Poaching is a global problem causing the decline of species worldwide. Optimizing the efficiency of ranger patrols to deter poaching activity at the lowest possible cost is crucial for protecting species with limited resources. We applied decision analysis and spatial optimization algorithms to allocate efforts of ranger patrols throughout a national park. Our objective was to mitigate poaching activity at or below management risk targets for the lowest monetary cost. We examined this trade‐off by constructing a Pareto efficiency frontier using integer linear programming. We used data from a ranger‐based monitoring program in Nyungwe National Park, Rwanda. Our measure of poaching risk is based on dynamic occupancy models that account for imperfect detection of poaching activities. We found that in order to achieve a 5% reduction in poaching risk, 622 ranger patrol events (each corresponding to patrolling 1‐km2 sites) were needed within a year at a cost of US$49,760. In order to attain a 60% reduction in poaching risk, 15,560 patrol events were needed at a cost of US\\$1,244,800. We evaluated the trade‐off between patrol cost and poaching risk based on our model by constructing a Pareto efficiency frontier and park managers found the solution for a 50% risk reduction to be a practical trade‐off based on funding constraints (comparable to recent years) and the diminishing returns between risk mitigation and cost. This expected reduction in risk required 8,558 patrol events per year at a cost of US \\$684,640. Our results suggest that optimal solutions could increase efficiency compared to the actual effort allocations from 2006 to 2016 in Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets compared to ~50% reduction in risk under the optimal strategy). The modeling framework in this study took into account imperfect detection of poaching risk as well as the directional and conditional nature of ranger patrol events given the spatial adjacency relationships of neighboring sites and access points. Our analyses can help to improve the efficiency of ranger patrols, and the modeling framework can be broadly applied to other spatial conservation planning problems with conditional, multilevel, site selection.
The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires > 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (<1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.
Water-quality activities and water-level measurements conducted by the U.S. Geological Survey (USGS) Idaho National Laboratory (INL) Project Office coincide with the USGS mission of appraising the quantity and quality of the Nation’s water resources. The activities are conducted in cooperation with the U.S. Department of Energy’s (DOE) Idaho Operations Office. Results of water-quality and hydraulic head investigations are presented in various USGS publications or in refereed scientific journals, and the data are stored in the National Water Information System (NWIS) database. The results of the studies are used by researchers, regulatory and managerial agencies, and civic groups.
In its broadest sense, “quality assurance” refers to doing the job right the first time. It includes the functions of planning for products, review and acceptance of the products, and an audit designed to evaluate the system that produces the products. Quality control and quality assurance differ in that quality control ensures that things are done correctly given the “state-of-the-art” technology, and quality assurance ensures that quality control is maintained within specified limits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211004","collaboration":"DOE/ID-22253Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.
","language":"English","publisher":"Copernicus","doi":"10.5194/hess-25-1529-2021","usgsCitation":"Saxe, S., Farmer, W., Driscoll, J.M., and Hogue, T.S., 2021, Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates: Hydrology and Earth System Sciences, v. 25, p. 1529-1598, https://doi.org/10.5194/hess-25-1529-2021.","productDescription":"70 p.","startPage":"1529","endPage":"1598","ipdsId":"IP-117307","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":452922,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-25-1529-2021","text":"Publisher Index Page"},{"id":436432,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9588YM2","text":"USGS data release","linkHelpText":"Collection of Hydrologic Models, Reanalysis Datasets, and Remote Sensing Products Aggregated by Ecoregion over the CONUS from 1900 to 2018"},{"id":385353,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"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":"25","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814840,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219422,"text":"70219422 - 2021 - Mammal species composition and habitat associations in a commercial forest and mixed-plantation landscape","interactions":[],"lastModifiedDate":"2021-04-05T13:10:25.140287","indexId":"70219422","displayToPublicDate":"2021-03-26T08:08:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Mammal species composition and habitat associations in a commercial forest and mixed-plantation landscape","docAbstract":"Commercial forest plantations of fast-growing species have been established globally to meet increasing demands for timber, pulpwood, and other wood products. Industrial plantations may contribute to tropical forest conservation by reducing exploitation of primary and secondary natural forests. Whether such plantations can support critical elements of biodiversity, including provision of habitat and movement corridors for species of conservation concern, is an important question in Southeast Asia. Our objectives were to investigate relationships between habitat gradients and community attributes of medium-sized to large mammals in a mixed plantation mosaic in Bengkoka Peninsula, Sabah, East Malaysia. Data on mammals were collected using 59 remote camera stations deployed for a minimum of 21 days (24-hour sampling occasions) in three major land-use types: natural forest, Acacia plantations, and non-Acacia plantations (oil palm, rubber, young Eucalyptus pellita). We used sample-based rarefaction to evaluate variation in species richness with land use. We used generalized linear models and ordination analyses to evaluate whether variation in mammal detections and species composition was associated with habitat gradients. We recorded >22 mammal species over 1572 sampling occasions. Natural forest area was positively associated with mammal species richness and detections of threatened mammals. Overall detections of mammals increased with decreasing elevation, but decreased within, and close to, Acacia plantations. Detections of threatened mammals increased with greater proportions of natural forest and Acacia and increasing proximity to roads. Sample-based rarefaction indicated that species richness of mammals in Acacia and natural forest was considerably higher than observed. Both natural forest and Acacia plantations shared similar values for species richness and diversity, but non-Acacia plantations scored lower in both metrics. Mammal species composition differed among different types of land use. Smaller generalists used non-Acacia plantation forests. A variety of other mammals including some threatened species used natural forest, Acacia, or a combination of the two. Acacia plantations possess attributes supporting a diversity of mammal species, including those we defined as threatened based on IUCN criteria. However, this is likely a function of the habitat mosaic with natural forest in the study area and the mangrove forests on the fringes of the peninsula serving as refuges of mammal diversity. Retention and restoration of natural and mangrove forests may therefore enhance the conservation potential of industrial Acacia plantations. Additionally, controlled road access in conjunction with anti-poaching operations and strengthening public awareness are essential to reduce the threat of overexploitation.
A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.
Despite the importance of species–area relationships (SARs) to conservation, SARs in human‐fragmented rivers have received little attention. Our aim was to test for the presence and strength of SARs for littoral fish assemblages of an extensively dammed river in south‐central Ontario, Canada, and to examine long‐running hypotheses for the drivers of SARs. Twenty‐six navigational dams with locks built between 1837 and 1913 occur along the 160 km length of the Trent River examined in this study. We evaluated the relationship between richness and fragment area, and then used linear models to test whether the area per se, habitat diversity, or other hypotheses were best supported by the data. A power–function relationship with area explained 46% of the variation in fish species richness, and the slope (z = 0.4) was high compared with SARs reported from other ecosystems, indicating that species accumulated rapidly with an increase in fragment area. Multi‐predictor models suggested that area was significantly related to richness, but that vegetation cover diversity had a stronger relative effect. The slope of our SAR may indicate that there is a high degree of isolation between populations in different fragments, even though the lock system reportedly allows some passage of organisms. Our findings also suggest that mitigating against local extinction due to small population sizes (i.e., area effects), and enhancing aquatic vegetation cover may be viable strategies for promoting species diversity in the study river. Studies of SARs in fragmented rivers may offer additional benefits to supporting restoration planning where efforts are being made to increase species diversity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3411","usgsCitation":"Carl, L.M., Esselman, P., Sparks-Jackson, B.L., and Wilson, C.C., 2021, The species–area relationship for a highly fragmented temperate river system: Ecosphere, v. 12, no. 3, e03411, 17 p., https://doi.org/10.1002/ecs2.3411.","productDescription":"e03411, 17 p.","ipdsId":"IP-074620","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":490069,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3411","text":"Publisher Index Page"},{"id":436433,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1AYLW","text":"USGS data release","linkHelpText":"Habitat and fish assemblages along four river mainstems in Ontario, Canada, 1997 to 2001, with supporting spatial data"},{"id":384698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario","otherGeospatial":"Rice Lake, Trent River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.99468994140625,\n 44.43476045009948\n ],\n [\n -78.41629028320312,\n 44.173339873464684\n ],\n [\n -78.28582763671875,\n 43.94537239244209\n ],\n [\n -78.01803588867188,\n 43.95822503841972\n ],\n [\n -77.86972045898438,\n 43.982933852960805\n ],\n [\n -77.73101806640625,\n 43.982933852960805\n ],\n [\n -77.57858276367188,\n 44.049102784014536\n ],\n [\n -77.574462890625,\n 44.26683800273895\n ],\n [\n -77.99468994140625,\n 44.43476045009948\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Carl, Leon M. 0000-0001-6419-2214 lcarl@usgs.gov","orcid":"https://orcid.org/0000-0001-6419-2214","contributorId":256693,"corporation":false,"usgs":true,"family":"Carl","given":"Leon","email":"lcarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":813065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":813066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparks-Jackson, Beth L 0000-0002-1726-1480","orcid":"https://orcid.org/0000-0002-1726-1480","contributorId":256695,"corporation":false,"usgs":false,"family":"Sparks-Jackson","given":"Beth","email":"","middleInitial":"L","affiliations":[{"id":51831,"text":"Contractor to USGS Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":813067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Christopher C. 0000-0002-9528-0652","orcid":"https://orcid.org/0000-0002-9528-0652","contributorId":256696,"corporation":false,"usgs":false,"family":"Wilson","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":51832,"text":"Aquatic Biodiversity and Conservation Unit, Ontario Ministry of Natural Resources, Peterborough, ON, Canada","active":true,"usgs":false}],"preferred":false,"id":813068,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219512,"text":"70219512 - 2021 - Drivers of methane flux differ between lakes and reservoirs, complicating global upscaling efforts","interactions":[],"lastModifiedDate":"2021-04-12T14:57:05.766282","indexId":"70219512","displayToPublicDate":"2021-03-25T09:53:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8116,"text":"Journal of Geophysical Research-Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of methane flux differ between lakes and reservoirs, complicating global upscaling efforts","docAbstract":"Methane is an important greenhouse gas with growing atmospheric concentrations. Freshwater lakes and reservoirs contribute substantially to atmospheric methane concentrations, but the magnitude of this contribution is poorly constrained. Uncertainty stems partially from whether the sites currently sampled represent the global population as well as incomplete knowledge of which environmental variables predict methane flux. Thus, determining the main drivers of methane flux across diverse waterbody types will inform more accurate upscaling approaches. Here we use a new database of total, diffusive, and ebullitive areal methane emissions from 313 lakes and reservoirs (ranging in surface area from 6 m2 to 5,400 km2) to identify the best predictors of methane emission. We found that the best predictors of methane emission differed by waterbody type (lakes vs. reservoirs), and that ecosystem morphometric variables (e.g., surface area and maximum depth) were more important predictors in lakes whereas metrics of autochthonous production (e.g., chlorophyll a) were more important in reservoirs. We also found that productivity strongly predicted methane ebullition, whereas ecosystem morphometry and waterbody type were more important predictors of diffusive methane flux. Finally, we identify several knowledge gaps that limit upscaling efforts. First, we need more methane emission measurements in small reservoirs, large lakes, and both natural and artificial ponds. Additionally, more accurate upscaling efforts require improved global information about waterbody surface area, waterbody type (lake vs. reservoir), ice phenology, and the distribution of productivity‐related predictor variables such as total phosphorus, DOC, and chlorophyll a.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JG005600","usgsCitation":"Deemer, B., and Holgerson, M.A., 2021, Drivers of methane flux differ between lakes and reservoirs, complicating global upscaling efforts: Journal of Geophysical Research-Biogeosciences, v. 126, no. 4, e2019JG005600, 15 p., https://doi.org/10.1029/2019JG005600.","productDescription":"e2019JG005600, 15 p.","ipdsId":"IP-112962","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":385017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holgerson, Meredith A.","contributorId":257243,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":51986,"text":"Departments of Biology and Environmental Studies, St. Olaf College, Northfield, Minnesota, USA","active":true,"usgs":false}],"preferred":false,"id":813863,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219451,"text":"70219451 - 2021 - Physics‐based evaluation of the maximum magnitude of potential earthquakes induced by the Hutubi (China) underground gas storage","interactions":[],"lastModifiedDate":"2021-04-22T17:57:05.216862","indexId":"70219451","displayToPublicDate":"2021-03-25T08:05:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Physics‐based evaluation of the maximum magnitude of potential earthquakes induced by the Hutubi (China) underground gas storage","docAbstract":"The world’s largest underground gas storage facility in Hutubi (HUGS), China, is a unique case where cyclic gas injection‐extraction induced both seismicity and ground deformation. To assess the potential for future induced seismicity, we develop a framework physically based on a well‐constrained hydro‐geomechanical model and on fully coupled poroelastic simulations. We first interpret the spatiotemporal distribution and focal mechanisms of induced earthquakes and use these to estimate the magnitude and location of the largest potential earthquake. The sharp increase in seismicity was controlled by poroelastic loading on secondary southwest‐dipping thrust faults with spatial scales too small to be resolved by 3D seismic surveys. Both operational and local geological factors affect the seismic productivity at the HUGS site, distinguishing it from most cases of seismicity induced by wastewater disposal and hydraulic fracturing. We then conduct slip tendency analyses for major faults imaged by the seismic data, including the largest reservoir‐bounding Hutubi fault hydraulically connected to injection wells. The reactivation potentials of these imaged faults are estimated to be extremely low. Accordingly, future seismicity would most likely occur on failure‐prone secondary faults in regions with positive stress perturbation due to poroelastic loading. The maximum magnitude likely depends on the spatial scales of the secondary faults. As the occurrence of detected earthquakes is spatially and temporally consistent with the simulated evolution of Coulomb stress perturbation, the location of the largest potential earthquake probably depends on the sizes of the poroelastic stressing regions.
