Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product.
Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing.
The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics.
The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing.
Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both.
More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland.
China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent.
This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues.
The GCEP30 product, which can be browsed at full resolution at www.croplands.org, has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1868","usgsCitation":"Thenkabail, P.S., Teluguntla, P.G., Xiong, J., Oliphant, A., Congalton, R.G., Ozdogan, M., Gumma, M.K., Tilton, J.C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., and Foley, D., 2021, Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud: U.S. Geological Survey Professional Paper 1868, 63 p., https://doi.org/10.3133/pp1868.","productDescription":"Report: ix, 63 p.; Dataset","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119164","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":391888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1868/pp1868.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1868/covrthb.jpg"},{"id":391890,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/","text":"Associated data","linkHelpText":"- Release of GFSAD 30 meter Cropland Extent Products"}],"contact":"Director,
Western Geographic Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Identifying which environmental and genetic factors affect growth pattern phenotypes can help biologists predict how organisms distribute finite energy resources in response to varying environmental conditions and physiological states. This information may be useful for monitoring and managing populations of cryptic, endangered, and invasive species. Consequently, we assessed the effects of food availability, clutch, and sex on the growth of invasive Burmese pythons (Python bivittatus Kuhl) from the Greater Everglades Ecosystem in Florida, USA. Though little is known from the wild, Burmese pythons have been physiological model organisms for decades, with most experimental research sourcing individuals from the pet trade. Here, we used 60 hatchlings collected as eggs from the nests of two wild pythons, assigned them to High or Low feeding treatments, and monitored growth and meal consumption for 12 weeks, a period when pythons are thought to grow very rapidly. None of the 30 hatchlings that were offered food prior to their fourth week post-hatching consumed it, presumably because they were relying on internal yolk stores. Although only two clutches were used in the experiment, we found that nearly all phenotypic variation was explained by clutch rather than feeding treatment or sex. Hatchlings from clutch 1 (C1) grew faster and were longer, heavier, in better body condition, ate more frequently, and were bolder than hatchlings from clutch 2 (C2), regardless of food availability. On average, C1 and C2 hatchling snout-vent length (SVL) and weight grew 0.15 cm d−1 and 0.10 cm d−1, and 0.20 g d−1 and 0.03 g d−1, respectively. Additional research may be warranted to determine whether these effects remain with larger clutch sample sizes and to identify the underlying mechanisms and fitness implications of this variation to help inform risk assessments and management.
","language":"English","publisher":"The Company of Biologists","doi":"10.1242/bio.058739","usgsCitation":"Josimovich, J.M., Falk, B., Grajal-Puche, A., Hanslowe, E.B., Bartoszek, I., Reed, R., and Currylow, A.F., 2021, Clutch may predict growth of hatchling Burmese pythons better than food availability or sex: Biology Open, v. 10, no. 11, bio058739, 10 p., https://doi.org/10.1242/bio.058739.","productDescription":"bio058739, 10 p.","ipdsId":"IP-121446","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450169,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1242/bio.058739","text":"Publisher Index Page"},{"id":436113,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WHSSJ6","text":"USGS data release","linkHelpText":"Hatchling Growth Experiment Dataset from Invasive Burmese Pythons Captured in 2015 in Southern Florida"},{"id":391972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Josimovich, Jillian Maureen 0000-0002-7523-3496 jjosimovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7523-3496","contributorId":257058,"corporation":false,"usgs":true,"family":"Josimovich","given":"Jillian","email":"jjosimovich@usgs.gov","middleInitial":"Maureen","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falk, Bryan G. 0000-0002-9690-5626","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":265395,"corporation":false,"usgs":false,"family":"Falk","given":"Bryan G.","affiliations":[{"id":54672,"text":"National Park Service, Everglades National Park, 40001 SR 9336, Homestead, Florida 33034, USA","active":true,"usgs":false}],"preferred":false,"id":827123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grajal-Puche, Alejandro 0000-0003-1807-4799","orcid":"https://orcid.org/0000-0003-1807-4799","contributorId":265397,"corporation":false,"usgs":false,"family":"Grajal-Puche","given":"Alejandro","affiliations":[{"id":54677,"text":"Department of Biological Sciences, P.O. Box 5640, Northern Arizona