712838002892\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
3450 Princeton Pike, Suite 110
Lawrenceville, New Jersey 08648
The 2012 Little Bear Fire caused substantial vegetation loss in the Eagle Creek Basin of south-central New Mexico. This loss was expected to alter the localized hydrologic response to precipitation by creating conditions that amplify surface runoff, which might alter the geomorphology of North Fork Eagle Creek, a major tributary to Eagle Creek. To monitor short-term geomorphic change, annual geomorphic surveys of North Fork Eagle Creek were conducted from 2017 to 2021. The surveys measured 14 cross sections, stream gradients, woody debris accumulations, and pools found within the study reach. During the 2017–21 study period, the study reach experienced multiple high-flow events that resulted from both monsoonal rainfall and snowmelt runoff. Comparisons of the cross-section and channel profile data for the repeat geomorphic surveys indicate localized erosion and deposition occurred as a result of the high-flow events but overall study reach geomorphology shower little change through the study period. Additionally, the number of woody debris accumulations and pools increased during the study period. Evidence from the 5-year geomorphic survey indicates that the North Fork Eagle Creek’s geomorphology did not change substantially during the study period. Wildfire severity and frequency within mountainous regions of the Southwest are projected to increase and their effect on fluvial systems remains uncertain; however, continued geomorphic studies can provide informative insight on watershed post-wildfire resiliency and recovery by establishing baselines that can be used in the event of a future severe wildfire within the Eagle Creek Basin.
Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Sustainable management of groundwater resources highly relies on the accurate estimation of recharge. However, accurate recharge estimation is a challenge, especially in data-scarce regions, as the existing models are data-intensive and require extensive parameterization. This study developed a process-based hydrologic model combining local and remotely sensed data for characterizing recharge in data-limited regions using a Basin Characterization Model (BCM). This study was conducted in Raya and Kobo Valleys, a semi-arid region in Northern Ethiopia, considering both the structural basin and the surrounding mountainous recharge areas. Climatic Research Unit monthly datasets for 1991 to 2020 and WaPOR actual evapotranspiration data were used. The model results show that the average annual recharge and surface runoff from 1991 to 2020 were 73 mm and 167 mm, respectively, with a substantial portion contributed along the front of the mountainous parts of the study area. The mountainous recharge occurred along and above the valleys as mountain-block and mountain-front recharge. The long-term estimates of the monthly recharge time series indicated that the water balance components follow the temporal pattern of rainfall amount. However, the relation of recharge to precipitation was nonlinearly related, showing the episodic nature of recharge in semi-arid regions. This study informed the spatial and temporal distribution of recharge and runoff hydrologic variables at fine spatial scales for each grid cell, allowing results to be summarized for various planning units, including farmlands. One third of the precipitation in the drainage basin becomes recharge and runoff, while the remaining is lost through evapotranspiration. The current study’s findings are vital for developing plans for sustainable management of water resources in semi-arid regions. Also, monthly groundwater withdrawals for agriculture should be regulated in relation to spatial and temporal recharge patterns. We conclude that combining scarce local data with global datasets and tools is a useful approach for estimating recharge to manage groundwater resources in data-scarce regions.
","language":"English","publisher":"MDPI","doi":"10.3390/su152215887","usgsCitation":"Mekonen, S.S., Boyce, S.E., Mohammed, A.K., Flint, L.E., Flint, A., and Disse, M., 2023, Recharge estimation approach in a data-scarce semi-arid region, Northern Ethiopian Rift Valley: Sustainability, v. 15, no. 22, 15887, 25 p., https://doi.org/10.3390/su152215887.","productDescription":"15887, 25 p.","ipdsId":"IP-146940","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su152215887","text":"Publisher Index Page"},{"id":426968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia","otherGeospatial":"Kobo Valley, Riya Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.36,\n 12.88\n ],\n [\n 39.36,\n 11.92\n ],\n [\n 39.84,\n 11.92\n ],\n [\n 39.84,\n 12.88\n ],\n [\n 39.36,\n 12.88\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"22","noUsgsAuthors":false,"publicationDate":"2023-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Mekonen, Sisay Simachew","contributorId":333048,"corporation":false,"usgs":false,"family":"Mekonen","given":"Sisay","email":"","middleInitial":"Simachew","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897192,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897193,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, Abdella K.","contributorId":333049,"corporation":false,"usgs":false,"family":"Mohammed","given":"Abdella","email":"","middleInitial":"K.","affiliations":[{"id":79718,"text":"Hydraulic and Water Resources Engineering, Arba Minch University","active":true,"usgs":false}],"preferred":false,"id":897194,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flint, Lorraine E. 0000-0002-7868-441X","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":306090,"corporation":false,"usgs":false,"family":"Flint","given":"Lorraine","email":"","middleInitial":"E.","affiliations":[{"id":66369,"text":"Earth Knowledge, Inc.","active":true,"usgs":false}],"preferred":false,"id":897195,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flint, Alan L 0000-0002-5118-751X","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":239656,"corporation":false,"usgs":false,"family":"Flint","given":"Alan L","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":897196,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Disse, Markus","contributorId":333050,"corporation":false,"usgs":false,"family":"Disse","given":"Markus","email":"","affiliations":[{"id":79717,"text":"Hydrology and River Basin Management Department, Technical University of Munich","active":true,"usgs":false}],"preferred":false,"id":897197,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250090,"text":"70250090 - 2023 - Linking meso-scale spatial variation in methylmercury production to bioaccumulation in tidal marsh food webs","interactions":[],"lastModifiedDate":"2023-12-21T14:34:06.041328","indexId":"70250090","displayToPublicDate":"2023-11-13T06:40:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Linking meso-scale spatial variation in methylmercury production to bioaccumulation in tidal marsh food webs","docAbstract":"Differences in sediment biogeochemistry among tidal marsh features with different hydrological and geomorphological characteristics, including marsh interiors, marsh edges, first-order channels, and third-order channels, can result in spatial variation in MeHg production and availability. To better understand the link between MeHg production in sediments and bioaccumulation in primary and secondary consumer invertebrates and fish, we characterized mesoscale spatial variation in sediment biogeochemistry and MeHg concentrations of sediments, water, and consumer tissues among marsh features. Our results indicated that marsh interiors had biogeochemical conditions, including greater concentrations of organic matter and sulfate reduction rates, that resulted in greater MeHg concentrations in sediments and surface water particulates from marsh interiors compared to other features. Tissue MeHg concentrations of consumers also differed among features, with greater concentrations from marsh edges and interiors compared to channels. This spatial mismatch of MeHg concentrations in sediments and water compared to those in consumers may have resulted from differences in behavior and physiology among consumers that influenced the spatial scale over which MeHg was integrated into tissues. Our results highlight the importance of sampling across a suite of marsh features and considering the behavioral and physiological traits of sentinel taxa for contaminant monitoring studies.