Kemp’s ridley sea turtles Lepidochelys kempii were disproportionately affected by the Deepwater Horizon (DWH) oil spill, which began on 20 April 2010. Embryo deformities were documented in inviable L. kempii eggs before (2008-2010) and after (2011-2013) the DWH spill in 2 Texas (USA) nesting areas (Upper Texas Coast and Padre Island National Seashore). Additional nesting trends, including clutch size and hatching success, were also investigated. Total and late-stage embryo deformity prevalence were 1.5 times greater after 2010 than before, but low in all nesting seasons (mean ± SD: 0.7 ± 8.5% total; 0.6 ± 8.0% late-stage) and did not differ between locations. Craniofacial and carapace deformities were the most frequently observed deformity types. Documented nests in both areas declined in 2010 relative to previous years, ending an exponential increase observed beginning in 1995. Clutch size remained consistent before and after the spill. Hatching success averaged 87.0 ± 33.3% in all years, but no effects from DWH were determined. Collectively, these data represent useful benchmarks against which to judge impacts of future crude oil spills and other catastrophic events.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01107","usgsCitation":"Shaver, D.J., Gredzens, C., Walker, J.S., Godard-Codding, C., Yacabucci, J.E., Frey, A., Dutton, P., and Schmitt, C.J., 2021, Embryo deformities and nesting trends in Kemp’s ridley sea turtles Lepidochelys kempii before and after the Deepwater Horizon oil spill: Endangered Species Research, v. 44, p. 277-289, https://doi.org/10.3354/esr01107.","productDescription":"13 p.","startPage":"277","endPage":"289","ipdsId":"IP-122895","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":452949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01107","text":"Publisher Index Page"},{"id":384756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Padre Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.23724365234375,\n 27.63487379134253\n ],\n [\n -97.0751953125,\n 27.63487379134253\n ],\n [\n -97.0751953125,\n 27.715141756723987\n ],\n [\n -97.23724365234375,\n 27.715141756723987\n ],\n [\n -97.23724365234375,\n 27.63487379134253\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":813157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gredzens, Christian","contributorId":209784,"corporation":false,"usgs":false,"family":"Gredzens","given":"Christian","email":"","affiliations":[{"id":37980,"text":"Marine Turtle Research, Ecology and Conservation Group, Florida State University, Tallahassee, FL, USA 32306","active":true,"usgs":false}],"preferred":false,"id":813158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, J. Shelby","contributorId":256733,"corporation":false,"usgs":false,"family":"Walker","given":"J.","email":"","middleInitial":"Shelby","affiliations":[{"id":33240,"text":"National Park Service, Padre Island National Seashore, Corpus Christi, TX","active":true,"usgs":false}],"preferred":false,"id":813159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godard-Codding, Céline A. J.","contributorId":256734,"corporation":false,"usgs":false,"family":"Godard-Codding","given":"Céline A. J.","affiliations":[{"id":36344,"text":"The Institute of Environmental and Human Health, Texas Tech University, Lubbock, TX","active":true,"usgs":false}],"preferred":false,"id":813160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yacabucci, Janet E.","contributorId":256736,"corporation":false,"usgs":false,"family":"Yacabucci","given":"Janet","email":"","middleInitial":"E.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":813161,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frey, Amy","contributorId":196390,"corporation":false,"usgs":false,"family":"Frey","given":"Amy","email":"","affiliations":[],"preferred":false,"id":813163,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dutton, Peter H.","contributorId":256741,"corporation":false,"usgs":false,"family":"Dutton","given":"Peter H.","affiliations":[{"id":51846,"text":"NOAA Fisheries, Southwest Fisheries Science Center, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":813164,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmitt, Christopher J. 0000-0001-6804-2360 cjschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6804-2360","contributorId":491,"corporation":false,"usgs":true,"family":"Schmitt","given":"Christopher","email":"cjschmitt@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":813162,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219098,"text":"sir20205120 - 2021 - Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017","interactions":[],"lastModifiedDate":"2021-03-24T22:26:41.479816","indexId":"sir20205120","displayToPublicDate":"2021-03-24T15:35:00","publicationYear":"2021","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":"2020-5120","displayTitle":"Assessment of Water Quality and Discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017","title":"Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017","docAbstract":"The U.S. Geological Survey, Cape Cod National Seashore of the National Park Service, and Friends of Herring River cooperated from 2015 to 2017 to assess nutrient concentrations and fluxes across the ocean-estuary boundary at a dike on the Herring River in Wellfleet, Massachusetts. The purpose of this assessment was to characterize environmental conditions prior to a future removal of the dike, which has restricted saltwater inputs into the Herring River watershed for more than 100 years. Water temperature, dissolved oxygen, pH, and specific conductance were monitored continuously, and flow-weighted composite samples were collected approximately twice per month at the ocean-estuary boundary. Bidirectional discharge was computed for the U.S. Geological Survey Herring River at Chequessett Neck Road at Wellfleet, Massachusetts, streamgage (011058798) by using a stage-area rating and index-velocity ratings developed with acoustic Doppler current profile measurements made upstream and downstream from the dike. LOADEST regression modeling software was used to estimate nutrient fluxes (loads) from composite, paired nutrient concentration and discharge data in conjunction with continuous discharge data. Temperature, dissolved oxygen, pH, and specific conductance were also monitored continuously on two tributaries to the Herring River, Pole Dike Creek and Bound Brook, from late-May 2016 to mid-June 2017. Composite or discrete water samples were collected from the tributaries approximately twice per month in most months from late-May 2016 to mid-June 2017 and analyzed for total nitrogen, total phosphorus, and dissolved organic carbon.
Flow-weighted concentrations of ammonium, nitrate, and total nitrogen on the Herring River at the dike on the ebb tide generally varied between 0.01 and 0.1, 0.003 and 0.03, and 0.3 and 0.7 milligram per liter as nitrogen, respectively. Flow-weighted concentrations of orthophosphate, total dissolved phosphorus, and total phosphorus generally varied between 0.002 and 0.02, 0.003 and 0.06, and 0.03 and 0.1 milligram per liter as phosphorus, respectively, on the ebb tide. Flow-weighted concentrations of silicate and dissolved organic carbon on the ebb tide generally varied between 0.08 and 3.0 milligrams per liter of silica (silicon dioxide), and 1.7 and 5.6 milligrams per liter of carbon, respectively. Ebb tide concentrations of nitrate were highest in winter and lowest in summer. By contrast, ebb tide concentrations of phosphorus species were highest in late summer and early fall and lowest in winter. Silica and dissolved organic carbon did not exhibit systematic variation in seasonal concentrations. There was uncertainty in estimates of nutrient fluxes, but the LOADEST-estimated fluxes indicated that annual (and in almost all cases seasonal) exports (ebb tides) exceeded inputs (flood tides). Ebb tide concentrations of ammonium, nitrate, total nitrogen, and silica were positively correlated with antecedent cumulative 7-day precipitation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205120","collaboration":"Prepared in cooperation with the National Park Service and Friends of Herring River","usgsCitation":"Huntington, T.G., Spaetzel, A.B., Colman, J.A., Kroeger, K.D., and Bradley, R.T., 2021, Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017: U.S. Geological Survey Scientific Investigations Report 2020–5120, 59 p., https://doi.org/10.3133/sir20205120.","productDescription":"Report: x, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106718","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5120/coverthb.jpg"},{"id":384603,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BKW4BX","text":"USGS data release","linkHelpText":"Tidal daily discharge and quality assurance data supporting an assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015–September 2017"},{"id":384602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5120/sir20205120.pdf","text":"Report","size":"3.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5120"}],"country":"United States","state":"Massachusetts","city":"Wellfleet","otherGeospatial":"Herring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.07801055908203,\n 41.93318868195924\n ],\n [\n -69.99870300292969,\n 41.93318868195924\n ],\n [\n -69.99870300292969,\n 41.98833256890643\n ],\n [\n -70.07801055908203,\n 41.98833256890643\n ],\n [\n -70.07801055908203,\n 41.93318868195924\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Groundwater is the primary source of municipal water for Saipan. Nearly all groundwater for the municipal water supply is withdrawn from a freshwater-lens system with a limited amount of freshwater that is susceptible to saltwater intrusion. The status of Saipan’s groundwater resources has not been thoroughly assessed since 2003. The U.S. Geological Survey—in cooperation with the Office of Grants Management, Commonwealth of the Northern Mariana Islands, and in collaboration with the Commonwealth Utilities Corporation—assessed the status and characteristics of Saipan’s groundwater resources by (1) evaluating groundwater withdrawals from municipal production wells during 2014–19, (2) evaluating chloride concentrations of municipal groundwater withdrawals during 2009–19, and (3) collecting salinity profiles at selected groundwater-monitoring wells during 2018–19. At the time of preparation of this report (2019), the periods of groundwater-withdrawal and chloride-concentration data represent the only periods of data available since 2003.
During 2014–19, groundwater for the municipal water supply was withdrawn from about 143 production wells. Most of the wells are drilled into limestone formations in the southern plateau and the Kagman Peninsula and generally have withdrawal rates of about 40–60 gallons per minute. Records of monthly groundwater withdrawals from municipal production wells were available for May 2014–March 2019; during that period, monthly withdrawals ranged from 5.7 to 12.8 million gallons per day (Mgal/d) and averaged 9.3 Mgal/d, although records were unavailable for 9 months (May 2015–January 2016). Private wells, mainly located on the western coastal plain, currently are permitted to withdraw a total of about 7 Mgal/d of groundwater. Actual groundwater withdrawals from private wells, however, are uncertain because withdrawal records for private wells are not available.