University, Flagstaff, Arizona 86011, USA","active":true,"usgs":false}],"preferred":false,"id":827124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanslowe, Emma B. 0000-0003-4331-6729","orcid":"https://orcid.org/0000-0003-4331-6729","contributorId":265394,"corporation":false,"usgs":false,"family":"Hanslowe","given":"Emma","email":"","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":827125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartoszek, Ian A.","contributorId":269426,"corporation":false,"usgs":false,"family":"Bartoszek","given":"Ian A.","affiliations":[{"id":55974,"text":"Conservancy of Southwest Florida, Naples, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":827126,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Currylow, Andrea Faye 0000-0003-1631-8964","orcid":"https://orcid.org/0000-0003-1631-8964","contributorId":257055,"corporation":false,"usgs":true,"family":"Currylow","given":"Andrea","email":"","middleInitial":"Faye","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":827128,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227182,"text":"70227182 - 2021 - The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities","interactions":[],"lastModifiedDate":"2022-01-04T16:16:06.757333","indexId":"70227182","displayToPublicDate":"2021-11-18T10:00:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities","docAbstract":"Inland recreational fishing, defined as primarily leisure-driven fishing in freshwaters, is a popular pastime in the USA. State natural resource agencies endeavor to provide high-quality and sustainable fishing opportunities for anglers. Managers often use creel and other angler survey data to inform state- and waterbody-level management efforts. Despite the broad implementation of angler surveys and their importance to fisheries management at state scales, regional and national coordination among these activities is minimal, limiting data applicability for larger-scale management practices and research. Here, we introduce the U.S. Inland Creel and Angler Survey Catalog (CreelCat), a first-of-its-kind, publicly available national database of angler survey data that establishes a baseline of national inland recreational fishing metrics. We highlight research and management applications to help support sustainable inland recreational fishing practices, consider cautions, and make recommendations for implementation.
","language":"English","publisher":"Wiley","doi":"10.1002/fsh.10671","usgsCitation":"Lynch, A.J., Sievert, N., Embke, H.S., Robertson, A., Myers, B.J., Allen, M.S., Feiner, Z., Hoogakker, F., Knoche, S., Krogman, R., Midway, S.R., Nieman, C.L., Paukert, C., Pope, K.L., Rogers, M.W., Wszola, L.S., and Beard, 2021, The U.S. Inland Creel and Angler Survey Catalog (CreelCat): Development, applications, and opportunities: Fisheries Magazine, v. 46, no. 11, p. 574-583, https://doi.org/10.1002/fsh.10671.","productDescription":"10 p.","startPage":"574","endPage":"583","ipdsId":"IP-122018","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":500804,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.lsu.edu/oceanography_coastal_pubs/1070","text":"External Repository"},{"id":436114,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E45TNJ","text":"USGS data release","linkHelpText":"The US Inland Creel and Angler Survey Catalog (CreelCat) Application and CreelCatch R Package"},{"id":393862,"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 \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n 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In areas where it has been introduced by humans, American beaver (Castor canadensis) is an iconic example of such species due to its capacity to alter trophic dynamics of entire ecosystems and create new invasional pathways for other non-native species. The species is problematic in several watersheds within the Southern California-Northern Baja California Coast Ecoregion, a recognized hotspot of biodiversity, due to its ability to modify habitat in ways that favor invasive predators and competitors over the region's native species and habitat. Beaver was deliberately introduced across California in the mid-1900s and generally accepted as non-native to the region up to the early 2000s; however, articles promoting the idea that beaver may be a natural resident have gained traction in recent years, due in large part to the species' charismatic nature rather than by presentation of sound evidence. Here, we discuss the problems associated with beaver disturbance and its effects on conserving the region's native fauna and flora. We refute arguments underlying the claim that beaver is native to the region, and review paleontological, zooarchaeological, and historical survey data from renowned field biologists and naturalists over the past ~160 years to show that no evidence exists that beaver arrived by any means other than deliberate human introduction. Managing this ecosystem engineer has potential to reduce the richness and abundance of other non-native species because the novel, engineered habitat now supporting these species would diminish in beaver-occupied watersheds. At the same time, hydrologic functionality would shift toward more natural, ephemeral conditions that favor the regions' native species while suppressing the dominance of the most insidious invaders.