Climate change affects cryosphere-fed rivers and alters seasonal sediment dynamics, affecting cyclical fluvial material supply and year-round water-food-energy provisions to downstream communities. Here, we demonstrate seasonal sediment-transport regime shifts from the 1960s to 2000s in four cryosphere-fed rivers characterized by glacial, nival, pluvial, and mixed regimes, respectively. Spring sees a shift toward pluvial-dominated sediment transport due to less snowmelt and more erosive rainfall. Summer is characterized by intensified glacier meltwater pulses and pluvial events that exceptionally increase sediment fluxes. Our study highlights that the increases in hydroclimatic extremes and cryosphere degradation lead to amplified variability in fluvial fluxes and higher summer sediment peaks, which can threaten downstream river infrastructure safety and ecosystems and worsen glacial/pluvial floods. We further offer a monthly-scale sediment-availability-transport model that can reproduce such regime shifts and thus help facilitate sustainable reservoir operation and river management in wider cryospheric regions under future climate and hydrological change.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adi5019","usgsCitation":"Zhang, T., Li, D., East, A.E., Kettner, A.J., Best, J., Ni, J., and Lu, X., 2023, Shifted sediment-transport regimes by climate change and amplified hydrological variability in cryosphere-fed rivers: Science Advances, v. 9, no. 45, eadi5019, 12 p., https://doi.org/10.1126/sciadv.adi5019.","productDescription":"eadi5019, 12 p.","ipdsId":"IP-147639","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adi5019","text":"Publisher Index Page"},{"id":422725,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Tinghu","contributorId":210005,"corporation":false,"usgs":false,"family":"Zhang","given":"Tinghu","email":"","affiliations":[],"preferred":false,"id":888409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Dongfeng","contributorId":297068,"corporation":false,"usgs":false,"family":"Li","given":"Dongfeng","email":"","affiliations":[{"id":64287,"text":"National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":888410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kettner, Albert J.","contributorId":331669,"corporation":false,"usgs":false,"family":"Kettner","given":"Albert","email":"","middleInitial":"J.","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":888412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Best, James L.","contributorId":331670,"corporation":false,"usgs":false,"family":"Best","given":"James L.","affiliations":[{"id":35161,"text":"University of Illinois, Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":888413,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ni, Jinren","contributorId":331671,"corporation":false,"usgs":false,"family":"Ni","given":"Jinren","email":"","affiliations":[{"id":79261,"text":"Peking University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":888414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lu, Xixi","contributorId":298889,"corporation":false,"usgs":false,"family":"Lu","given":"Xixi","email":"","affiliations":[{"id":64287,"text":"National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":888415,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250689,"text":"70250689 - 2023 - Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere","interactions":[],"lastModifiedDate":"2023-12-27T12:49:16.223157","indexId":"70250689","displayToPublicDate":"2023-11-08T06:46:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7120,"text":"Limnology & Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Response of lake metabolism to catchment inputs inferred using high-frequency lake and stream data from across the northern hemisphere","docAbstract":"In lakes, the rates of gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) are often controlled by resource availability. Herein, we explore how catchment vs. within lake predictors of metabolism compare using data from 16 lakes spanning 39°N to 64°N, a range of inflowing streams, and trophic status. For each lake, we combined stream loads of dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP) with lake DOC, TN, and TP concentrations and high frequency in situ monitoring of dissolved oxygen. We found that stream load stoichiometry indicated lake stoichiometry for C : N and C : P (r2 = 0.74 and r2 = 0.84, respectively), but not for N : P (r2 = 0.04). As we found a strong positive correlation between TN and TP, we only used TP in our statistical models. For the catchment model, GPP and R were best predicted by DOC load, TP load, and load N : P (R2 = 0.85 and R2 = 0.82, respectively). For the lake model, GPP and R were best predicted by TP concentrations (R2 = 0.86 and R2 = 0.67, respectively). The inclusion of N : P in the catchment model, but not the lake model, suggests that both N and P regulate metabolism and that organisms may be responding more strongly to catchment inputs than lake resources. Our models predicted NEP poorly, though it is unclear why. Overall, our work stresses the importance of characterizing lake catchment loads to predict metabolic rates, a result that may be particularly important in catchments experiencing changing hydrologic regimes related to global environmental change.
Changes in groundwater quality have been evaluated for more than 2,200 wells in 25 Principal Aquifers in the United States based on repeated decadal sampling (once every 10 years) from 1988 to 2021. The purpose of this study is to identify contaminants with changing concentrations, the locations and magnitude of those changes, the factors driving those changes, the obstacles to interpreting the changes, and approaches to ameliorate those obstacles. Sampling was conducted in 89 networks of 20–30 wells each that represent various geographic regions, aquifer types and land use types. Each network, and the wells that comprise them, are sampled on a rotating basis once every 10 years. Of the 28 constituents evaluated for trends, concentrations of Na, Cl, dissolved solids, SO4, and NO3 exhibited statistically significant increases at the network level more frequently than other constituents. Factors affecting trends in Cl and NO3 are emphasized in this study. Regional patterns show large increases of Cl in urban areas in the Northeast and Midcontinent, where road-deicing salt application rates are 10 to 100 times greater than in other regions of the country, and in semiarid and arid regions of the western United States, where evaporation concentrates solutes in recharge. The largest increases in NO3 were in agricultural areas of the semiarid west, arid west and Pacific regions which are characterized by oxic groundwater, long-term increases in nitrogen fertilizer usage, and high rates of irrigation. However, finding a direct relation between increasing contaminant sources and corresponding groundwater quality response, particularly when sampling once every 10 years, can be complicated by factors such as uncertainty in the timing, mass, and location of contaminant sources, groundwater residence time (recharge date), geochemical conditions in the aquifer that affect contaminant transport, and variability in water quality due to climatic factors such as seasonality and hydrologic conditions. Understanding groundwater residence time allows the changes in groundwater quality to be evaluated in the context of recharge date rather than the sample date. Likewise, information on geochemical characteristics of the aquifer can be helpful for understanding relations between contaminant source inputs and groundwater concentrations. For example, oxic geochemical conditions in the aquifer may allow for conservative transport and accumulation of NO3 in groundwater, whereas reducing environments could favor NO3 degradation. Differences in hydrologic conditions (wetter or drier than average) on the date of sampling could impact the statistical results of sampling at decadal intervals and obscure long-term patterns. Samples collected under substantially different hydrologic conditions can be identified, and high-frequency sampling can improve interpretation of measured results in these cases. Although decadal sampling and associated water-quality interpretations have limitations, repeated, scheduled sampling of thousands of wells over multiple decades has great value for identifying and understanding long-term, regional groundwater-quality trends. Despite these limitations, the concepts presented herein provide options that could be used to interpret trends or changes when sampling over longer timespans, which is less common than trend networks with higher frequency sampling intervals.
Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.118744","usgsCitation":"He, X., Andreadisa, K., Roy, A.H., Kumar, A., and Butler, C., 2023, Developing a stochastic hydrological model for informing lake water level drawdown management: Journal of Environmental Management, v. 345, 118744, 13 p., https://doi.org/10.1016/j.jenvman.2023.118744.","productDescription":"118744, 13 p.","ipdsId":"IP-153469","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433010,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-70.827398,41.602067],[-70.823735,41.598569],[-70.820918,41.587673],[-70.821001,41.587268],[-70.821743,41.583656],[-70.82191,41.582841],[-70.830087,41.585385],[-70.837632,41.595374],[-70.838147,41.596056],[-70.838452,41.59646],[-70.834529,41.60261],[-70.832044,41.606504],[-70.831802,41.606272],[-70.828025,41.602666],[-70.827398,41.602067]]],[[[-70.59628,41.471905],[-70.57485,41.468259],[-70.567356,41.471208],[-70.56328,41.469127],[-70.553277,41.452955],[-70.552943,41.443394],[-70.555588,41.430882],[-70.553096,41.423952],[-70.547567,41.415831],[-70.538301,41.409241],[-70.528581,41.4051],[-70.517584,41.403769],[-70.506984,41.400242],[-70.502372,41.392005],[-70.501306,41.385391],[-70.498959,41.384339],[-70.490758,41.383634],[-70.484503,41.38629],[-70.472604,41.399128],[-70.473035,41.408757],[-70.470788,41.412875],[-70.463833,41.419145],[-70.450431,41.420703],[-70.446233,41.39648],[-70.449268,41.380422],[-70.448262,41.353651],[-70.451084,41.348161],[-70.496162,41.346452],[-70.538294,41.348958],[-70.599157,41.349272],[-70.709826,41.341723],[-70.733253,41.336226],[-70.747541,41.329952],[-70.764188,41.318706],[-70.768015,41.311959],[-70.766166,41.308962],[-70.768687,41.303702],[-70.775665,41.300982],[-70.802083,41.314207],[-70.819415,41.327212],[-70.838777,41.347209],[-70.833802,41.353386],[-70.812309,41.355745],[-70.800289,41.3538],[-70.783291,41.347829],[-70.774974,41.349176],[-70.768901,41.353246],[-70.729225,41.397728],[-70.724366,41.398942],[-70.712432,41.40885],[-70.711493,41.41546],[-70.701378,41.430925],[-70.686881,41.441334],[-70.64933,41.461068],[-70.603555,41.482384],[-70.598444,41.481151],[-70.59628,41.471905]]],[[[-70.092142,41.297741],[-70.082072,41.299093],[-70.062565,41.308726],[-70.046088,41.321651],[-70.031332,41.339332],[-70.028805,41.359919],[-70.030924,41.367453],[-70.035162,41.372161],[-70.038458,41.376399],[-70.045586,41.383598],[-70.049564,41.3879],[-70.049053,41.391702],[-70.033514,41.385816],[-70.018446,41.36863],[-69.960277,41.278731],[-69.960181,41.264546],[-69.964422,41.25457],[-69.965725,41.252466],[-69.975,41.247392],[-70.001586,41.239353],[-70.015225,41.237964],[-70.052807,41.242685],[-70.083239,41.2444],[-70.096967,41.24085],[-70.118669,41.242351],[-70.170681,41.255881],[-70.237175,41.282724],[-70.256164,41.288123],[-70.266776,41.294453],[-70.273478,41.301528],[-70.275526,41.310464],[-70.260632,41.310092],[-70.249276,41.305623],[-70.244435,41.303203],[-70.240153,41.295384],[-70.229541,41.290171],[-70.20869,41.290171],[-70.196304,41.294612],[-70.12446,41.293851],[-70.092142,41.297741]]],[[[-73.022903,42.741133],[-72.930261,42.73916],[-72.809113,42.736581],[-72.458519,42.726853],[-72.285954,42.721631],[-72.282981,42.721557],[-72.124526,42.717636],[-71.981402,42.713294],[-71.928811,42.712234],[-71.898716,42.71142],[-71.745817,42.707287],[-71.636214,42.704888],[-71.631814,42.704788],[-71.542533,42.702672],[-71.351874,42.698154],[-71.330206,42.69719],[-71.294205,42.69699],[-71.278929,42.711258],[-71.267905,42.72589],[-71.255605,42.736389],[-71.25518,42.73665],[-71.245504,42.742589],[-71.233404,42.745489],[-71.223904,42.746689],[-71.208302,42.743314],[-71.208227,42.743294],[-71.208137,42.743273],[-71.181803,42.73759],[-71.186104,42.790689],[-71.174403,42.801589],[-71.167703,42.807389],[-71.165603,42.808689],[-71.149703,42.815489],[-71.132503,42.821389],[-71.116048,42.817751],[-71.064201,42.806289],[-71.053601,42.833089],[-71.047501,42.844089],[-71.044401,42.848789],[-71.031201,42.859089],[-70.9665,42.868989],[-70.949199,42.876089],[-70.931699,42.884189],[-70.930799,42.884589],[-70.927629,42.885326],[-70.914899,42.886589],[-70.914886,42.886564],[-70.902768,42.88653],[-70.886136,42.88261],[-70.848625,42.860939],[-70.837376,42.864996],[-70.830795,42.868918],[-70.821769,42.87188],[-70.817296,42.87229],[-70.817731,42.850613],[-70.80522,42.781798],[-70.792867,42.747118],[-70.772267,42.711064],[-70.770453,42.704824],[-70.778552,42.69852],[-70.778671,42.693622],[-70.764421,42.68565],[-70.748752,42.683878],[-70.744427,42.682092],[-70.72982,42.669602],[-70.728845,42.663877],[-70.689402,42.653319],[-70.682594,42.654525],[-70.681594,42.662342],[-70.663548,42.677603],[-70.645101,42.689423],[-70.630077,42.692699],[-70.620031,42.688006],[-70.622864,42.67599],[-70.623815,42.665481],[-70.622791,42.660873],[-70.61482,42.65765],[-70.595474,42.660336],[-70.591742,42.648508],[-70.591469,42.639821],[-70.594014,42.63503],[-70.605611,42.634898],[-70.61842,42.62864],[-70.635635,42.600243],[-70.654727,42.582234],[-70.664887,42.580436],[-70.668022,42.581732],[-70.668115,42.585361],[-70.668488,42.589643],[-70.670442,42.592249],[-70.672583,42.594296],[-70.675747,42.594669],[-70.678819,42.594389],[-70.681428,42.593173],[-70.684502,42.588858],[-70.698574,42.577393],[-70.729688,42.57151],[-70.737044,42.576863],[-70.757283,42.570455],[-70.804091,42.561595],[-70.815391,42.554195],[-70.823291,42.551495],[-70.848492,42.550195],[-70.871382,42.546404],[-70.872357,42.542952],[-70.866279,42.522617],[-70.859751,42.520441],[-70.857125,42.521492],[-70.842091,42.519495],[-70.831091,42.503596],[-70.835991,42.490496],[-70.841591,42.487596],[-70.847391,42.491496],[-70.857791,42.490296],[-70.879692,42.478796],[-70.886493,42.470197],[-70.887992,42.467096],[-70.887292,42.464896],[-70.894292,42.460896],[-70.908092,42.466896],[-70.917693,42.467996],[-70.921993,42.466696],[-70.934993,42.457896],[-70.934264,42.444646],[-70.933155,42.437833],[-70.928226,42.430986],[-70.913192,42.427697],[-70.908392,42.425197],[-70.901992,42.420297],[-70.905692,42.416197],[-70.936393,42.418097],[-70.943295,42.436248],[-70.943612,42.452092],[-70.94702,42.456236],[-70.96047,42.446166],[-70.960647,42.443787],[-70.960835,42.441272],[-70.982994,42.423996],[-70.987694,42.416696],[-70.990595,42.407098],[-70.989195,42.402598],[-70.985068,42.402041],[-70.983426,42.396246],[-70.980336,42.391513],[-70.972706,42.389968],[-70.970195,42.388036],[-70.97174,42.387071],[-70.972513,42.385042],[-70.972706,42.381759],[-70.972223,42.377316],[-70.960798,42.360648],[-70.953292,42.349698],[-70.953022,42.343973],[-70.963578,42.34686],[-70.972418,42.353875],[-70.974897,42.355843],[-70.979927,42.356382],[-70.982282,42.35592],[-70.998253,42.352788],[-71.006877,42.347039],[-71.010146,42.339234],[-71.011804,42.335274],[-71.01568,42.326019],[-71.013165,42.315419],[-71.005399,42.307196],[-71.000948,42.302483],[-71.006158,42.28811],[-71.0049,42.28272],[-70.996097,42.271222],[-70.98909,42.267449],[-70.967351,42.268168],[-70.948971,42.272505],[-70.945547,42.269081],[-70.935886,42.264189],[-70.923169,42.263211],[-70.910941,42.265412],[-70.906302,42.271636],[-70.896267,42.2851],[-70.895778,42.292436],[-70.897123,42.29586],[-70.915588,42.302463],[-70.91749,42.305686],[-70.907556,42.307889],[-70.882764,42.30886],[-70.881242,42.300663],[-70.870873,42.285668],[-70.861807,42.275965],[-70.851093,42.26827],[-70.831075,42.267424],[-70.824661,42.265935],[-70.811742,42.262935],[-70.788724,42.25392],[-70.780722,42.251792],[-70.770964,42.249197],[-70.764757,42.244062