The Commonwealth Utilities Corporation measured the chloride concentration of groundwater pumped from each of its production wells about twice a year from 2009–19; during this period, 146 production wells were active and sampled. Only 32 of the 146 (22 percent) municipal production wells had median chloride concentrations less than or equal to 250 milligrams per liter (mg/L), the secondary drinking water standard set by the U.S. Environmental Protection Agency. Eighty-one wells (55 percent) pumped water with median chloride concentrations above 500 mg/L.
The Mann-Kendall test was used to determine if chloride concentrations of groundwater withdrawals at 146 municipal production wells had statistically significant trends during December 2009–February 2019. Trends were considered statistically significant for probability values (p-values) less than or equal to 0.05. Test results indicate an upward trend at 9 wells, a downward trend at 52 wells, and no trend at 85 wells.
Salinity profiles were measured in 12 selected monitor wells during July–August 2018 and were measured in six of the twelve selected monitor wells during March 2019. The salinity profiles were used to estimate the thickness of the freshwater lens at 10 monitor wells; freshwater-lens thickness was greatest (46 ft) in a monitor well in the Dan Dan well field near the northern part of the southern plateau. Freshwater-lens-thickness estimates elsewhere were (1) between 0 and 28 ft for the remaining monitor wells on the southern plateau, (2) between 19 and 21 ft for monitor wells on the Kagman Peninsula, (3) 2 ft for a monitor well in the Sablan Quarry well field on west-central Saipan, and (4) 8 ft for a monitor well in the Marpi Quarry well field on northern Saipan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205129","collaboration":"Prepared in cooperation with the Office of Grants Management and in collaboration with the Commonwealth Utilities Corporation, Commonwealth of the Northern Mariana Islands","usgsCitation":"Mitchell, J.N., Presley, T.K., and Carruth, R.L., 2021, Groundwater conditions and trends, 2009–19, Saipan, Commonwealth of the Northern Mariana Islands: U.S. Geological Survey Scientific Investigations Report 2020–5129, 51 p., https://doi.org/10.3133/sir20205129.","productDescription":"vii, 51 p.","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-111052","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":384606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5129/sir20205129.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5129/covrthb.jpg"}],"country":"United States","otherGeospatial":"Commonwealth of the Norhtern Marianas Islands, Saipan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 145.67665100097656,\n 15.083394661897604\n ],\n [\n 145.83595275878906,\n 15.083394661897604\n ],\n [\n 145.83595275878906,\n 15.339153696147529\n ],\n [\n 145.67665100097656,\n 15.339153696147529\n ],\n [\n 145.67665100097656,\n 15.083394661897604\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Proliferative kidney disease (PKD) is an emerging disease that recently resulted in a large mortality event of salmonids in the Yellowstone River (Montana, USA). Total PKD fish mortalities in the Yellowstone River were estimated in the tens of thousands, which resulted in a multi‐week river closure and an estimated economic loss of US$500,000. This event shocked scientists, managers, and the public, as this was the first occurrence of the disease in the Yellowstone River, the only reported occurrence of the disease in Montana in the past 25 yr, and arguably the largest wild PKD fish kill in the world. To understand why the Yellowstone River fish kill occurred, we used molecular and historical data to evaluate evidence for several hypotheses: Was the causative parasite Tetracapsuloides bryosalmonae a novel invader, was the fish kill associated with a unique parasite strain, and/or was the outbreak caused by unprecedented environmental conditions? We found that T. bryosalmonae is widely distributed in Montana and have documented occurrence of this parasite in archived fish collected in the Yellowstone River prior to the fish kill. T. bryosalmonae had minimal phylogeographic population structure, as the DNA of parasites sampled from the Yellowstone River and distant water bodies were very similar. These results suggest that T. bryosalmonae could be endemic in Montana. Due to data limitations, we could not reject the hypothesis that the fish kill was caused by a novel and more virulent genetic strain of the parasite. Finally, we found that single‐year environmental conditions are insufficient to explain the cause of the 2016 Yellowstone River PKD outbreak. Other regional rivers where we documented T. bryosalmonae had similar or even more extreme conditions than the Yellowstone River and similar or more extreme conditions have occurred in the Yellowstone River in the recent past, yet mass PKD mortalities have not been documented in either instance. We conclude by placing these results and unresolved hypotheses into the broader context of international research on T. bryosalmonae and PKD, which strongly suggests that a better understanding of bryozoans, the primary host of T. bryosalmonae, is required for better ecosystem understanding.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3436","usgsCitation":"Hutchins, P., Sepulveda, A., Hartikainen, H., Staigmiller, K.D., Opitz, S.T., Yamamoto, R.M., Huttinger, A., Cordes, R.J., Weiss, T., Hopper, L.R., Purcell, M.K., and Okamura, B., 2021, Exploration of the 2016 Yellowstone River fish kill and proliferative kidney disease in wild fish populations: Ecosphere, v. 3, no. 12, e03436, 20 p., https://doi.org/10.1002/ecs2.3436.","productDescription":"e03436, 20 p.","ipdsId":"IP-120532","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":452955,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3436","text":"Publisher Index Page"},{"id":436440,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95XKXE1","text":"USGS data release","linkHelpText":"T. bryosalmonae detection in fish and water, DNA sequence, and simple sequence repeat data collected in the Inter-Mountain West from 2011 to 2019"},{"id":384695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Madison River, Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.423095703125,\n 45.40616374516014\n ],\n [\n -111.368408203125,\n 45.40616374516014\n ],\n [\n -111.368408203125,\n 46.27103747280261\n ],\n [\n -112.423095703125,\n 46.27103747280261\n ],\n [\n -112.423095703125,\n 45.40616374516014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchins, Patrick Ross 0000-0001-5232-0821","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":256658,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick Ross","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartikainen, Hanna","contributorId":256659,"corporation":false,"usgs":false,"family":"Hartikainen","given":"Hanna","email":"","affiliations":[{"id":39130,"text":"University of Nottingham","active":true,"usgs":false}],"preferred":false,"id":812993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staigmiller, Ken D.","contributorId":256661,"corporation":false,"usgs":false,"family":"Staigmiller","given":"Ken","email":"","middleInitial":"D.","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":812994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Opitz, Scott T.","contributorId":256663,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott","email":"","middleInitial":"T.","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":812995,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yamamoto, Renee M.","contributorId":256665,"corporation":false,"usgs":false,"family":"Yamamoto","given":"Renee","email":"","middleInitial":"M.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huttinger, Amberly","contributorId":256668,"corporation":false,"usgs":false,"family":"Huttinger","given":"Amberly","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812997,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cordes, Rick J.","contributorId":256670,"corporation":false,"usgs":false,"family":"Cordes","given":"Rick","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812998,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weiss, Tammy","contributorId":256672,"corporation":false,"usgs":false,"family":"Weiss","given":"Tammy","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812999,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hopper, Lacey R.","contributorId":206813,"corporation":false,"usgs":false,"family":"Hopper","given":"Lacey","email":"","middleInitial":"R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":813000,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813001,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Okamura, Beth","contributorId":256676,"corporation":false,"usgs":false,"family":"Okamura","given":"Beth","email":"","affiliations":[{"id":51827,"text":"Natural History Museum","active":true,"usgs":false}],"preferred":false,"id":813002,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70218832,"text":"sir20205106 - 2021 - Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin","interactions":[],"lastModifiedDate":"2021-03-24T21:57:54.814314","indexId":"sir20205106","displayToPublicDate":"2021-03-24T09:50:00","publicationYear":"2021","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":"2020-5106","displayTitle":"Assessment of Contaminant Trends in Plumes and Wells and Monitoring Network Optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin","title":"Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin","docAbstract":"Soil and groundwater at the Badger Army Ammunition Plant (BAAP), Sauk County, Wisconsin, were affected by several contaminants as a result of production and waste disposal practices common during its operation from 1942 to 1975. Three distinct plumes of contaminated groundwater originate on BAAP property and extend off-site, as identified by previous studies. Routine sampling of groundwater quality from a network of monitoring wells and off-site private wells has been performed since 1990, although the number of wells monitored and the monitoring frequency have varied as the approved monitoring plan was modified. During the period of monitoring from 1990 to 2018, numerous site investigations and remedial actions were conducted to address the sources of contamination, contaminated soils, and groundwater. Concentrations of contaminants reportedly decreased between 2000 and 2012 within all three plumes. Five or six contaminants of concern (COCs) were identified for each of the three plumes. An independent assessment of the contaminant plumes and of the monitoring network was conducted using groundwater-quality data collected from more than 600 wells between 2000 and 2018.
In a study conducted by the U.S. Geological Survey (USGS), in cooperation with the Army Environmental Command, a consistent data aggregation and interpolation scheme was applied to derive the likely maximum groundwater plume extents in four 3-year time periods between 2000 and 2018. The plume extent was defined by the Enforcement Standard for each COC and represents the maximum concentration observed in each 3-year time period. The plume boundary analysis shows that the spatial extent of groundwater contamination decreased for most COCs during the study period. Some plume boundaries are not well delineated by the existing monitoring network, particularly the downgradient edge of the Propellant Burning Ground plume. Maps identify the plume boundary in each time period, the sampling well network used to delineate the plume, and wells that were sampled in the 2010–12 period but not sampled in the 2015–18 period.