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California","active":true,"usgs":false}],"preferred":false,"id":833408,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matsuda, Tritia 0000-0001-9271-7671","orcid":"https://orcid.org/0000-0001-9271-7671","contributorId":213956,"corporation":false,"usgs":true,"family":"Matsuda","given":"Tritia","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833409,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833410,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227105,"text":"70227105 - 2021 - Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts","interactions":[],"lastModifiedDate":"2021-12-29T13:56:35.368279","indexId":"70227105","displayToPublicDate":"2021-11-18T07:55:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts","docAbstract":"• There are three general stages of a well's life on US public land: 1) the permitting process to drill, 2) active extraction of fossil fuel resource, and 3) plugging and abandonment of well.
• There is no national standard for oil and gas reclamation in the United States similar to mining and therefore current reclamation practices and standards fail to achieve long-term effectiveness across the western United States.
• A reclaimed well pad's land potential is determined by 3 properties: static (e.g., climate), dynamic (e.g., soil stability), and process (e.g., water retention).
• Understanding a reclaimed well pad's land potential enables federal land agencies to outline surface reclamation goals and requirements consistently and clearly.
• Monitoring for land potential increases the capacity of the private industry to practice adaptive management by enabling companies to respond to plant community changes while maintaining long-term progress toward recovery.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.10.004","usgsCitation":"Di Stefano, S., Karl, J.W., Duniway, M.C., Heinse, R., Hulet, A., and Wulfhorst, J., 2021, Oil and gas reclamation on US public lands: How it works and improving the process with land potential concepts: Rangelands, v. 43, no. 6, p. 211-221, https://doi.org/10.1016/j.rala.2021.10.004.","productDescription":"11 p.","startPage":"211","endPage":"221","ipdsId":"IP-126162","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":498665,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/671287","text":"External Repository"},{"id":393569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Di Stefano, Sean","contributorId":270642,"corporation":false,"usgs":false,"family":"Di Stefano","given":"Sean","email":"","affiliations":[{"id":56192,"text":"Department of Forest, Rangeland, and Fire Sciences, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":829645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heinse, Robert","contributorId":270646,"corporation":false,"usgs":false,"family":"Heinse","given":"Robert","email":"","affiliations":[{"id":56192,"text":"Department of Forest, Rangeland, and Fire Sciences, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829647,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hulet, April","contributorId":270647,"corporation":false,"usgs":false,"family":"Hulet","given":"April","affiliations":[{"id":56192,"text":"Department of Forest, Rangeland, and Fire Sciences, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829648,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wulfhorst, J.D.","contributorId":270648,"corporation":false,"usgs":false,"family":"Wulfhorst","given":"J.D.","email":"","affiliations":[{"id":56193,"text":"Department of Natural Resources and Society, College of Natural Resources, University of Idaho, ID USA, 83844","active":true,"usgs":false}],"preferred":false,"id":829649,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226776,"text":"70226776 - 2021 - Multi-model comparison of computed debris flow runout for the 9 January 2018 Montecito, California post-wildfire event","interactions":[],"lastModifiedDate":"2021-12-13T13:10:17.544528","indexId":"70226776","displayToPublicDate":"2021-11-18T07:00:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Multi-model comparison of computed debris flow runout for the 9 January 2018 Montecito, California post-wildfire event","docAbstract":"Hazard assessment for post-wildfire debris flows, which are common in the steep terrain of the western United States, has focused on the susceptibility of upstream basins to generate debris flows. However, reducing public exposure to this hazard also requires an assessment of hazards in downstream areas that might be inundated during debris flow runout. Debris flow runout models are widely available, but their application to hazard assessment for post-wildfire debris flows has not been extensively tested. Necessary inputs to these models include the total volume of the mobilized flow, flow properties (either inherent material properties or calibration coefficients), and site topography. Estimates of volume are possible in post-event (“back calculation”) studies, yet before an event, volume is an uncertain quantity. We simulated debris flow runout for the well-constrained 9 January 2018 Montecito event using three models (RAMMS, FLO2D, and D-Claw) to determine the relative importance of volume and flow properties. We broke the impacted area into three domains, and for each model-domain combination, we performed a numerical sampling study in which volume and flow properties varied within a wide, but plausible range. We assessed model performance based on inundation patterns and peak flow depths. We found all models could simulate the event with comparable results. Simulation performance was most sensitive to flow volume and less sensitive to flow properties. Our results emphasize the importance of reducing uncertainty in pre-event estimates of flow volume for hazard assessment.