],[-70.754488,42.228673],[-70.74723,42.221816],[-70.73056,42.21094],[-70.722269,42.207959],[-70.718707,42.184853],[-70.714301,42.168783],[-70.706264,42.163137],[-70.685315,42.133025],[-70.663931,42.108336],[-70.640169,42.088633],[-70.63848,42.081579],[-70.647349,42.076331],[-70.64819,42.068441],[-70.643208,42.050821],[-70.644337,42.045895],[-70.650874,42.046247],[-70.66936,42.037116],[-70.671666,42.02139],[-70.667512,42.01232],[-70.670934,42.007786],[-70.678798,42.00551],[-70.686798,42.012764],[-70.695809,42.013346],[-70.712204,42.007586],[-70.710034,41.999544],[-70.698981,41.987103],[-70.662476,41.960592],[-70.651673,41.958701],[-70.648365,41.961672],[-70.631251,41.950475],[-70.623513,41.943273],[-70.616491,41.940204],[-70.608166,41.940701],[-70.598078,41.947772],[-70.583572,41.950007],[-70.552941,41.929641],[-70.546386,41.916751],[-70.54741,41.911934],[-70.545949,41.907158],[-70.532084,41.889568],[-70.525567,41.85873],[-70.535487,41.839381],[-70.542065,41.831263],[-70.543168,41.824446],[-70.54103,41.815754],[-70.537289,41.810859],[-70.532656,41.804796],[-70.517411,41.790953],[-70.494048,41.773883],[-70.471552,41.761563],[-70.412476,41.744397],[-70.375341,41.738779],[-70.290957,41.734312],[-70.275203,41.726143],[-70.272289,41.721346],[-70.263654,41.714115],[-70.259205,41.713954],[-70.23485,41.733733],[-70.216073,41.742981],[-70.189254,41.751982],[-70.182076,41.750885],[-70.141533,41.760072],[-70.121978,41.758841],[-70.096061,41.766549],[-70.064314,41.772845],[-70.024734,41.787364],[-70.008462,41.800786],[-70.003842,41.80852],[-70.004486,41.838826],[-70.009013,41.876625],[-70.000188,41.886938],[-70.002922,41.890315],[-70.012154,41.891656],[-70.024335,41.89882],[-70.025553,41.911699],[-70.030537,41.929154],[-70.044995,41.930049],[-70.054464,41.927366],[-70.065671,41.911658],[-70.065723,41.899641],[-70.065372,41.887702],[-70.064084,41.878924],[-70.066002,41.877011],[-70.067566,41.877793],[-70.070889,41.882973],[-70.073039,41.899783],[-70.074006,41.93865],[-70.077421,41.985497],[-70.083775,42.012041],[-70.089578,42.024896],[-70.095595,42.032832],[-70.10806,42.043601],[-70.123043,42.051668],[-70.148294,42.06195],[-70.155415,42.062409],[-70.169781,42.059736],[-70.178468,42.05642],[-70.186816,42.05045],[-70.194456,42.03947],[-70.195345,42.034163],[-70.193074,42.027576],[-70.186295,42.021308],[-70.186708,42.019904],[-70.190834,42.020028],[-70.196693,42.022429],[-70.208016,42.03073],[-70.218701,42.045848],[-70.233256,42.057714],[-70.238875,42.060479],[-70.24354,42.060569],[-70.245385,42.063733],[-70.238087,42.072878],[-70.225626,42.078601],[-70.206899,42.0819],[-70.189305,42.082337],[-70.160166,42.078628],[-70.115968,42.067638],[-70.082624,42.054657],[-70.058531,42.040363],[-70.033501,42.017736],[-70.011898,41.98972],[-69.986085,41.949597],[-69.968598,41.9117],[-69.945314,41.845222],[-69.935952,41.809422],[-69.928652,41.74125],[-69.928261,41.6917],[-69.933114,41.670014],[-69.947599,41.645394],[-69.951169,41.640799],[-69.958272,41.639429],[-69.963234,41.633794],[-69.967869,41.627503],[-69.976478,41.603664],[-69.982768,41.581812],[-69.988215,41.554704],[-69.998071,41.54365],[-70.004136,41.54212],[-70.011504,41.542924],[-70.014456,41.545534],[-70.016584,41.550772],[-70.015059,41.553037],[-70.010644,41.552692],[-70.00153,41.561953],[-69.994357,41.576846],[-69.987192,41.608579],[-69.973035,41.641046],[-69.973153,41.646963],[-69.975719,41.653738],[-69.996359,41.667184],[-70.007011,41.671579],[-70.014211,41.671971],[-70.029346,41.667744],[-70.055523,41.664843],[-70.089238,41.662813],[-70.140877,41.650423],[-70.158621,41.650438],[-70.191061,41.645259],[-70.245867,41.628479],[-70.25621,41.620698],[-70.25542,41.617541],[-70.259601,41.610863],[-70.265424,41.609333],[-70.267587,41.610912],[-70.269687,41.617775],[-70.26913,41.625742],[-70.274522,41.632927],[-70.28132,41.635125],[-70.29062,41.635196],[-70.321588,41.630508],[-70.329924,41.634578],[-70.338067,41.636338],[-70.351634,41.634687],[-70.360352,41.631069],[-70.364892,41.626721],[-70.364744,41.623671],[-70.369854,41.615888],[-70.379151,41.611361],[-70.400581,41.606382],[-70.408535,41.607345],[-70.437246,41.605329],[-70.445289,41.591815],[-70.461278,41.57182],[-70.476256,41.558502],[-70.485571,41.554244],[-70.493244,41.552044],[-70.522327,41.548965],[-70.559689,41.54833],[-70.611081,41.542989],[-70.633607,41.538254],[-70.643627,41.532357],[-70.654104,41.519025],[-70.663856,41.514031],[-70.669518,41.513339],[-70.675379,41.512623],[-70.705181,41.496677],[-70.734306,41.486335],[-70.757171,41.469917],[-70.756481,41.465977],[-70.760863,41.460947],[-70.79027,41.446339],[-70.817478,41.445562],[-70.835867,41.441877],[-70.857528,41.425767],[-70.866946,41.422378],[-70.902763,41.421061],[-70.928197,41.415781],[-70.937282,41.411618],[-70.948431,41.409193],[-70.951045,41.411777],[-70.949861,41.415323],[-70.928165,41.431265],[-70.923698,41.430716],[-70.918983,41.4253],[-70.91164,41.424484],[-70.906011,41.425708],[-70.883247,41.432239],[-70.855265,41.448892],[-70.828546,41.456448],[-70.802186,41.460864],[-70.787769,41.474609],[-70.775268,41.477465],[-70.753905,41.492256],[-70.745053,41.500966],[-70.6948,41.52564],[-70.658659,41.543385],[-70.654302,41.549926],[-70.655365,41.557498],[-70.653899,41.56516],[-70.64878,41.56987],[-70.642748,41.572385],[-70.640948,41.577325],[-70.64204,41.583066],[-70.652449,41.60521],[-70.651986,41.610184],[-70.640003,41.624616],[-70.645251,41.633547],[-70.652614,41.637829],[-70.650419,41.644202],[-70.638695,41.649427],[-70.637632,41.654573],[-70.646308,41.678433],[-70.649285,41.680943],[-70.661475,41.681756],[-70.645962,41.693794],[-70.62544,41.698691],[-70.623652,41.707398],[-70.626529,41.712995],[-70.642914,41.71841],[-70.644641,41.71898],[-70.651093,41.715715],[-70.656596,41.715401],[-70.670453,41.721912],[-70.708193,41.730959],[-70.718739,41.73574],[-70.726331,41.732731],[-70.728933,41.723433],[-70.721302,41.712968],[-70.717451,41.69398],[-70.719575,41.685002],[-70.729395,41.68814],[-70.744396,41.696967],[-70.755347,41.694326],[-70.761481,41.676808],[-70.76236,41.667735],[-70.758198,41.661225],[-70.757622,41.654265],[-70.765463,41.641575],[-70.769318,41.641145],[-70.773654,41.645033],[-70.775798,41.649145],[-70.776709,41.650756],[-70.809118,41.656437],[-70.813286,41.65567],[-70.815729,41.652796],[-70.816351,41.645995],[-70.804664,41.641157],[-70.800215,41.631753],[-70.801063,41.629513],[-70.810279,41.624873],[-70.835296,41.624532],[-70.843177,41.628487],[-70.843522,41.62866],[-70.843528,41.628663],[-70.844165,41.628983],[-70.852518,41.626919],[-70.855031,41.624283],[-70.855162,41.624145],[-70.854232,41.618429],[-70.854211,41.618302],[-70.853445,41.613592],[-70.850181,41.593529],[-70.85222,41.589223],[-70.852488,41.588658],[-70.852551,41.588526],[-70.853121,41.587321],[-70.85324,41.587332],[-70.857239,41.587705],[-70.862852,41.600678],[-70.862998,41.601014],[-70.863486,41.602143],[-70.868501,41.613733],[-70.868904,41.614664],[-70.86836,41.622664],[-70.869624,41.625608],[-70.872665,41.627816],[-70.87904,41.629777],[-70.887643,41.632422],[-70.889209,41.632904],[-70.88926,41.632875],[-70.889594,41.632685],[-70.904513,41.624205],[-70.905765,41.623494],[-70.913202,41.619266],[-70.904522,41.610361],[-70.899981,41.593504],[-70.901381,41.592504],[-70.910814,41.595506],[-70.916581,41.607483],[-70.920074,41.61081],[-70.927172,41.611253],[-70.929722,41.609479],[-70.93,41.600441],[-70.927393,41.594064],[-70.931338,41.5842],[-70.937978,41.577416],[-70.941588,41.581034],[-70.946911,41.581089],[-70.948797,41.579038],[-70.9473,41.573659],