A series of statistical analyses using the Monitoring and Remediation Optimization System, version 3.0, program were applied to the available COC concentration data for two distinct periods, 2000 to 2012 and 2013 to 2018, with the break between periods coinciding with changes to the monitoring network in 2013. Trends in the concentration of COCs in individual wells varied, although generally more wells had decreasing than had increasing concentrations for most COCs in both time periods. The exceptions were ethyl ether in the 2004–12 period and 2,6-dinitrotoluene in the 2013–18 period, for which more wells had an increasing trend. Spatial moment analysis of concentration data from the well network was used to assess the stability of each plume for the COCs. During the 2000–12 period, most of the contaminant plumes for which data were sufficient to complete the analysis were either decreasing or stable in mass and size. The exceptions were carbon tetrachloride (associated solely with the Propellant Burning Ground plume) and 2,4-dinitrotoluene and 2,6-dinitrotoluene (in the Deterrent Burning Ground plume), which showed an increasing trend in mass. No COCs showed an increasing trend in plume mass in the 2013–18 period. Some wells with increasing trends in concentration or with concentrations greater than the enforcement standard are near the tail of a plume, where increased monitoring may be of value to better define future plume boundaries. A spatial optimization analysis covering the 2013–18 period identified six wells that provided information redundant to that from other wells. A temporal optimization analysis identified optimal sampling frequencies for 125 wells. Remedial actions directed at the Propellant Burning Ground plume coincided with a general decrease in plume mass and size, although in specific areas and depths, the plume size for specific contaminants may still be increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205106","collaboration":"Prepared in cooperation with the Army Environmental Command","usgsCitation":"Pajerowski, M., Goodling, P., and Metes, M., 2021, Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5106, 80 p., https://doi.org/10.3133/sir20205106.","productDescription":"Report: x, 80 p.; Data Release; 16 Plates","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118955","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":384411,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5106/sir20205106_plates.pdf","text":"Plates 1 through 16","size":"189 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5106/coverthb.jpg"},{"id":384402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5106/sir20205106.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5106"},{"id":384403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UKYNR","text":"USGS data release","linkHelpText":"Groundwater quality and plume boundaries for select contaminants of concern at Badger Army Ammunition Plant, Wisconsin (2000–2018)"}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.77375030517578,\n 43.30694264971061\n ],\n [\n -89.67041015625,\n 43.30694264971061\n ],\n [\n -89.67041015625,\n 43.420634784134876\n ],\n [\n -89.77375030517578,\n 43.420634784134876\n ],\n [\n -89.77375030517578,\n 43.30694264971061\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
The San Andreas fault has the highest calculated time-dependent probability for large-magnitude earthquakes in southern California. However, where the fault is multistranded east of the Los Angeles metropolitan area, it has been uncertain which strand has the fastest slip rate and, therefore, which has the highest probability of a destructive earthquake. Reconstruction of offset Pleistocene-Holocene landforms dated using the uranium-thorium soil carbonate and beryllium-10 surface exposure techniques indicates slip rates of 24.1 ± 3 millimeter per year for the San Andreas fault, with 21.6 ± 2 and 2.5 ± 1 millimeters per year for the Mission Creek and Banning strands, respectively. These data establish the Mission Creek strand as the primary fault bounding the Pacific and North American plates at this latitude and imply that 6 to 9 meters of elastic strain has accumulated along the fault since the most recent surface-rupturing earthquake, highlighting the potential for large earthquakes along this strand.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.aaz5691","usgsCitation":"Blisniuk, K., Scharer, K., Sharp, W., Burgmann, R., Amos, C., and Rymer, M., 2021, A revised position for the primary strand of the Pleistocene-Holocene San Andreas fault in southern California: Science Advances, v. 7, eaaz5691, 15 p., https://doi.org/10.1126/sciadv.aaz5691.","productDescription":"eaaz5691, 15 p.","ipdsId":"IP-111194","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":452956,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.aaz5691","text":"External Repository"},{"id":406838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.21612548828124,\n 33.527658137677335\n ],\n [\n -116.004638671875,\n 33.71748624018193\n ],\n [\n -116.8011474609375,\n 34.21634468843463\n ],\n [\n -116.971435546875,\n 33.93880275084578\n ],\n [\n -116.21612548828124,\n 33.527658137677335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blisniuk, Kim","contributorId":296614,"corporation":false,"usgs":false,"family":"Blisniuk","given":"Kim","email":"","affiliations":[{"id":24620,"text":"San Jose State University","active":true,"usgs":false}],"preferred":false,"id":851987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharp, Warren","contributorId":295386,"corporation":false,"usgs":false,"family":"Sharp","given":"Warren","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":851989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burgmann, Roland 0000-0002-3560-044X","orcid":"https://orcid.org/0000-0002-3560-044X","contributorId":264610,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","email":"","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":851990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amos, Colin","contributorId":196408,"corporation":false,"usgs":false,"family":"Amos","given":"Colin","affiliations":[],"preferred":false,"id":851991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rymer, Michael 0000-0002-5429-5073 mrymer@usgs.gov","orcid":"https://orcid.org/0000-0002-5429-5073","contributorId":220757,"corporation":false,"usgs":true,"family":"Rymer","given":"Michael","email":"mrymer@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851992,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220559,"text":"70220559 - 2021 - Across borders: External factors and prior behaviour influence North Pacific albatross associations with fishing vessels","interactions":[],"lastModifiedDate":"2021-06-30T18:56:32.323965","indexId":"70220559","displayToPublicDate":"2021-03-24T07:45:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Across borders: External factors and prior behaviour influence North Pacific albatross associations with fishing vessels","docAbstract":"Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13061210","usgsCitation":"Benedict, T.D., Brown, J.F., Boyte, S., Howard, D., Fuchs, B., Wardlow, B.D., Tadesse, T., and Evenson, K., 2021, Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions: Remote Sensing, v. 13, no. 6, 1210, 17 p., https://doi.org/10.3390/rs13061210.","productDescription":"1210, 17 p.","ipdsId":"IP-126651","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13061210","text":"Publisher Index Page"},{"id":384696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 37.68382032669382\n ],\n [\n -94.833984375,\n 37.68382032669382\n ],\n [\n -94.833984375,\n 39.977120098439634\n ],\n [\n -98.61328125,\n 39.977120098439634\n ],\n [\n -98.61328125,\n 37.68382032669382\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Benedict, Trenton D 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":256662,"corporation":false,"usgs":false,"family":"Benedict","given":"Trenton","email":"","middleInitial":"D","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":812984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":812985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":256667,"corporation":false,"usgs":false,"family":"Howard","given":"Daniel","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuchs, Brian","contributorId":192359,"corporation":false,"usgs":false,"family":"Fuchs","given":"Brian","email":"","affiliations":[],"preferred":false,"id":812987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":812988,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":812989,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Evenson, Kirk","contributorId":256674,"corporation":false,"usgs":false,"family":"Evenson","given":"Kirk","email":"","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812990,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70222461,"text":"70222461 - 2021 - Genetic structure of Maryland Brook Trout populations: Management implications for a threatened species","interactions":[],"lastModifiedDate":"2021-09-14T16:37:31.761678","indexId":"70222461","displayToPublicDate":"2021-03-23T08:49:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Genetic structure of Maryland Brook Trout populations: Management implications for a threatened species","docAbstract":"Brook Trout Salvelinus fontinalis have declined across their native range due to multiple anthropogenic factors, including landscape alteration and climate change. Although coldwater streams in Maryland (eastern United States) historically supported significant Brook Trout populations, only fragmented remnant populations remain, with the exception of the upper Savage River watershed in western Maryland. Using microsatellite data from 38 collections, we defined genetic relationships of Brook Trout populations in Maryland drainages. Microsatellite analyses of Brook Trout indicated the presence of five major discrete units defined as the Youghiogheny (Ohio), Susquehanna, Patapsco/Gunpowder, Catoctin, and Upper Potomac, with a distinct genetic subunit present in the Savage River (upper Potomac). We did not observe evidence for widespread hatchery introgression with native Brook Trout. However, genetic effects due to fragmentation were evident in several Maryland Brook Trout populations, resulting in erosion of diversity that may have negative implications for their future persistence. Our current study supplements an increasing body of evidence that Brook Trout populations in Maryland are highly susceptible to multiple anthropogenic stresses, and many populations may be extirpated in the near future. Future management efforts focused on habitat protection and potential stream restoration, coupled with a comprehensive assessment framework that includes genetic considerations, may provide the best outlook for Brook Trout populations in Maryland.
A multi-component geochemical dataset was collected from groundwater and surface-water bodies associated with the urban Fountain Creek alluvial aquifer, Colorado, USA, to facilitate analysis of recharge sources, geochemical interactions, and groundwater-residence times. Results indicate that groundwater can be separated into three distinct geochemical zones based on location within the flow system and proximity to surface water, and these zones can be used to infer sources of recharge and groundwater movement through the aquifer. Rare-earth-element concentrations and detections of wastewater-indicator compounds indicate the presence of effluent from wastewater-treatment plants in both groundwater and surface water. Effluent presence in groundwater indicates that streams in the area lose to groundwater in some seasons and are a source of focused groundwater recharge. Distributions of pharmaceuticals and wastewater-indicator compounds also inform an understanding of groundwater–surface-water interactions. Noble-gas isotopes corroborate rare-earth-element data in indicating geochemical evolution within the aquifer from recharge area to discharge area and qualitatively indicate variable groundwater-residence times and mixing with pre-modern groundwater. Quantitative groundwater-residence times calculated from 3H/3He, SF6, and lumped-parameter modeling generally are less than 20 years, but the presence of mixing with older groundwater of an unknown age is also indicated at selected locations. Future investigations would benefit by including groundwater-age tracers suited to quantification of mixing for both young (years to decades) and old (centuries and millennia) groundwater. This multi-faceted analysis facilitated development of a conceptual model for the investigated groundwater-flow system and illustrates the application of an encompassing suite of analytes in exploring hydrologic and geochemical interactions in complex systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w13060871","usgsCitation":"Newman, C.P., Paschke, S.S., and Keith, G.L., 