Groundwater supplies 35 percent of drinking water in the United States. Mapping the quantity and quality of groundwater at the depths used for potable supplies requires an understanding of locational variation in the characteristics of drinking-water wells (depth and open interval). Typical depths of domestic- and public-drinking-water supply wells vary by and within aquifer across the United States. The depths to the top and bottom of the zones from which drinking water is withdrawn are important predictor variables in regional- and national-scale statistical water models, but spatially extensive maps of the depths to drinking-water-supply sources are not consistently available in modeled regions. Therefore, it was necessary to generate a set of grids representing surfaces of the approximate common depth and length of open intervals in the wells from which water is withdrawn for domestic- and public-drinking-water supply (withdrawal zones) within the conterminous United States.
Well data (about 7.6 million records) were compiled from several sources, including the U.S. Geological Survey’s National Water Information System (600,922 records), the U.S. Environmental Protection Agency’s Safe Drinking Water Information System dataset (66,540 records, primarily public-supply wells), a groundwater ambient monitoring dataset (31,448 records, primarily domestic-supply wells), individual State data (6,096,503 records), a national brackish aquifer study (96,885 records), and a glacial aquifer study (729,564 records).
Fifty-seven principal aquifers and 65 secondary hydrogeologic regions have been designated in the conterminous United States. The principal aquifers and secondary hydrogeologic regions vary in depth, thickness, lithology, and transmissivity characteristics. Some principal aquifers underlie secondary hydrogeologic regions, and may in turn be overlain by glacial sediment or basin and valley fill aquifers, which may also be used as drinking-water sources. The principal aquifer and secondary hydrogeologic region polygons were merged with overlying sediment polygons, where present, including glacial sediment, coarse glacial sediment, and stream valley alluvium (alluvium) polygons, to generate unique hydrogeologic settings across the conterminous United States. A total of 288 distinct hydrogeologic settings resulted from the merging of principal aquifer, secondary hydrogeologic region, glacial sediment, coarse glacial sediment, and alluvium polygons.
Each well was assigned to a hydrogeologic setting on the basis of location. Hydrogeologic setting well groupings were used to guide calculations of the median value for well depth and depth to and length of open intervals across the hydrogeologic setting. Where well data were sparse or missing, wells from hydrogeologic settings with similar well construction properties, geology, physiography, and topography were grouped and used to calculate the moving median depth (if less than five wells in a 100-kilometer [62.1-mile] radius) and to estimate open interval length (if not available within hydrogeologic setting). Grids were generated to represent what might be considered as the “typical” or “median” domestic- and public-supply well in an area. The well properties are defined with moving median grids of top depth, bottom depth, and length of open interval at a 1-square-kilometer (0.38-square-mile) grid cell scale.
Median depths and open intervals of domestic- and public-supply wells varied by lithology of the hydrogeologic setting and overlying sediment. Overall, the median depths were 142 feet (43.3 meters) for all domestic-supply wells and 202 feet (61.6 meters) for all public-supply wells. The median open intervals were 21 feet (6.4 meters) for domestic-supply wells and 49 feet (14.9 meters) for public-supply wells. The shallowest median bottom open interval depths for domestic-supply wells were in the secondary hydrogeologic regions with coarse glacial sediment, which suggests that the wells are most commonly completed in the permeable coarse glacial sediment and not in the underlying secondary hydrogeologic region. Public-supply wells were completed at relatively shallow median depths when drilled in permeable sediment that overlie secondary hydrogeologic regions. When public-supply wells were completed in principal aquifers, the median depths were typically greater than wells completed in secondary hydrogeologic regions.