[-70.93783,41.565239],[-70.931545,41.540169],[-70.941785,41.540121],[-70.979225,41.530427],[-70.983354,41.520616],[-71.003275,41.511912],[-71.019354,41.508857],[-71.035514,41.499047],[-71.058418,41.505967],[-71.085663,41.509292],[-71.12057,41.497448],[-71.1224,41.522156],[-71.131312,41.592308],[-71.131618,41.593918],[-71.137492,41.602561],[-71.138599,41.60347],[-71.140588,41.605102],[-71.14091,41.607405],[-71.141509,41.616076],[-71.140468,41.623893],[-71.135688,41.628402],[-71.134484,41.641198],[-71.134478,41.641262],[-71.13267,41.658744],[-71.132888,41.660102],[-71.134688,41.660502],[-71.135188,41.660502],[-71.14587,41.662795],[-71.153989,41.664102],[-71.17609,41.668102],[-71.17609,41.668502],[-71.17599,41.671402],[-71.18129,41.672502],[-71.191175,41.674292],[-71.191178,41.674216],[-71.194384,41.674803],[-71.19564,41.67509],[-71.224798,41.710498],[-71.225709,41.711603],[-71.261392,41.752301],[-71.31779,41.776099],[-71.317795,41.776101],[-71.327896,41.780501],[-71.329396,41.7826],[-71.329296,41.7868],[-71.332196,41.7923],[-71.333896,41.7945],[-71.335797,41.7948],[-71.339297,41.7963],[-71.340697,41.7983],[-71.340797,41.8002],[-71.339297,41.8044],[-71.339297,41.8065],[-71.338897,41.8083],[-71.339197,41.809],[-71.347197,41.8231],[-71.344897,41.828],[-71.339597,41.832],[-71.337597,41.8337],[-71.335197,41.8355],[-71.341797,41.8437],[-71.342198,41.8448],[-71.333997,41.8623],[-71.340798,41.8816],[-71.339298,41.893399],[-71.339298,41.893599],[-71.338698,41.898399],[-71.352699,41.896699],[-71.354699,41.896499],[-71.362499,41.895599],[-71.364699,41.895399],[-71.365399,41.895299],[-71.370999,41.894599],[-71.373799,41.894399],[-71.3766,41.893999],[-71.3817,41.893199],[-71.3817,41.922699],[-71.3816,41.922899],[-71.381401,41.964799],[-71.381501,41.966699],[-71.381466,41.984998],[-71.381401,42.018798],[-71.458104,42.017762],[-71.498258,42.01722],[-71.499905,42.017198],[-71.500905,42.017098],[-71.527306,42.015098],[-71.527606,42.014998],[-71.559439,42.014342],[-71.576908,42.014098],[-71.76601,42.009745],[-71.799242,42.008065],[-71.80065,42.023569],[-71.89078,42.024368],[-71.987326,42.02688],[-72.063496,42.027347],[-72.102162,42.028899],[-72.135687,42.030245],[-72.135715,42.030245],[-72.249523,42.031626],[-72.317148,42.031907],[-72.45668,42.033999],[-72.509192,42.034217],[-72.528131,42.034295],[-72.573231,42.030141],[-72.582332,42.024695],[-72.590233,42.024695],[-72.606933,42.024995],[-72.607933,42.030795],[-72.643134,42.032395],[-72.695927,42.036788],[-72.714134,42.036608],[-72.755838,42.036195],[-72.757538,42.033295],[-72.753538,42.032095],[-72.751738,42.030195],[-72.754038,42.025395],[-72.757467,42.020947],[-72.758151,42.020865],[-72.760558,42.021846],[-72.762151,42.021527],[-72.76231,42.019775],[-72.761354,42.018183],[-72.759738,42.016995],[-72.761238,42.014595],[-72.763238,42.012795],[-72.763265,42.009742],[-72.766139,42.007695],[-72.766739,42.002995],[-72.774757,42.002129],[-72.816741,41.997595],[-72.813541,42.036494],[-72.847142,42.036894],[-72.863619,42.037709],[-72.863733,42.03771],[-72.999549,42.038653],[-73.008745,42.03886],[-73.053254,42.039861],[-73.127276,42.041964],[-73.229798,42.044877],[-73.231056,42.044945],[-73.293097,42.04694],[-73.29442,42.046984],[-73.432812,42.050587],[-73.487314,42.049638],[-73.496879,42.049675],[-73.508142,42.086257],[-73.352527,42.510002],[-73.264957,42.74594],[-73.022903,42.741133]]]]},\"properties\":{\"name\":\"Massachusetts\",\"nation\":\"USA \"}}]}","volume":"345","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andreadisa, Konstantinos","contributorId":342567,"corporation":false,"usgs":false,"family":"Andreadisa","given":"Konstantinos","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kumar, Abhiskek","contributorId":342569,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhiskek","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Caitlyn","contributorId":316779,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910197,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249833,"text":"70249833 - 2023 - A novel boat-based field application of a high-frequency conductometric ammonium analyzer to characterize spatial variation in aquatic ecosystems","interactions":[],"lastModifiedDate":"2023-12-21T14:31:51.192037","indexId":"70249833","displayToPublicDate":"2023-10-31T15:25:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7183,"text":"Limnology and Oceanography Methods","active":true,"publicationSubtype":{"id":10}},"title":"A novel boat-based field application of a high-frequency conductometric ammonium analyzer to characterize spatial variation in aquatic ecosystems","docAbstract":"Documenting dissolved inorganic nitrogen (DIN) concentration and form at appropriate temporal and spatial scales is key to understanding aquatic ecosystem health, particularly as DIN fuels primary productivity. In addition to point and non-point source nutrient inputs, factors such as hydrology, geomorphology, temperature, light, and biogeochemical transformations influence nutrient dynamics in surface waters, allowing for the formation of steep spatial gradients and patchiness. Documenting nutrient variability is also necessary to identify sources, quantify transformation rates, and understand drivers. Due to logistical and cost constraints, it is often unfeasible to measure concentrations of nutrients in surface waters using discrete sampling followed by laboratory analysis at a resolution high enough to identify steep spatial gradients and patchiness. Because of these constraints, data generated from discrete sampling are limited in space and time, often missing key variabilities. Recent advancements of in situ nitrate plus nitrite (NO3- and NO2-) sensor technology has enabled highly temporally and spatially resolved NO3- concentration measurements in aquatic ecosystems. However, comparable information about ammonium (NH4+) concentrations remains unavailable. To address this need, U.S. Geological Survey collaborated with Timberline Instruments to modify their commercially available benchtop TL-2800 ammonia analyzer to collect high-frequency continuous (1 unique sample measurement per second) NH4+ concentration measurements at a micromolar (0.5 µM) resolution in flow-through mode while receiving water pumped from a moving boat. Although the utility of this method is described for spatial surveys, we anticipate that it would be adaptable to installation at a fixed station for continuous monitoring of NH4+ concentration.","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lom3.10579","usgsCitation":"Richardson, E.T., Hansen, A., Kraus, T.E., Downing, B.D., Forsberg, D., Stillian, J., O’Donnell, K., Sturgeon, C.L., and Bergamaschi, B.A., 2023, A novel boat-based field application of a high-frequency conductometric ammonium analyzer to characterize spatial variation in aquatic ecosystems: Limnology and Oceanography Methods, v. 21, no. 12, p. 761-774, https://doi.org/10.1002/lom3.10579.","productDescription":"14 p.","startPage":"761","endPage":"774","ipdsId":"IP-117787","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":441730,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lom3.10579","text":"Publisher Index Page"},{"id":422311,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.20848323920738,\n 37.8750433280058\n ],\n [\n -121.07377092901734,\n 37.8750433280058\n ],\n [\n -121.07377092901734,\n 38.640000890410164\n ],\n [\n -122.20848323920738,\n 38.640000890410164\n ],\n [\n -122.20848323920738,\n 