2021, Natural and anthropogenic geochemical tracers to investigate residence times and groundwater–surface-water interactions in an urban alluvial aquifer: Water, v. 13, no. 6, 30 p., https://doi.org/10.3390/w13060871.","productDescription":"30 p.","ipdsId":"IP-118155","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":452974,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13060871","text":"Publisher Index Page"},{"id":436443,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99SPQM2","text":"USGS data release","linkHelpText":"Environmental-tracer modeling to support hydrogeochemical evaluation of the Fountain Creek Alluvial Aquifer, El Paso County, Colorado, 2018-2019"},{"id":384712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Colorado Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.2490234375,\n 38.61687046392973\n ],\n [\n -104.1888427734375,\n 38.61687046392973\n ],\n [\n -104.1888427734375,\n 39.16839998800286\n ],\n [\n -105.2490234375,\n 39.16839998800286\n ],\n [\n -105.2490234375,\n 38.61687046392973\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paschke, Suzanne S. 0000-0002-3471-4242 spaschke@usgs.gov","orcid":"https://orcid.org/0000-0002-3471-4242","contributorId":1347,"corporation":false,"usgs":true,"family":"Paschke","given":"Suzanne","email":"spaschke@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keith, Gabrielle L. 0000-0002-2304-8504 gkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-2304-8504","contributorId":256699,"corporation":false,"usgs":true,"family":"Keith","given":"Gabrielle","email":"gkeith@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813077,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259605,"text":"70259605 - 2021 - Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor","interactions":[],"lastModifiedDate":"2024-10-17T12:04:01.889156","indexId":"70259605","displayToPublicDate":"2021-03-23T07:01:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor","docAbstract":"The rate of occurrence of anomalous ultra-low frequency electromagnetic (ULFEM) pulses has been claimed to have increased days to weeks prior to the M5.4 2007 and M4.0 2010 Alum Rock earthquakes. We re-examine the previously reported ultra-low frequency (ULF: 0.01–10 Hz) magnetic data recorded at a QuakeFinder site located 9 km from the earthquake hypocentre, and compare to data from a nearby Stanford-USGS site located 42 km from the hypocentre, to analyse the characteristics of the pulses and assess their origin. Using pulse definitions and pulse-counting algorithms analogous to those previously reported, we corroborate the increase in pulse counts before the 2007 Alum Rock earthquake at the QuakeFinder station, but we note that the number of pulses depends on chosen temporal and amplitude detection thresholds. These thresholds are arbitrary because we lack a clear physical model or basis for their selection. We do not see the same increase in pulse counts before the 2010 Alum Rock earthquake at the QuakeFinder or Stanford-USGS stations. In addition, the majority of pulses in the QuakeFinder data and Stanford-USGS data do not match temporally, indicating the pulses lack a common origin and are not from lightning or solar-driven ionospheric/magnetospheric disturbances. Our assessment of the temporal distribution of pulse counts shows pulse counts increase during peak human activity hours, suggesting these pulses result from local cultural noise and are not tectonic in origin. The many unknowns about the character and even existence of precursory earthquake pulses means that standard numerical and statistical tests cannot easily be applied. Yet here we show that exhaustive investigation of many different aspects of ULFEM signals can be used to properly characterize their origin.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab530","usgsCitation":"Wang, C., Christman, L., Klemperer, S., Glen, J.M., McPhee, D., and Bin, C., 2021, Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor: Geophysical Journal International, v. 229, no. 3, p. 2081-2095, https://doi.org/10.1093/gji/ggab530.","productDescription":"15 p.","startPage":"2081","endPage":"2095","ipdsId":"IP-136197","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggab530","text":"Publisher Index Page"},{"id":462937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.74855580223482,\n 37.923464830974424\n ],\n [\n -122.74855580223482,\n 37.22689363596939\n ],\n [\n -121.73781361473472,\n 37.22689363596939\n ],\n [\n -121.73781361473472,\n 37.923464830974424\n ],\n [\n -122.74855580223482,\n 37.923464830974424\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"229","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Can","contributorId":345181,"corporation":false,"usgs":false,"family":"Wang","given":"Can","email":"","affiliations":[{"id":82515,"text":"Institute of Geophysics, China Earthquake Administration, Beijing 100081, P.R. China","active":true,"usgs":false}],"preferred":false,"id":915919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christman, Lilianna","contributorId":345182,"corporation":false,"usgs":false,"family":"Christman","given":"Lilianna","email":"","affiliations":[],"preferred":false,"id":915920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klemperer, Simon","contributorId":345183,"corporation":false,"usgs":false,"family":"Klemperer","given":"Simon","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":915921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":915922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McPhee, Darcy 0000-0002-5177-3068 dmcphee@usgs.gov","orcid":"https://orcid.org/0000-0002-5177-3068","contributorId":2621,"corporation":false,"usgs":true,"family":"McPhee","given":"Darcy","email":"dmcphee@usgs.gov","affiliations":[{"id":412,"text":"National Cooperative Geologic Mapping Program","active":false,"usgs":true}],"preferred":true,"id":915923,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bin, Chen","contributorId":345184,"corporation":false,"usgs":false,"family":"Bin","given":"Chen","email":"","affiliations":[{"id":82515,"text":"Institute of Geophysics, China Earthquake Administration, Beijing 100081, P.R. China","active":true,"usgs":false}],"preferred":false,"id":915924,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219055,"text":"fs20213018 - 2021 - Earth Resources Observation and Science Center hosting services","interactions":[],"lastModifiedDate":"2021-03-23T12:09:25.892839","indexId":"fs20213018","displayToPublicDate":"2021-03-22T15:33:57","publicationYear":"2021","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":"2021-3018","displayTitle":"Earth Resources Observation and Science Center Hosting Services","title":"Earth Resources Observation and Science Center hosting services","docAbstract":"The Earth Resources Observation and Science (EROS) Center has a long history of leveraging technology in support of Earth science and business applications including data management, processing, and virtualization and complex solutions to visualize and distribute data. It is the aim of EROS to offer operational excellence and service as a key component to the Federal Cloud-Smart directives and Department of the Interior data center consolidation goals. The EROS Center offers space, network, security, and environmental controls and information technology service management. Included with leveraging technology, the EROS Center has industry-leading staff to assist with supporting Earth science and business applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213018","usgsCitation":"U.S. Geological Survey, 2021, Earth Resources Observation and Science Center hosting services: U.S. Geological Survey Fact Sheet 2021–3018, 2 p., https://doi.org/10.3133/fs20213018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-124389","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":384545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3018/coverthb.jpg"},{"id":384546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3018/fs20213018.pdf","text":"Report","size":"737 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3018"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report documents the computer software package, Basin Characterization Model, version 8 (BCMv8)—a monthly, gridded, regional water-balance model—and provides detailed operational instructions and example applications. After several years of many applications and uses of a previous version, CA-BCM, published in 2014, the BCMv8 was refined to improve the accuracy of the water-balance components, particularly the recharge estimate, which is the most difficult to accurately assess. The improvement of the various water-balance components targeted the actual evapotranspiration component, which, in turn, reduced the uncertainty of the recharge estimate. The improvement of this component was enabled by the availability of a national, gridded actual-evapotranspiration product from the U.S. Geological Survey that was unique in its scope to combine remotely sensed spatial variability and ground-based long-term water-balance constraints. This dataset provided the ability to assess monthly actual evapotranspiration for 62 vegetation types and to perform regional calibration in watersheds throughout California with the objective of closing the water balance using improved estimates for each component. The refinements, including vegetation-specific evapotranspiration, enabled the development of applications that could explore various aspects of landscape disturbance, such as wildfire, forest management, or urbanization. The improvements to BCMv8 also provided the ability to assess long-term sustainability of water resources under a variety of management applications or future climate projections.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6H1","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Flint, L.E., Flint, A.L., and Stern, M.A., 2021, The basin characterization model—A regional water balance software package: U.S. Geological Survey Techniques and Methods 6–H1, 85 p., https://doi.org/10.3133/tm6H1.","productDescription":"x, 85 p.","numberOfPages":"85","onlineOnly":"Y","ipdsId":"IP-101075","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":436445,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K23J25","text":"USGS data release","linkHelpText":"Future Climate and Hydrology from Twenty Localized Constructed Analog (LOCA) Scenarios and the Basin Characterization Model (BCMv8)"},{"id":384554,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/06/h01/images"},{"id":384553,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/06/h01/tm6h1.xml"},{"id":384551,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/h01/covrthb.jpg"},{"id":384552,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/h01/tm6h1.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384678,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PT36UI","linkHelpText":"The Basin Characterization Model—A regional water balance software package (BCMv8) data release and model archive for hydrologic California, water years 1896-2020"}],"country":"United