Well data used in this study were limited to those available from national or State digital databases. Several quality-assurance checks were performed during data compilation, but a comprehensive quality assurance inspection for each of the data sources was outside the scope of this study. Grids defining typical open intervals in domestic- and public-supply wells are presented. Although there are many places where multiple aquifers are stacked, these results correspond primarily to the aquifer with the highest documented number of wells for each use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215069","programNote":"National Water Quality Program","usgsCitation":"Degnan, J.R., Kauffman, L.J., Erickson, M.L., Belitz, K., and Stackelberg, P.E., 2021, Depth of groundwater used for drinking-water supplies in the United States: U.S. Geological Survey Scientific Investigations Report 2021–5069, 69 p., https://doi.org/10.3133/sir20215069.","productDescription":"Report: iv, 69 p.; Data Release; Interactive Maps","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122329","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390686,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94640EM","text":"USGS data release","description":"USGS data release","linkHelpText":"Data for depth of groundwater used for drinking-water supplies in the United 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Although polar bears (Ursus maritimus) and brown bears (U. arctos) have been exhibited in zoological gardens for centuries, little is known about their nutritional needs. Multiple recent studies on both wild and captive polar bears and brown bears have found that they voluntarily select dietary macronutrient proportions resulting in much lower dietary protein and higher fat or digestible carbohydrate concentrations than are currently fed in most zoos. These lower protein concentrations selected by both species maximized growth rates and efficiencies of energy utilization in brown bears and may play a role in reducing kidney, liver, and cardiovascular diseases in both species. Therefore, we propose the need for the development of new dietary regimens for both species in managed care that better reflect their macronutrient needs. We developed a new kibble that is higher in fat and lower in protein than typical diets that have been fed in managed care, has a fatty acid profile more consistent with wild bear diets, and has been readily consumed by both brown bears and polar bears. The kibble can be fed as the sole diet or as part of more complex diets with additional fruits, meats, or vegetables. Because many nutritional deficiencies and related diseases can take months or years to appear, we urge caution and continued long-term monitoring of bears and their diets to ensure their optimal health.
The Kirkwood–Cohansey aquifer in southern New Jersey is an important source of drinking-water supplies, but the availability of the resource is limited in some areas by high concentrations of radium, a potential carcinogen at elevated concentrations. Radium (226Ra plus 228Ra) concentrations from a network of 25 drinking-water wells showed a statistically significant increase over a decadal time scale (p < 0.05), with a median increase of 0.35 picocuries per liter. Increases in Ra are correlated with road-salt application rates, and we hypothesize that the correlation is causal. Geochemical processes associated with road-salt applications that can mobilize Ra into solution include competition by excess sodium for sorption sites and formation of chloride complexes (RaCl+ and RaCl2). The largest increases in Ra were in groundwater with low pH (≤5), which is an indirect surrogate for low cation-sorption capacity. Correlations with other potential anthropogenic causes for the increase in Ra were not observed, further suggesting a road-salt effect. Given the significant increase in Ra concentrations in this drinking-water source, the known carcinogenic risks from Ra, the direct link to road-salt application, and the likelihood for continued increases, additional monitoring is necessary in areas with similar hydrogeologic and geochemical settings.
Turtle body size is associated with demographic and other traits like mating success, reproductive output, maturity, and survival. As such, growth analyses are valuable for testing life history theory, demographic modeling, and conservation planning. Two important but unsettled research areas relate to growth after maturity and growth rate variation. If individuals exhibit indeterminate growth after maturity, older adults may have an advantage in fecundity, survival, or both over younger/smaller adults. Similarly, depending on how growth varies, a portion of the population may mature earlier, grow larger, or both. We used 23-years of capture-mark-recapture data to study growth and maturity in the Spotted Turtle (Clemmys guttata), a species suffering severe population declines and for which demographic data are needed for development of effective conservation and management strategies. There was strong support for models incorporating sex as a factor, with the interval growth model reparametrized for capture-mark-recapture data producing later mean maturation estimates than the age-based growth model. We found most individuals (94%) continued growing after maturity, but the instantaneous relative annual plastral growth rate was low. We recommend future studies examine the possible contribution of such slow, continued adult growth to fecundity and survival. Even seemingly negligible amounts of annual adult growth can have demographic consequences affecting the population vital rates for long-lived species.