37.8750433280058\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Angela 0000-0003-0938-7611 anhansen@usgs.gov","orcid":"https://orcid.org/0000-0003-0938-7611","contributorId":171551,"corporation":false,"usgs":true,"family":"Hansen","given":"Angela","email":"anhansen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Bryan D. 0000-0002-2007-5304","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":294720,"corporation":false,"usgs":false,"family":"Downing","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":887280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Forsberg, Don","contributorId":331297,"corporation":false,"usgs":false,"family":"Forsberg","given":"Don","email":"","affiliations":[{"id":79180,"text":"Timberline Instruments","active":true,"usgs":false}],"preferred":false,"id":887281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stillian, John","contributorId":331298,"corporation":false,"usgs":false,"family":"Stillian","given":"John","email":"","affiliations":[{"id":79180,"text":"Timberline Instruments","active":true,"usgs":false}],"preferred":false,"id":887282,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Donnell, Katy 0000-0003-2323-8970 kodonnell@usgs.gov","orcid":"https://orcid.org/0000-0003-2323-8970","contributorId":5640,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Katy","email":"kodonnell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887283,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sturgeon, Crystal Lee 0000-0002-1799-9127","orcid":"https://orcid.org/0000-0002-1799-9127","contributorId":302710,"corporation":false,"usgs":true,"family":"Sturgeon","given":"Crystal","email":"","middleInitial":"Lee","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887284,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":887285,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274317,"text":"70274317 - 2023 - Networks of tree-ring based streamflow reconstructions for the Pacific Northwest, U.S.A","interactions":[],"lastModifiedDate":"2026-03-26T15:31:40.785954","indexId":"70274317","displayToPublicDate":"2023-10-31T00:00:00","publicationYear":"2023","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":"Networks of tree-ring based streamflow reconstructions for the Pacific Northwest, U.S.A","docAbstract":"Water resources in the Pacific Northwest (PNW) are characterized by significant interannual to interdecadal variation. Paleo-proxy reconstructions such as those derived from tree-rings provide longer-term context and supplement information on this expected range of variability, which can improve planning, management, and response related to extreme events and hydrologic change. However, existing paleo-proxy reconstructions have yet to address the potential for pronounced within- and among-basin variations in the PNW due to a lack of spatial coverage. Here we develop methodologically consistent reconstructions for 36 gages in the PNW, including the Columbia and Snake River drainages, as well as key coastal watersheds. These reconstructions extend back at least to the 1500s coefficient of efficiency. Reconstruction skill is relatively high (mean R2 = 0.63), and snowpack- or winter precipitation-sensitive chronologies from high-elevation sites provide important contributions to reconstruction skill. At the whole-region scale, reconstructed variability indicates evidence for drier and wetter years, more persistent decadal variability, and correspondingly longer episodes of deficit and surplus compared to instrumental records. Within the region, this expanded range of extremes appears especially prevalent in the Snake River and southern PNW watersheds. Regionally, cumulative deficits in the early 1500s and mid 1600s rival those of the early 20th century, though persistence and timing vary widely among basins. These reconstructions suggest that considering within-region variability will be key for water management and planning under climate change, and that sub-regional adaptation strategies are likely to be advantageous.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035255","usgsCitation":"Littell, J.S., Pederson, G.T., Martin, J.T., and Gray, S.T., 2023, Networks of tree-ring based streamflow reconstructions for the Pacific Northwest, U.S.A: Water Resources Research, v. 59, no. 11, e2023WR035255, 19 p., https://doi.org/10.1029/2023WR035255.","productDescription":"e2023WR035255, 19 p.","ipdsId":"IP-157562","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":501606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr035255","text":"Publisher Index Page"},{"id":501578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.71989106589547,\n 51.23952552193842\n ],\n [\n -119.80255789994311,\n 50.58241223780449\n ],\n [\n -120.03798004087265,\n 48.335839842710136\n ],\n [\n -124.01371557903101,\n 45.96848992109024\n ],\n [\n -123.89678202651015,\n 43.17034661083997\n ],\n [\n -117.55123466152142,\n 43.759998649017774\n ],\n [\n -116.75453238797795,\n 41.1626488502026\n ],\n [\n -107.8814009064086,\n 41.44779467194607\n ],\n [\n -111.20892445986055,\n 45.51412922045732\n ],\n [\n -113.15663189749853,\n 52.616945225148044\n ],\n [\n -114.23620971604478,\n 53.31116037143961\n ],\n [\n -117.71989106589547,\n 51.23952552193842\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":957851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":957852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":957853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gray, Stephen T. 0000-0002-0959-3418 sgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-3418","contributorId":209851,"corporation":false,"usgs":true,"family":"Gray","given":"Stephen","email":"sgray@usgs.gov","middleInitial":"T.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":957854,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255038,"text":"70255038 - 2023 - Stream hydrology and a pulse subsidy shape patterns of fish foraging","interactions":[],"lastModifiedDate":"2024-06-17T15:25:51.081928","indexId":"70255038","displayToPublicDate":"2023-10-30T10:18:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Stream hydrology and a pulse subsidy shape patterns of fish foraging","docAbstract":"The lower Mississippi River Valley spans over 200,000 square kilometres in parts of seven states, encompassing areas of critical groundwater supplies, natural hazards, infrastructure, and low-lying coastal regions. From 2018 - 2022, the U.S. Geological Survey acquired over 82,000 line-kilometres of airborne electromagnetic, radiometric, and magnetic data over this region to provide comprehensive and systematic information about subsurface geologic and hydrologic properties that support multiple scientific and societal interests. Most of the data were acquired on a regional grid of west-east flight lines separated by 3 - 6 kilometres; however, several high-resolution inset grids with line spacing as close as 200 m were acquired in targeted areas of interest. Approximately 8,000 line-kilometres were acquired along streams and rivers to characterise the potential for surface water-groundwater connection, and another 6,000 line-kilometres were acquired along the Mississippi and Arkansas River levees to characterise this critical infrastructure. Here, we present a summary of the data along with several examples of how they are being used to inform regional groundwater model development, inferences of groundwater salinity, identification of faults in the New Madrid seismic zone, and levee infrastructure.