States","state":"California","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-122.421439,37.869969],[-122.41847,37.852721],[-122.434403,37.852434],[-122.446316,37.861046],[-122.430958,37.872242],[-122.421439,37.869969]]],[[[-122.3785,37.826505],[-122.377879,37.830648],[-122.369941,37.832137],[-122.358779,37.814278],[-122.362661,37.807577],[-122.372422,37.811301],[-122.3785,37.826505]]],[[[-120.248484,33.999329],[-120.230001,34.010136],[-120.19578,34.004284],[-120.167306,34.008219],[-120.147647,34.024831],[-120.140362,34.025974],[-120.115058,34.019866],[-120.090182,34.019806],[-120.073609,34.024477],[-120.057637,34.03734],[-120.043259,34.035806],[-120.050382,34.013331],[-120.046575,34.000002],[-120.011123,33.979894],[-119.978876,33.983081],[-119.979913,33.969623],[-119.97026,33.944359],[-120.017715,33.936366],[-120.048611,33.915775],[-120.098601,33.907853],[-120.121817,33.895712],[-120.168974,33.91909],[-120.224461,33.989059],[-120.248484,33.999329]]],[[[-119.789798,34.05726],[-119.755521,34.056716],[-119.712576,34.043265],[-119.686507,34.019805],[-119.637742,34.013178],[-119.612226,34.021256],[-119.604287,34.031561],[-119.608798,34.035245],[-119.59324,34.049625],[-119.5667,34.053452],[-119.52064,34.034262],[-119.542449,34.021082],[-119.547072,34.005469],[-119.560464,33.99553],[-119.575636,33.996009],[-119.596877,33.988611],[-119.662825,33.985889],[-119.721206,33.959583],[-119.742966,33.963877],[-119.758141,33.959212],[-119.842748,33.97034],[-119.873358,33.980375],[-119.884896,34.008814],[-119.876329,34.032087],[-119.916216,34.058351],[-119.923337,34.069361],[-119.919155,34.07728],[-119.912857,34.077508],[-119.857304,34.071298],[-119.825865,34.059794],[-119.818742,34.052997],[-119.789798,34.05726]]],[[[-120.46258,34.042627],[-120.440248,34.036918],[-120.415287,34.05496],[-120.403613,34.050442],[-120.390906,34.051994],[-120.368813,34.06778],[-120.370176,34.074907],[-120.362251,34.073056],[-120.354982,34.059256],[-120.36029,34.05582],[-120.358608,34.050235],[-120.346946,34.046576],[-120.331161,34.049097],[-120.302122,34.023574],[-120.317052,34.018837],[-120.347706,34.020114],[-120.35793,34.015029],[-120.409368,34.032198],[-120.427408,34.025425],[-120.454134,34.028081],[-120.465329,34.038448],[-120.46258,34.042627]]],[[[-118.524531,32.895488],[-118.535823,32.90628],[-118.551134,32.945155],[-118.573522,32.969183],[-118.586928,33.008281],[-118.596037,33.015357],[-118.606559,33.01469],[-118.605534,33.030999],[-118.594033,33.035951],[-118.57516,33.033961],[-118.569013,33.029151],[-118.559171,33.006291],[-118.540069,32.980933],[-118.496811,32.933847],[-118.369984,32.839273],[-118.353504,32.821962],[-118.356541,32.817311],[-118.379968,32.824545],[-118.394565,32.823978],[-118.425634,32.800595],[-118.44492,32.820593],[-118.496298,32.851572],[-118.507193,32.876264],[-118.524531,32.895488]]],[[[-118.500212,33.449592],[-118.477646,33.448392],[-118.445812,33.428907],[-118.423576,33.427258],[-118.382037,33.409883],[-118.370323,33.409285],[-118.365094,33.388374],[-118.310213,33.335795],[-118.303174,33.320264],[-118.305084,33.310323],[-118.325244,33.299075],[-118.374768,33.320065],[-118.440047,33.318638],[-118.465368,33.326056],[-118.48877,33.356649],[-118.478465,33.38632],[-118.48875,33.419826],[-118.515914,33.422417],[-118.52323,33.430733],[-118.53738,33.434608],[-118.563442,33.434381],[-118.60403,33.47654],[-118.54453,33.474119],[-118.500212,33.449592]]],[[[-119.543842,33.280329],[-119.528141,33.284929],[-119.465717,33.259239],[-119.429559,33.228167],[-119.444269,33.21919],[-119.476029,33.21552],[-119.545872,33.233406],[-119.564971,33.24744],[-119.578942,33.278628],[-119.562042,33.271129],[-119.543842,33.280329]]],[[[-122.289533,42.007764],[-121.035195,41.993323],[-120.001058,41.995139],[-119.995926,40.499901],[-120.005743,39.228664],[-120.001014,38.999574],[-119.333423,38.538328],[-118.714312,38.102185],[-117.875927,37.497267],[-117.244917,37.030244],[-116.488233,36.459097],[-115.852908,35.96966],[-115.102881,35.379371],[-114.633013,35.002085],[-114.629015,34.986148],[-114.634953,34.958918],[-114.629753,34.938684],[-114.635176,34.875003],[-114.623939,34.859738],[-114.586842,34.835672],[-114.57101,34.794294],[-114.552682,34.766871],[-114.516619,34.736745],[-114.470477,34.711368],[-114.452628,34.668546],[-114.451753,34.654321],[-114.441465,34.64253],[-114.438739,34.621455],[-114.424202,34.610453],[-114.429747,34.591734],[-114.422382,34.580711],[-114.405228,34.569637],[-114.380838,34.529724],[-114.378124,34.507288],[-114.386699,34.457911],[-114.375789,34.447798],[-114.335372,34.450038],[-114.32613,34.437251],[-114.294836,34.421389],[-114.286802,34.40534],[-114.264317,34.401329],[-114.226107,34.365916],[-114.199482,34.361373],[-114.176909,34.349306],[-114.157206,34.317862],[-114.138282,34.30323],[-114.134768,34.268965],[-114.139055,34.259538],[-114.159697,34.258242],[-114.223384,34.205136],[-114.229715,34.186928],[-114.254141,34.173831],[-114.287294,34.170529],[-114.320777,34.138635],[-114.353031,34.133121],[-114.366521,34.118575],[-114.390565,34.110084],[-114.411681,34.110031],[-114.43338,34.088413],[-114.43934,34.057893],[-114.434949,34.037784],[-114.438266,34.022609],[-114.46283,34.008421],[-114.46117,33.994687],[-114.499883,33.961789],[-114.522002,33.955623],[-114.535478,33.934651],[-114.533679,33.926072],[-114.508558,33.906098],[-114.518555,33.889847],[-114.50434,33.876882],[-114.503017,33.867998],[-114.514673,33.858638],[-114.52453,33.858477],[-114.529597,33.848063],[-114.520465,33.827778],[-114.527161,33.816191],[-114.504863,33.760465],[-114.504483,33.750998],[-114.512348,33.734214],[-114.496565,33.719155],[-114.494197,33.707922],[-114.495719,33.698454],[-114.523959,33.685879],[-114.531523,33.675108],[-114.525201,33.661583],[-114.530244,33.65014],[-114.526947,33.637534],[-114.529662,33.622794],[-114.524813,33.611351],[-114.540617,33.591412],[-114.5403,33.580615],[-114.524391,33.553683],[-114.558898,33.531819],[-114.560552,33.518272],[-114.569533,33.509219],[-114.591554,33.499443],[-114.622918,33.456561],[-114.627125,33.433554],[-114.635183,33.422726],[-114.652828,33.412922],[-114.687953,33.417944],[-114.701732,33.408388],[-114.725535,33.404056],[-114.708408,33.384147],[-114.698035,33.352442],[-114.707962,33.323421],[-114.731223,33.302434],[-114.723259,33.288079],[-114.684363,33.276025],[-114.672401,33.26047],[-114.689421,33.24525],[-114.674479,33.225504],[-114.678749,33.203448],[-114.675831,33.18152],[-114.679359,33.159519],[-114.703682,33.113769],[-114.706488,33.08816],[-114.68902,33.084036],[-114.686991,33.070969],[-114.674296,33.057171],[-114.673659,33.041897],[-114.662317,33.032671],[-114.64598,33.048903],[-114.618788,33.027202],[-114.589778,33.026228],[-114.575161,33.036542],[-114.52013,33.029984],[-114.502871,33.011153],[-114.492938,32.971781],[-114.476156,32.975168],[-114.467664,32.966861],[-114.469113,32.952673],[-114.48074,32.937027],[-114.47664,32.923628],[-114.462929,32.907944],[-114.468971,32.845155],[-114.494116,32.823288],[-114.510217,32.816417],[-114.530755,32.793485],[-114.532432,32.776923],[-114.526856,32.757094],[-114.539093,32.756949],[-114.539224,32.749812],[-114.564447,32.749554],[-114.564508,32.742298],[-114.581736,32.742321],[-114.581784,32.734946],[-114.612697,32.734516],[-114.618373,32.728245],[-114.688779,32.737675],[-114.701918,32.745548],[-114.719633,32.718763],[-116.04662,32.623353],[-117.124862,32.534156],[-117.136664,32.618754],[-117.168866,32.671952],[-117.196767,32.688851],[-117.213068,32.687751],[-117.236239,32.671353],[-117.246069,32.669352],[-117.25757,32.72605],[-117.25257,32.752949],[-117.25497,32.786948],[-117.26107,32.803148],[-117.280971,32.822247],[-117.28217,32.839547],[-117.27387,32.851447],[-117.26497,32.848947],[-117.25617,32.859447],[-117.25167,32.874346],[-117.25447,32.900146],[-117.28077,33.012343],[-117.315278,33.093504],[-117.328359,33.121842],[-117.362572,33.168437],[-117.469794,33.296417],[-117.50565,33.334063],[-117.547693,33.365491],[-117.59588,33.386629],[-117.607905,33.406317],[-117.645582,33.440728],[-117.684584,33.461927],[-117.691984,33.456627],[-117.715349,33.460556],[-117.726486,33.483427],[-117.784888,33.541525],[-117.814188,33.552224],[-117.840289,33.573523],[-117.87679,33.592322],[-117.927091,33.605521],[-117.940591,33.620021],[-118.000593,33.654319],[-118.029694,33.676418],[-118.088896,33.729817],[-118.132698,33.753217],[-118.180831,33.763072],[-118.187701,33.749218],[-118.181367,33.717367],[-118.207476,33.716905],[-118.258687,33.703741],[-118.317205,33.712818],[-118.360505,33.736817],[-118.385006,33.741417],[-118.396606,33.735917],[-118.411211,33.741985],[-118.428407,33.774715],[-118.405007,33.800215],[-118.394376,33.804289],[-118.392107,33.840915],[-118.460611,33.969111],[-118.482729,33.995912],[-118.519514,34.027509],[-118.543115,34.038508],[-118.569235,34.04164],[-118.609652,34.036424],[-118.668358,34.038887],[-118.706215,34.029383],[-118.744952,34.032103],[-118.783433,34.021543],[-118.805114,34.001239],[-118.854653,34.034215],[-118.928048,34.045847],[-118.938081,34.043383],[-119.004644,34.066231],[-119.037494,34.083111],[-119.088536,34.09831],[-119.109784,34.094566],[-119.130169,34.100102],[-119.18864,34.139005],[-119.216441,34.146105],[-119.257043,34.213304],[-119.278644,34.266902],[-119.290945,34.274902],[-119.313034,34.275689],[-119.337475,34.290576],[-119.370356,34.319486],[-119.388249,34.317398],[-119.42777,34.353016],[-119.461036,34.374064],[-119.536957,34.395495],[-119.559459,34.413395],[-119.616862,34.420995],[-119.638864,34.415696],[-119.671866,34.416096],[-119.688167,34.412497],[-119.684666,34.408297],[-119.709067,34.395397],[-119.729369,34.395897],[-119.794771,34.417597],[-119.835771,34.415796],[-119.853771,34.407996],[-119.873971,34.408795],[-119.925227,34.433931],[-119.956433,34.435288],[-120.008077,34.460447],[-120.038828,34.463434],[-120.088591,34.460208],[-120.141165,34.473405],[-120.25777,34.467451],[-120.295051,34.470623],[-120.341369,34.458789],[-120.471376,34.447846],[-120.47661,34.475131],[-120.511421,34.522953],[-120.581293,34.556959],[-120.622575,34.554017],[-120.637805,34.56622],[-120.645739,34.581035],[-120.640244,34.604406],[-120.60197,34.692095],[-120.60045,34.70464],[-120.614852,34.730709],[-120.62632,34.738072],[-120.637415,34.755895],[-120.616296,34.816308],[-120.610266,34.85818],[-120.616325,34.866739],[-120.639283,34.880413],[-120.647328,34.901133],[-120.670835,34.904115],[-120.63999,35.002963],[-120.629931,35.061515],[-120.630957,35.101941],[-120.644311,35.139616],[-120.651134,35.147768],[-120.662475,35.153357],[-120.675074,35.153061],[-120.698906,35.171192],[-120.714185,35.175998],[-120.74887,35.177795],[-120.754823,35.174701],[-120.756086,35.160459],[-120.760492,35.15971],[-120.778998,35.168897],[-120.786076,35.177666],[-120.856047,35.206487],[-120.89679,35.247877],[-120.862684,35.346776],[-120.866099,35.393045],[-120.884757,35.430196],[-120.907937,35.449069],[-120.946546,35.446715],[-120.969436,35.460197],[-121.003359,35.46071],[-121.101595,35.548814],[-121.126027,35.593058],[-121.143561,35.606046],[-121.166712,35.635399],[-121.251034,35.656641],[-121.284973,35.674109],[-121.289794,35.689428],[-121.314632,35.71331],[-121.315786,35.75252],[-121.332449,35.783106],[-121.388053,35.823483],[-121.413146,35.855316],[-121.439584,35.86695],[-121.462264,35.885618],[-121.461227,35.896906],[-121.472435,35.91989],[-121.4862,35.970348],[-121.503112,36.000299],[-121.531876,36.014368],[-121.574602,36.025156],[-121.590395,36.050363],[-121.592853,36.065062],[-121.606845,36.072065],[-121.618672,36.087767],[-121.629634,36.114452],[-121.680145,36.165818],[-121.717176,36.195146],[-121.779851,36.227407],[-121.797059,36.234211],[-121.813734,36.234235],[-121.826425,36.24186],[-121.851967,36.277831],[-121.874797,36.289064],[-121.888491,36.30281],[-121.894714,36.317806],[-121.892917,36.340428],[-121.905446,36.358269],[-121.903195,36.393603],[-121.914378,36.404344],[-121.91474,36.42589],[-121.9416,36.485602],[-121.938763,36.506423],[-121.944666,36.521861],[-121.925937,36.525173],[-121.932508,36.559935],[-121.942533,36.566435],[-121.957335,36.564482],[-121.978592,36.580488],[-121.970427,36.582754],[-121.941666,36.618059],[-121.93643,36.636746],[-121.923866,36.634559],[-121.890164,36.609259],[-121.889064,36.601759],[-121.860604,36.611136],[-121.831995,36.644856],[-121.814462,36.682858],[-121.807062,36.714157],[-121.805643,36.750239],[-121.788278,36.803994],[-121.809363,36.848654],[-121.862266,36.931552],[-121.894667,36.961851],[-121.930069,36.97815],[-121.95167,36.97145],[-121.972771,36.954151],[-122.012373,36.96455],[-122.023373,36.96215],[-122.027174,36.95115],[-122.050122,36.948523],[-122.105976,36.955951],[-122.155078,36.98085],[-122.20618,37.013949],[-122.252181,37.059448],[-122.284882,37.101747],[-122.306139,37.116383],[-122.337071,37.117382],[-122.337833,37.135936],[-122.359791,37.155574],[-122.367085,37.172817],[-122.390599,37.182988],[-122.405073,37.195791],[-122.407181,37.219465],[-122.419113,37.24147],[-122.411686,37.265844],[-122.40085,37.359225],[-122.423286,37.392542],[-122.443687,37.435941],[-122.452087,37.48054],[-122.472388,37.50054],[-122.493789,37.492341],[-122.499289,37.495341],[-122.516689,37.52134],[-122.519533,37.537302],[-122.513688,37.552239],[-122.517187,37.590637],[-122.501386,37.599637],[-122.494085,37.644035],[-122.496784,37.686433],[-122.514483,37.780829],[-122.50531,37.788312],[-122.485783,37.790629],[-122.478083,37.810828],[-122.463793,37.804653],[-122.407452,37.811441],[-122.398139,37.80563],[-122.385323,37.790724],[-122.375854,37.734979],[-122.356784,37.729505],[-122.361749,37.71501],[-122.370411,37.717572],[-122.391374,37.708331],[-122.387626,37.67906],[-122.374291,37.662206],[-122.3756,37.652389],[-122.387381,37.648462],[-122.386072,37.637662],[-122.35531,37.615736],[-122.358583,37.611155],[-122.373309,37.613773],[-122.378545,37.605592],[-122.360219,37.592501],[-122.317676,37.590865],[-122.305895,37.575484],[-122.262698,37.572866],[-122.214264,37.538505],[-122.196593,37.537196],[-122.194957,37.522469],[-122.168449,37.504143],[-122.155686,37.501198],[-122.140142,37.507907],[-122.127706,37.500053],[-122.111344,37.50758],[-122.111998,37.528851],[-122.147014,37.588411],[-122.145378,37.600846],[-122.152905,37.640771],[-122.163049,37.667933],[-122.246826,37.72193],[-122.257953,37.739601],[-122.257134,37.745001],[-122.242638,37.753744],[-122.253753,37.761218],[-122.293996,37.770416],[-122.330963,37.786035],[-122.33555,37.799538],[-122.333711,37.809797],[-122.323567,37.823214],[-122.303931,37.830087],[-122.301313,37.847758],[-122.310477,37.873938],[-122.309986,37.892755],[-122.32373,37.905845],[-122.33453,37.908791],[-122.35711,37.908791],[-122.367582,37.903882],[-122.385908,37.908136],[-122.39049,37.922535],[-122.413725,37.937262],[-122.430087,37.963115],[-122.415361,37.963115],[-122.399832,37.956009],[-122.367582,37.978168],[-122.361905,37.989991],[-122.367909,38.01253],[-122.340093,38.003694],[-122.321