Arid and semiarid ecosystems drive year-to-year variability in the strength of the terrestrial carbon (C) sink, yet there is uncertainty about how soil C gains and losses contribute to this variation. To address this knowledge gap, we embedded C-depleted soil mesocosms, containing litter or biocrust C inputs, within an in situ dryland ecosystem warming experiment. Over the course of one year, changes in microbial biomass and total soil organic C pools were monitored alongside hourly measurements of soil CO2 flux. We also developed a biogeochemical model to explore the mechanisms that gave rise to observed soil C dynamics. Field data and model simulations demonstrated that water exerted much stronger control on soil biogeochemistry than temperature, with precipitation events triggering large CO2 pulses and transport of litter- and biocrust-derived C into the soil profile. We expected leaching of organic matter would result in steady accumulation of C within the mineral soil over time. Instead, the size of the total organic C pool fluctuated throughout the year, largely in response to microbial growth: increases in the size of microbial biomass were negatively correlated with the quantity of C residing in the top 2 cm, where most biogeochemical changes were observed. Our data and models suggest that microbial responses to precipitation events trigger rapid metabolism of dissolved organic C inputs, which strongly limit accumulation of autotroph-derived C belowground. Accordingly, changes in the magnitude and/or frequency of precipitation events in this dryland ecosystem could have profound impacts on the strength of the soil C sink.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006492","usgsCitation":"Waring, B.G., Smith, K.R., Grote, E.E., Howell, A.J., Reibold, R.H., Tucker, C.L., and Reed, S., 2021, Climatic controls on soil carbon accumulation and loss in a dryland ecosystems: Journal of Geophysical Research, v. 126, no. 12, e2021JG006492, 13 p., https://doi.org/10.1029/2021JG006492.","productDescription":"e2021JG006492, 13 p.","ipdsId":"IP-133338","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450184,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1978553","text":"External Repository"},{"id":393749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Castle Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.46605682373047,\n 38.58896696823242\n ],\n [\n -109.30744171142578,\n 38.58896696823242\n ],\n [\n -109.30744171142578,\n 38.718465403583835\n ],\n [\n -109.46605682373047,\n 38.718465403583835\n ],\n [\n -109.46605682373047,\n 38.58896696823242\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":829892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kenneth R","contributorId":270738,"corporation":false,"usgs":false,"family":"Smith","given":"Kenneth","email":"","middleInitial":"R","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":829893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grote, Edmund E. 0000-0002-9103-9482 ed_grote@usgs.gov","orcid":"https://orcid.org/0000-0002-9103-9482","contributorId":4271,"corporation":false,"usgs":true,"family":"Grote","given":"Edmund","email":"ed_grote@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howell, Armin J. 0000-0003-1243-0238 ahowell@usgs.gov","orcid":"https://orcid.org/0000-0003-1243-0238","contributorId":196798,"corporation":false,"usgs":true,"family":"Howell","given":"Armin","email":"ahowell@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":829897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829898,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220289,"text":"70220289 - 2021 - Arctic Alaska Basin, Hanna Trough and Beaufortian Rifted Margin Composite Tectono-Sedimentary Elements","interactions":[],"lastModifiedDate":"2025-02-04T16:09:58.361136","indexId":"70220289","displayToPublicDate":"2021-11-17T10:04:25","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Arctic Alaska Basin, Hanna Trough and Beaufortian Rifted Margin Composite Tectono-Sedimentary Elements","docAbstract":"The Arctic Alaska Composite Tectono-Sedimentary Element (AA CTSE) as defined for this volume comprises Mississippian to Lower Cretaceous strata beneath the Alaska North Slope and the Beaufort and Chukchi Seas of the Arctic Ocean. The AA CTSE rests on Devonian and older sedimentary and metasedimentary rocks, considered economic basement for petroleum, and is overlain by Cretaceous to Cenozoic syntectonic strata deposited in the foreland of the Chukotka and Brooks Range orogens. The Mississippian – Triassic part of the AA CTSE is divided into a fold-and-thrust belt in the south and a relatively undeformed platform in the north. The Jurassic – Lower Cretaceous part of the AA CTSE is divided into synrift basins in the north and rift-shoulder deposits in the south. The AA CTSE includes