","conferenceTitle":"AEM2023 8th International Airborne Electromagnetics Workshop","conferenceDate":"September 3-7, 2023","conferenceLocation":"Fitzroy Island, Queensland, Australia","language":"English","publisher":"Australian Society of Exploration Geophysicists","doi":"10.5281/zenodo.10052667","usgsCitation":"Minsley, B.J., Adams, R.F., Asquith, W.H., Burton, B.L., Hoogenboom, B.E., James, S.R., Killian, C.D., Knierim, K.J., Kress, W.H., Lindaman, M., Leaf, A.T., Rigby, J.R., and Traylor, J.P., 2023, System-scale airborne electromagnetic surveys in the lower Mississippi River Valley support multidisciplinary applications, AEM2023 8th International Airborne Electromagnetics Workshop, Fitzroy Island, Queensland, Australia, September 3-7, 2023, 5 p., https://doi.org/10.5281/zenodo.10052667.","productDescription":"5 p.","ipdsId":"IP-150848","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"lower Mississippi River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.74456800918571,\n 37.63192332238003\n ],\n [\n -92.98685410111881,\n 37.63192332238003\n ],\n [\n -92.98685410111881,\n 27.15668126283292\n ],\n [\n -87.74456800918571,\n 27.15668126283292\n ],\n [\n -87.74456800918571,\n 37.63192332238003\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","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":886127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Ryan F. 0000-0001-7299-329X rfadams@usgs.gov","orcid":"https://orcid.org/0000-0001-7299-329X","contributorId":5499,"corporation":false,"usgs":true,"family":"Adams","given":"Ryan","email":"rfadams@usgs.gov","middleInitial":"F.","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burton, Bethany L. 0000-0001-5011-7862 blburton@usgs.gov","orcid":"https://orcid.org/0000-0001-5011-7862","contributorId":138925,"corporation":false,"usgs":true,"family":"Burton","given":"Bethany","email":"blburton@usgs.gov","middleInitial":"L.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":886130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoogenboom, Bennett Eugene 0000-0001-8096-3533","orcid":"https://orcid.org/0000-0001-8096-3533","contributorId":239871,"corporation":false,"usgs":true,"family":"Hoogenboom","given":"Bennett","email":"","middleInitial":"Eugene","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":886131,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":886132,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Killian, Courtney D. 0000-0002-2137-2722","orcid":"https://orcid.org/0000-0002-2137-2722","contributorId":213990,"corporation":false,"usgs":true,"family":"Killian","given":"Courtney","email":"","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886133,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886134,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kress, Wade H. 0000-0002-6833-028X wkress@usgs.gov","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":1576,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","email":"wkress@usgs.gov","middleInitial":"H.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886135,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lindaman, Maxwell A. 0000-0003-1786-1272","orcid":"https://orcid.org/0000-0003-1786-1272","contributorId":219064,"corporation":false,"usgs":true,"family":"Lindaman","given":"Maxwell A.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886136,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886137,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":260894,"corporation":false,"usgs":true,"family":"Rigby","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886138,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Traylor, Jonathan P. 0000-0002-2008-1923 jtraylor@usgs.gov","orcid":"https://orcid.org/0000-0002-2008-1923","contributorId":5322,"corporation":false,"usgs":true,"family":"Traylor","given":"Jonathan","email":"jtraylor@usgs.gov","middleInitial":"P.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":886139,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70249724,"text":"ofr20231073 - 2023 - Multiple-well monitoring site adjacent to the Elk Hills Oil Field, Kern County, California","interactions":[],"lastModifiedDate":"2026-02-03T21:02:00.418803","indexId":"ofr20231073","displayToPublicDate":"2023-10-26T14:27:37","publicationYear":"2023","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":"2023-1073","displayTitle":"Multiple-Well Monitoring Site Adjacent to the Elk Hills Oil Field, Kern County, California","title":"Multiple-well monitoring site adjacent to the Elk Hills Oil Field, Kern County, California","docAbstract":"The Elk Hills Oil Field is one of the many fields selected for regional groundwater mapping and monitoring by the California State Water Resources Control Board as part of the Oil and Gas Regional Monitoring Program (California State Water Resources Control Board, 2015, 2022b; U.S. Geological Survey, 2022a). The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating groundwater resources near areas of oil and gas development in California, including (1) the location of groundwater resources near oil fields; (2) the proximity of oil and gas operations to groundwater, and the geologic materials between them; (3) evidence (or lack of evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2022a). As part of this evaluation, the USGS installed a multiple-well monitoring site near the administrative boundary of the Elk Hills Oil Field in the southern San Joaquin Valley about 6 miles northeast of Taft, California (California Department of Water Resources, 2020; fig. 1). Data collected at the Elk Hills multiple-well monitoring site (ELKH) provide information about the geology, hydrology, geophysical properties, and water quality of the aquifer system, thus enhancing the understanding of relations between adjacent groundwater and the Elk Hills Oil Field in an area where groundwater data are limited, particularly at different depths in the aquifer. This report presents construction information for the ELKH and initial geohydrologic data collected from the site. Similar sites installed on the east side of the Lost Hills Oil Field, on the east side of the North and South Belridge Oil Fields, and within the Poso Creek Oil Field were described by Everett and others (2020a, b, 2023).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231073","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., Gillespie J.M., Shepherd, M.M., Morita, A.Y., Bobbitt, M., Kohel, C.A., and Warden, J.G., 2023, Multiple-well monitoring site adjacent to the Elk Hills Oil Field, Kern County, California: U.S. Geological Survey Open-File Report 2023–1073, 11 p., https://doi.org/10.3133/ofr20231073.","productDescription":"11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-148290","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":499485,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115583.htm","linkFileType":{"id":5,"text":"html"}},{"id":422144,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231073/full"},{"id":422143,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1073/images"},{"id":422141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1073/ofr20231073.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":422140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1073/covrthb.jpg"},{"id":422142,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1073/ofr20231073.xml"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.35,\n 35.2\n ],\n [\n -119.35,\n 35.1\n ],\n [\n -119.1,\n 35.1\n ],\n [\n -119.1,\n 35.2\n ],\n [\n -119.35,\n 35.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Urban areas usually exhibit relatively warmer air and surface temperatures than surrounding non-urban lands, a phenomenon recognized as Surface Urban Heat Island (SUHI). As urban areas continue to develop and the climate continues to warm, it has become increasingly important to quantify and map the SUHI effect and learn how to mitigate it. To help meet the expanding need of analysis ready data for SUHI based studies, a methodology was developed to evaluate Land Surface Temperature (LST) using the Landsat Collection 1 Provisional Surface Temperature Science Product. The Landsat derived LST products were processed for 50 major cities throughout the Conterminous U.S. The SUHI product package includes per-pixel annual surface temperature, annual intensity, annual hotspot, and hotspot probability bands from 1985 to 2020.