112,38.012857],[-122.300823,38.010893],[-122.283478,38.022674],[-122.262861,38.0446],[-122.273006,38.07438],[-122.314567,38.115287],[-122.366273,38.141467],[-122.39638,38.149976],[-122.403514,38.150624],[-122.409798,38.136231],[-122.439577,38.116923],[-122.454958,38.118887],[-122.489974,38.112014],[-122.483757,38.071762],[-122.499465,38.032165],[-122.497828,38.019402],[-122.481466,38.007621],[-122.462812,38.003367],[-122.452995,37.996167],[-122.448413,37.984713],[-122.456595,37.978823],[-122.471975,37.981768],[-122.488665,37.966714],[-122.487684,37.948716],[-122.479175,37.941516],[-122.48572,37.937589],[-122.499465,37.939225],[-122.503064,37.928753],[-122.478193,37.918608],[-122.471975,37.910427],[-122.472303,37.902573],[-122.458558,37.894064],[-122.448413,37.89341],[-122.438268,37.880974],[-122.45005,37.871157],[-122.462158,37.868866],[-122.480811,37.873448],[-122.479151,37.825428],[-122.505383,37.822128],[-122.548986,37.836227],[-122.561487,37.851827],[-122.584289,37.859227],[-122.60129,37.875126],[-122.656519,37.904519],[-122.682171,37.90645],[-122.70264,37.89382],[-122.727297,37.904626],[-122.736898,37.925825],[-122.766138,37.938004],[-122.783244,37.951334],[-122.797405,37.976657],[-122.821383,37.996735],[-122.856573,38.016717],[-122.882114,38.025273],[-122.939711,38.031908],[-122.956811,38.02872],[-122.981776,38.009119],[-122.97439,37.992429],[-123.024066,37.994878],[-123.011533,38.003438],[-122.99242,38.041758],[-122.960889,38.112962],[-122.949074,38.15406],[-122.953629,38.17567],[-122.965408,38.187113],[-122.968112,38.202428],[-122.993959,38.237602],[-122.968569,38.242879],[-122.967203,38.250691],[-122.977082,38.267902],[-122.986319,38.273164],[-123.002911,38.295708],[-123.024333,38.310573],[-123.038742,38.313576],[-123.051061,38.310693],[-123.053504,38.299385],[-123.063671,38.302178],[-123.074684,38.322574],[-123.068437,38.33521],[-123.068265,38.359865],[-123.128825,38.450418],[-123.202277,38.494314],[-123.249797,38.511045],[-123.287156,38.540223],[-123.331899,38.565542],[-123.343338,38.590008],[-123.371876,38.607235],[-123.398166,38.647044],[-123.441774,38.699744],[-123.461291,38.717001],[-123.514784,38.741966],[-123.541837,38.776764],[-123.579856,38.802835],[-123.58638,38.802857],[-123.605317,38.822765],[-123.647387,38.845472],[-123.659846,38.872529],[-123.71054,38.91323],[-123.725367,38.917438],[-123.726315,38.936367],[-123.738886,38.95412],[-123.729053,38.956667],[-123.711149,38.977316],[-123.6969,39.004401],[-123.690095,39.031157],[-123.693969,39.057363],[-123.713392,39.108422],[-123.721505,39.125327],[-123.737913,39.143442],[-123.742221,39.164885],[-123.765891,39.193657],[-123.774998,39.212083],[-123.777368,39.237214],[-123.787893,39.264327],[-123.803848,39.278771],[-123.803081,39.291747],[-123.811387,39.312825],[-123.808772,39.324368],[-123.822085,39.343857],[-123.826306,39.36871],[-123.81469,39.446538],[-123.766475,39.552803],[-123.787417,39.604552],[-123.782322,39.621486],[-123.792659,39.684122],[-123.808208,39.710715],[-123.829545,39.723071],[-123.838089,39.752409],[-123.839797,39.795637],[-123.851714,39.832041],[-123.907664,39.863028],[-123.930047,39.909697],[-123.954952,39.922373],[-123.980031,39.962458],[-124.035904,40.013319],[-124.056408,40.024305],[-124.068908,40.021307],[-124.079983,40.029773],[-124.080709,40.06611],[-124.110549,40.103765],[-124.187874,40.130542],[-124.214895,40.160902],[-124.296497,40.208816],[-124.320912,40.226617],[-124.327691,40.23737],[-124.34307,40.243979],[-124.363414,40.260974],[-124.363634,40.276212],[-124.347853,40.314634],[-124.362796,40.350046],[-124.365357,40.374855],[-124.373599,40.392923],[-124.391496,40.407047],[-124.409591,40.438076],[-124.38494,40.48982],[-124.383224,40.499852],[-124.387023,40.504954],[-124.382816,40.519],[-124.329404,40.61643],[-124.158322,40.876069],[-124.137066,40.925732],[-124.118147,40.989263],[-124.112165,41.028173],[-124.125448,41.048504],[-124.138217,41.054342],[-124.153622,41.05355],[-124.154513,41.087159],[-124.160556,41.099011],[-124.159065,41.121957],[-124.165414,41.129822],[-124.158539,41.143021],[-124.149674,41.140845],[-124.1438,41.144686],[-124.106986,41.229678],[-124.072294,41.374844],[-124.063076,41.439579],[-124.066057,41.470258],[-124.081427,41.511228],[-124.081987,41.547761],[-124.092404,41.553615],[-124.101123,41.569192],[-124.097385,41.585251],[-124.100961,41.602499],[-124.114413,41.616768],[-124.120225,41.640354],[-124.135552,41.657307],[-124.147412,41.717955],[-124.164716,41.740126],[-124.17739,41.745756],[-124.194953,41.736778],[-124.23972,41.7708],[-124.248704,41.771459],[-124.255994,41.783014],[-124.245027,41.7923],[-124.230678,41.818681],[-124.208439,41.888192],[-124.203402,41.940964],[-124.204948,41.983441],[-124.211605,41.99846],[-123.656998,41.995137],[-123.624554,41.999837],[-123.347562,41.999108],[-123.145959,42.009247],[-123.045254,42.003049],[-122.893961,42.002605],[-122.289533,42.007764]]]]},\"properties\":{\"name\":\"California\",\"nation\":\"USA \"}}]}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The Navajo (N) aquifer is the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Availability of water is an important issue in the Black Mesa area because of continued water requirements for industrial and municipal use by a growing population and because of its arid climate. Precipitation in the area typically ranges from less than 6 to more than 16 inches per year depending on location.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from November 2016 to December 2018. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater and surface-water chemistry.
In calendar year 2017, total groundwater withdrawals were 3,710 acre-feet (acre-ft), industrial withdrawals were 1,110 acre-ft, and municipal withdrawals were 2,600 acre-ft. In calendar year 2018, total groundwater withdrawals were 3,670 acre-ft, industrial withdrawals were 1,170 acre-ft, and municipal withdrawals were 2,500 acre-ft. Total withdrawals during 2017 and 2018 were about 49 percent less than total withdrawals in 2005 because of Peabody Western Coal Company’s discontinued use of water to transport coal in a coal slurry pipeline.
From the prestress period (prior to 1965) to 2018, measured water levels available for comparison in wells completed in the unconfined areas of the N aquifer within the Black Mesa area declined in 8 of 14 wells, the changes ranged from +12.1 feet to −39.4 feet, and the median change was -0.6 feet. Water levels also declined in 15 of 18 wells measured in the confined area of the aquifer. The median change for the confined area of the aquifer was −40.2 feet (ft), with changes ranging from +14.2 ft to −189.0 ft. From the prestress period to 2018, the median water-level change for all 32 wells in both the confined and unconfined areas was −9.4 ft.
Spring flow was measured at four springs in 2017 and 2018. Flow fluctuated during the period of record for Burro Spring and Pasture Canyon Spring, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Unnamed Spring near Dennehotso. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Pasture Canyon Spring has fluctuated for the period of record.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2018), Dinnebito Wash near Sand Springs 09401110 (1993 to 2018), Polacca Wash near Second Mesa 09400568 (1994 to 2018), and Pasture Canyon Springs 09401265 (2004 to 2018). Median winter flows (November through February) of each water year were used as an index of the amount of groundwater discharge at the above-named sites. For the period of record, the median winter flows have generally remained constant at Dinnebito Wash and Polacca Wash, whereas a decreasing trend was indicated at Moenkopi Wash and Pasture Canyon Springs.
In 2017 and 2018, water samples collected from two wells, four springs, and three streams in the Black Mesa area were analyzed for selected chemical constituents. The results from wells and springs were compared with previous analyses from the same wells and springs. At the Peabody 2 well, a significant (p<0.05) decreasing trend in dissolved solids over time was found, while concentrations of dissolved solids have not varied significantly (p>0.05) at the Kykotsmovi PM2 well. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 30 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Burro Spring and Unnamed Spring near Dennehotso have varied for the period of record, but there is no statistical trend in the data. Baseflow water chemistry samples were collected from Moenkopi, Dinnebito, and Polacca washes in 2017. Samples from all three washes had total-dissolved solids concentrations higher than is typically found in the N aquifer water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211124","collaboration":"Prepared in cooperation with the Navajo Nation and Peabody Western Coal Company","usgsCitation":"Mason, J.P., 2021, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2016–2018: U.S. Geological Survey Open-File Report 2021–1124, 50 p., https://doi.org/10.3133/ofr20211124.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-110021","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":384543,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181193","text":"Open-File Report 2018-1193","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data, Black Mesa Area, Northeastern Arizona—2015–2016"},{"id":384455,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1124/ofr20211124.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384454,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1124/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.76391601562499,\n 35.32633026307483\n ],\n [\n -109.171142578125,\n 35.32633026307483\n ],\n [\n -109.171142578125,\n 36.99377838872517\n ],\n [\n -111.76391601562499,\n 36.99377838872517\n ],\n [\n -111.76391601562499,\n 35.32633026307483\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Volcanic plumes are common and far-reaching manifestations of volcanic activity during and between eruptions. Observations of the rate of emission and composition of volcanic plumes are essential to recognize and, in some cases, predict the state of volcanic activity. Measurements of the size and location of the plumes are important to assess the impact of the emission from sporadic or localized events to persistent or widespread processes of climatic and environmental importance. These observations provide information on volatile budgets on Earth, chemical evolution of magmas, and atmospheric circulation and dynamics. Space-based observations during the last decades have given us a global view of Earth's volcanic emission, particularly of sulfur dioxide (SO2). Although none of the satellite missions were intended to be used for measurement of volcanic gas emission, specially adapted algorithms have produced time-averaged global emission budgets. These have confirmed that tropospheric plumes, produced from persistent degassing of weak sources, dominate the total emission of volcanic SO2. Although space-based observations have provided this global insight into some aspects of Earth's volcanism, it still has important limitations. The magnitude and short-term variability of lower-atmosphere emissions, historically less accessible from space, remain largely uncertain. Operational monitoring of volcanic plumes, at scales relevant for adequate surveillance, has been facilitated through the use of ground-based scanning differential optical absorption spectrometer (ScanDOAS) instruments since the beginning of this century, largely due to the coordinated effort of the Network for Observation of Volcanic and Atmospheric Change (NOVAC). In this study, we present a compilation of results of homogenized post-analysis of measurements of SO2 flux and plume parameters obtained during the period March 2005 to January 2017 of 32 volcanoes in NOVAC. This inventory opens a window into the short-term emission patterns of a diverse set of volcanoes in terms of magma composition, geographical location, magnitude of emission, and style of eruptive activity. We find that passive volcanic degassing is by no means a stationary process in time and that large sub-daily variability is observed in the flux of volcanic gases, which has implications for emission budgets produced using short-term, sporadic observations. The use of a standard evaluation method allows for intercomparison between different volcanoes and between ground- and space-based measurements of the same volcanoes. The emission of several weakly degassing volcanoes, undetected by satellites, is presented for the first time. We also compare our results with those reported in the literature, providing ranges of variability in emission not accessible in the past. The open-access data repository introduced in this article will enable further exploitation of this unique dataset, with a focus on volcanological research, risk assessment, satellite-sensor validation, and improved quantification of the prevalent tropospheric component of global volcanic emission.
","language":"English","publisher":"Copernicus","doi":"10.5194/essd-13-1167-2021","usgsCitation":"Arellano, S., Galle, B., Apaza, F., Avard, G., Barrington, C., Bobrowski, N., Bucarey, C., Burbano, V., Burton, M., Chacon, Z., Chigna, G., Clarito, C.J., Conde, V., Costa, F., de Moor, M., Delgado-Granados, H., Di Muro, A., Fernandez, D., Garzon, G., Gunawan, H., Haerani, N., Hansteen, T., Hidalgo, S., Inguaggiato, S., Johansson, M., Kern, C., Kihlman, M., Kowalski, P., Masias, P., Montalvo, F., Moller, J., Platt, U., Rivera, C., Saballos, A., Salerno, G., Taisne, B., Vasconez, F., Velazquez, G., Vita, F., and Yalire, M.M., 2021, Synoptic analysis of a decade of daily measurements of SO2 emission in the troposphere from volcanoes of the global ground-based Network for Observation of Volcanic and Atmospheric Change: Earth System Science Data, v. 13, p. 1167-1188, https://doi.org/10.5194/essd-13-1167-2021.","productDescription":"22 p.","startPage":"1167","endPage":"1188","ipdsId":"IP-114711","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":452986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-13-1167-2021","text":"Publisher Index Page"},{"id":384573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2021-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Arellano, Santiago","contributorId":205719,"corporation":false,"usgs":false,"family":"Arellano","given":"Santiago","affiliations":[{"id":37153,"text":"Department of Earth and Space Sciences – Chalmers University of Technology, Göteborg, Sweden","active":true,"usgs":false}],"preferred":false,"id":812710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Galle, Bo","contributorId":255645,"corporation":false,"usgs":false,"family":"Galle","given":"Bo","email":"","affiliations":[{"id":51629,"text":"Chalmers University, Sweden","active":true,"usgs":false}],"preferred":false,"id":812711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Apaza, Fredy","contributorId":190927,"corporation":false,"usgs":false,"family":"Apaza","given":"Fredy","email":"","affiliations":[],"preferred":false,"id":812712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Avard, Geoffroy","contributorId":173679,"corporation":false,"usgs":false,"family":"Avard","given":"Geoffroy","email":"","affiliations":[{"id":27271,"text":"Observatorio Volcanológico y Sismológico de Costa Rica, Universidad Nacional, Heredia, Costa Rica","active":true,"usgs":false}],"preferred":false,"id":812713,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barrington, Charlotte","contributorId":205722,"corporation":false,"usgs":false,"family":"Barrington","given":"Charlotte","email":"","affiliations":[{"id":37155,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore 639798","active":true,"usgs":false}],"preferred":false,"id":812714,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bobrowski, Nicole","contributorId":255646,"corporation":false,"usgs":false,"family":"Bobrowski","given":"Nicole","affiliations":[{"id":51631,"text":"University of Heidelberg, Germany","active":true,"usgs":false}],"preferred":false,"id":812715,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bucarey, Claudia","contributorId":255648,"corporation":false,"usgs":false,"family":"Bucarey","given":"Claudia","email":"","affiliations":[{"id":27236,"text":"SERNAGEOMIN","active":true,"usgs":false}],"preferred":false,"id":812716,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burbano, Viviana","contributorId":255649,"corporation":false,"usgs":false,"family":"Burbano","given":"Viviana","email":"","affiliations":[{"id":51633,"text":"SGC","active":true,"usgs":false}],"preferred":false,"id":812717,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burton, Mike","contributorId":255650,"corporation":false,"usgs":false,"family":"Burton","given":"Mike","email":"","affiliations":[{"id":37573,"text":"University of Manchester, UK","active":true,"usgs":false}],"preferred":false,"id":812718,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chacon, Zoraida","contributorId":255651,"corporation":false,"usgs":false,"family":"Chacon","given":"Zoraida","email":"","affiliations":[{"id":51633,"text":"SGC","active":true,"usgs":false}],"preferred":false,"id":812719,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Chigna, Gustavo","contributorId":255652,"corporation":false,"usgs":false,"family":"Chigna","given":"Gustavo","email":"","affiliations":[{"id":51634,"text":"INSIVUMEH","active":true,"usgs":false}],"preferred":false,"id":812720,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Clarito, Christian Joseph","contributorId":255667,"corporation":false,"usgs":false,"family":"Clarito","given":"Christian","email":"","middleInitial":"Joseph","affiliations":[],"preferred":false,"id":812751,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Conde, Vladimir","contributorId":255653,"corporation":false,"usgs":false,"family":"Conde","given":"Vladimir","email":"","affiliations":[{"id":51629,"text":"Chalmers University, Sweden","active":true,"usgs":false}],"preferred":false,"id":812721,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":812722,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"de Moor, Maarten","contributorId":173676,"corporation":false,"usgs":false,"family":"de Moor","given":"Maarten","email":"","affiliations":[{"id":27271,"text":"Observatorio Volcanológico y Sismológico de Costa Rica, Universidad Nacional, Heredia, Costa Rica","active":true,"usgs":false}],"preferred":false,"id":812723,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Delgado-Granados, Hugo","contributorId":255654,"corporation":false,"usgs":false,"family":"Delgado-Granados","given":"Hugo","email":"","affiliations":[{"id":16152,"text":"UNAM","active":true,"usgs":false}],"preferred":false,"id":812724,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Di Muro, Andrea","contributorId":255655,"corporation":false,"usgs":false,"family":"Di Muro","given":"Andrea","email":"","affiliations":[{"id":47633,"text":"IPGP","active":true,"usgs":false}],"preferred":false,"id":812725,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Fernandez, D.","contributorId":68094,"corporation":false,"usgs":true,"family":"Fernandez","given":"D.","affiliations":[],"preferred":false,"id":812726,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Garzon, Gustavo","contributorId":255668,"corporation":false,"usgs":false,"family":"Garzon","given":"Gustavo","email":"","affiliations":[],"preferred":false,"id":812752,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Gunawan, Hendra","contributorId":194977,"corporation":false,"usgs":false,"family":"Gunawan","given":"Hendra","email":"","affiliations":[],"preferred":false,"id":812727,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Haerani, Nia","contributorId":255669,"corporation":false,"usgs":false,"family":"Haerani","given":"Nia","email":"","affiliations":[],"preferred":false,"id":812753,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Hansteen, Thor","contributorId":255656,"corporation":false,"usgs":false,"family":"Hansteen","given":"Thor","email":"","affiliations":[{"id":48696,"text":"GEOMAR","active":true,"usgs":false}],"preferred":false,"id":812754,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Hidalgo, Silvana","contributorId":178530,"corporation":false,"usgs":false,"family":"Hidalgo","given":"Silvana","affiliations":[],"preferred":false,"id":812755,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Inguaggiato, Salvatore","contributorId":138807,"corporation":false,"usgs":false,"family":"Inguaggiato","given":"Salvatore","email":"","affiliations":[{"id":12533,"text":"Istituto Nazionale di Geofisica e Vulcanologia – Sezione di Palermo- Via Ugo La Malfa, 153, 90146 Palermo, Italy","active":true,"usgs":false}],"preferred":false,"id":812756,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Johansson, Mattias","contributorId":255657,"corporation":false,"usgs":false,"family":"Johansson","given":"Mattias","email":"","affiliations":[{"id":51629,"text":"Chalmers University, Sweden","active":true,"usgs":false}],"preferred":false,"id":812757,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812758,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Kihlman, Manne","contributorId":255658,"corporation":false,"usgs":false,"family":"Kihlman","given":"Manne","email":"","affiliations":[{"id":51629,"text":"Chalmers University, Sweden","active":true,"usgs":false}],"preferred":false,"id":812759,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Kowalski, Philippe","contributorId":255659,"corporation":false,"usgs":false,"family":"Kowalski","given":"Philippe","email":"","affiliations":[{"id":47633,"text":"IPGP","active":true,"usgs":false}],"preferred":false,"id":812760,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Masias, Pablo","contributorId":190934,"corporation":false,"usgs":false,"family":"Masias","given":"Pablo","email":"","affiliations":[],"preferred":false,"id":812761,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Montalvo, Francisco","contributorId":255660,"corporation":false,"usgs":false,"family":"Montalvo","given":"Francisco","email":"","affiliations":[{"id":51636,"text":"SNET","active":true,"usgs":false}],"preferred":false,"id":812762,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Moller, Joakim","contributorId":255670,"corporation":false,"usgs":false,"family":"Moller","given":"Joakim","email":"","affiliations":[],"preferred":false,"id":812763,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Platt, Ulrich","contributorId":26609,"corporation":false,"usgs":true,"family":"Platt","given":"Ulrich","affiliations":[],"preferred":false,"id":812764,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Rivera, Claudia","contributorId":255662,"corporation":false,"usgs":false,"family":"Rivera","given":"Claudia","email":"","affiliations":[{"id":16152,"text":"UNAM","active":true,"usgs":false}],"preferred":false,"id":812765,"contributorType":{"id":1,"text":"Authors"},"rank":33},{"text":"Saballos, Armando","contributorId":205257,"corporation":false,"usgs":false,"family":"Saballos","given":"Armando","email":"","affiliations":[{"id":37067,"text":"INETER","active":true,"usgs":false}],"preferred":false,"id":812766,"contributorType":{"id":1,"text":"Authors"},"rank":34},{"text":"Salerno, Giuseppe","contributorId":255663,"corporation":false,"usgs":false,"family":"Salerno","given":"Giuseppe","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":812767,"contributorType":{"id":1,"text":"Authors"},"rank":35},{"text":"Taisne, Benoit","contributorId":255664,"corporation":false,"usgs":false,"family":"Taisne","given":"Benoit","email":"","affiliations":[{"id":51637,"text":"EOS","active":true,"usgs":false}],"preferred":false,"id":812768,"contributorType":{"id":1,"text":"Authors"},"rank":36},{"text":"Vasconez, Freddy","contributorId":255665,"corporation":false,"usgs":false,"family":"Vasconez","given":"Freddy","email":"","affiliations":[{"id":51638,"text":"IGEPN","active":true,"usgs":false}],"preferred":false,"id":812769,"contributorType":{"id":1,"text":"Authors"},"rank":37},{"text":"Velazquez, Gabriela","contributorId":255666,"corporation":false,"usgs":false,"family":"Velazquez","given":"Gabriela","email":"","affiliations":[{"id":27236,"text":"SERNAGEOMIN","active":true,"usgs":false}],"preferred":false,"id":812770,"contributorType":{"id":1,"text":"Authors"},"rank":38},{"text":"Vita, Fabio","contributorId":138806,"corporation":false,"usgs":false,"family":"Vita","given":"Fabio","email":"","affiliations":[{"id":12533,"text":"Istituto Nazionale di Geofisica e Vulcanologia – Sezione di Palermo- Via Ugo La Malfa, 153, 90146 Palermo, Italy","active":true,"usgs":false}],"preferred":false,"id":812771,"contributorType":{"id":1,"text":"Authors"},"rank":39},{"text":"Yalire, Mathieu M.","contributorId":204385,"corporation":false,"usgs":false,"family":"Yalire","given":"Mathieu","email":"","middleInitial":"M.","affiliations":[{"id":36925,"text":"Goma Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":812772,"contributorType":{"id":1,"text":"Authors"},"rank":40}]}} ]}