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2023 - 2023 IEEE international geoscience and remote sensing symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 16-21, 2023","conferenceLocation":"Pasadena, CA","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS52108.2023.10282386","usgsCitation":"Mueller, C., Hussain, R., Xian, G.Z., Shi, H., and Arab, S., 2023, Mapping the Surface Urban Heat Island effect using the Landsat Surface Temperature Product, in IGARSS 2023 - 2023 IEEE international geoscience and remote sensing symposium, Pasadena, CA, July 16-21, 2023, p. 441-444, https://doi.org/10.1109/IGARSS52108.2023.10282386.","productDescription":"4 p.","startPage":"441","endPage":"444","ipdsId":"IP-153990","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":429325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mueller, Chase 0000-0002-9948-1304","orcid":"https://orcid.org/0000-0002-9948-1304","contributorId":302266,"corporation":false,"usgs":false,"family":"Mueller","given":"Chase","affiliations":[],"preferred":false,"id":877861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hussain, Reza 0000-0002-5445-3027","orcid":"https://orcid.org/0000-0002-5445-3027","contributorId":301245,"corporation":false,"usgs":false,"family":"Hussain","given":"Reza","affiliations":[{"id":65343,"text":"KBR, Contractor to U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":877862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":877863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":877864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":877865,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249598,"text":"fs20233043 - 2023 - Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center","interactions":[],"lastModifiedDate":"2026-02-09T17:47:45.10106","indexId":"fs20233043","displayToPublicDate":"2023-10-18T16:01:18","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3043","displayTitle":"Hydrologic Investigations of Green Infrastructure by the Central Midwest Water Science Center","title":"Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center","docAbstract":"The water management system within developed communities includes stormwater, wastewater, and drinking-water sources and sinks. Each water management system component provides critical services that support public health in these areas. Stormwater can be quite variable and difficult to manage in developed communities because the amount of stormwater that must be routed through a developed area depends on changing land cover and variable precipitation. In addition to flooding concerns, stormwater also is a major cause of water contamination in developed communities because it carries contaminants such as trash, bacteria, heavy metals, and sediments to local waterways. Historically, communities have managed stormwater with gray infrastructure such as street gutters, culverts, sewer systems, and tunnels. Although these structures efficiently capture and route stormwater to a local waterway or treatment plant, they do not filter any contaminants. Furthermore, many older communities have combined storm sewer and sanitary sewer systems. These combined systems result in an excessive amount of wastewater to be treated before being released into receiving water or the untreated waters are released directly to receiving waters during storms.
Many communities are now incorporating green infrastructure stormwater mitigating solutions—pervious surfaces (allows water through), grassed swales, bioretention basins, and rain gardens—into their stormwater-management systems. Green infrastructure can absorb and filter stormwater where it falls by taking advantage of natural soil and plant storage and filtration capabilities. Thus, green infrastructure projects can potentially reduce the amount of stormwater and the concentration and transport of contaminants. Increasing green infrastructure in a developed community may reduce the requirements for new storm sewer infrastructure, improve the water quality of nearby waterways, and enhance aesthetics.
The U.S. Geological Survey has partnered with several cooperators to quantify the effects of green infrastructure projects in several developed communities throughout the central Midwest. As part of these green infrastructure projects, the U.S. Geological Survey Central Midwest Water Science Center and cooperators installed, calibrated, and monitored equipment to measure hydrologic responses (including flooding and water movement) and selected water-quality constituents in developed communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233043","usgsCitation":"Atkinson, A.A., Heimann, D.C., and Bailey, C.R., 2023, Hydrologic investigations of green infrastructure by the Central Midwest Water Science Center: U.S. Geological Survey Fact Sheet 2023–3043, 4 p., https://doi.org/10.3133/fs20233043.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-147766","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499695,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115581.htm","linkFileType":{"id":5,"text":"html"}},{"id":499694,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115580.htm","linkFileType":{"id":5,"text":"html"}},{"id":421980,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3043/fs20233043.pdf","text":"Report","size":"2.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3043"},{"id":421979,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3043/images"},{"id":421978,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3043/fs20233043.XML","text":"XML"},{"id":421976,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3043/coverthb.jpg"},{"id":421981,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233043/full","text":"HTML","linkFileType":{"id":5,"text":"html"}}],"contact":"Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
We rarely consider light limitation in ecosystem productivity, yet light limitation is a major constraint on river autotrophy. Because the light that reaches benthic autotrophs must first pass through terrestrial vegetation and an overlying water column that can be loaded with sediments or colored organic material, there is strong selection for river autotrophs to have high light use efficiencies (LUEs), that is, the efficiency at which light energy is converted to biomass. In contrast to prior studies that have estimated river LUE on single days, we calculated continuous LUE over more than 6 full years for 64 free-flowing rivers across the United States. This dataset represents the largest compilation of continuous estimates of daily rates of gross primary productivity (GPP) and daily light inputs from which we calculated daily estimates of LUE. Early estimates of LUE in rivers found that clearwater springs with stable flows could achieve LUEs of 4%, much higher than LUEs reported for terrestrial plants. We found that 53% of the rivers in our dataset have LUEs that exceed 4% on at least one day of their time series. Because of the high variability in daily LUE, measurements taken on any given day may misrepresent a river ecosystem's annual LUE. Though most rivers share a high potential, the mean annual LUE of all rivers in our dataset is much lower, only 0.5%. We found that rivers with more variable flow regimes had lower annual LUEs, which indicates that LUE is constrained by hydrologic disturbances that remove, bury, or shade autotrophic biomass. Comparisons of LUE across ecosystems allow us to reframe our view of rivers, by recognizing the high efficiency with which they convert light to biomass compared with lentic, marine, and terrestrial ecosystems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4659","usgsCitation":"Thellman, A., Savoy, P., and Bernhardt, E., 2023, High potential but low achievement: Frequent disturbance constrains the light use efficiency of river ecosystems: Ecosphere, v. 14, no. 10, e4659, 9 p., https://doi.org/10.1002/ecs2.4659.","productDescription":"e4659, 9 p.","ipdsId":"IP-151660","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":492879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4659","text":"Publisher Index Page"},{"id":492731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"14","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Thellman, Audrey 0000-0003-3716-6664","orcid":"https://orcid.org/0000-0003-3716-6664","contributorId":265349,"corporation":false,"usgs":false,"family":"Thellman","given":"Audrey","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":943676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Savoy, Philip 0000-0002-6075-837X","orcid":"https://orcid.org/0000-0002-6075-837X","contributorId":300288,"corporation":false,"usgs":true,"family":"Savoy","given":"Philip","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":943677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernhardt, Emily S.","contributorId":92143,"corporation":false,"usgs":false,"family":"Bernhardt","given":"Emily S.","affiliations":[{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":943678,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249590,"text":"70249590 - 2023 - Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar","interactions":[],"lastModifiedDate":"2023-10-18T11:59:10.643012","indexId":"70249590","displayToPublicDate":"2023-10-16T06:55:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar","docAbstract":"Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.