oil-prone source rocks in the Triassic, Jurassic, and Cretaceous and proven reservoir rocks spanning the Mississippian to Lower Cretaceous. Much of the central part of the AA CTSE lies in the oil window whereas the northern and southern parts are mainly in the gas window. Known hydrocarbon accumulations in the AA CTSE total more than 30 billion barrels of oil equivalent and yet-to-find estimates suggest a similar volume remains to be discovered","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society, London, Memoirs","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"The Geological Society of London","doi":"10.1144/m57-2018-26","usgsCitation":"Houseknecht, D.W., 2021, Arctic Alaska Basin, Hanna Trough and Beaufortian Rifted Margin Composite Tectono-Sedimentary Elements, chap. of Geological Society, London, Memoirs, v. 57, 18 p., https://doi.org/10.1144/m57-2018-26.","productDescription":"18 p.","ipdsId":"IP-097477","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":490072,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/m57-2018-26","text":"Publisher Index Page"},{"id":481671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -136.5744800240832,\n 73.48129004791153\n ],\n [\n -138.2471596101051,\n 75.08779606716953\n ],\n [\n -155.01532165240496,\n 76.6720069019496\n ],\n [\n -173.46045058586498,\n 72.51742405199943\n ],\n [\n -166.78189271016328,\n 68.39273635171199\n ],\n [\n -134.69076739237028,\n 68.41381738362409\n ],\n [\n -136.5744800240832,\n 73.48129004791153\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"57","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Houseknecht, David W. 0000-0002-9633-6910 dhouse@usgs.gov","orcid":"https://orcid.org/0000-0002-9633-6910","contributorId":645,"corporation":false,"usgs":true,"family":"Houseknecht","given":"David","email":"dhouse@usgs.gov","middleInitial":"W.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815020,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225532,"text":"sir20215109 - 2021 - Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","interactions":[],"lastModifiedDate":"2021-11-23T13:06:28.021637","indexId":"sir20215109","displayToPublicDate":"2021-11-17T07:15:00","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":"2021-5109","displayTitle":"Documentation and Mapping of Flooding From the January and March 2018 Nor’easters in Coastal New England","title":"Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","docAbstract":"In January and March 2018, coastal Massachusetts experienced flooding from two separate nor’easters. To put the January and March floods into historical context, the USGS computed statistical stillwater elevations. Stillwater elevations recorded in January 2018 in Boston (9.66 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 2 and 1 percent (between a 50- and 100-year recurrence interval). Stillwater elevations recorded in March 2018 in Boston (9.17 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 4 and 2 percent (between a 25- and 50-year recurrence interval). Flood maps show that the area inundated by the January storm is slightly more extensive than that of the March storm, reflecting the respective profiles of the two storms. On the basis of a limited dataset, the attenuation of peak water levels was estimated as a function of the hydraulic distance inland and the starting stillwater elevation computed for the flood within 0.6 foot of what was measured in the field. A simple one-dimensional model was calibrated using flood elevation data collected after the January flood, and the results of the model were validated using flood elevation data collected after the March flood to model the attenuation of the flood elevations as the storms move inland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215109","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P.J., Olson, S.A., Sturtevant, L.P., and Kalmon, R.D., 2021, Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England: U.S. Geological Survey Scientific Investigations Report 2021–5109, 13 p., https://doi.org/10.3133/sir20215109.","productDescription":"Report: iv, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125348","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390667,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RINQ4B","text":"USGS data release","linkHelpText":"Data and shapefiles used to document the floods associated with the January and March 2018 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Ecological transformation creates many challenges for public natural resource management and requires managers to grapple with new relationships to change and new ways to manage it. In the context of unfamiliar trajectories of ecological change, a manager can resist, accept, or direct change, choices that make up the resist-accept-direct (RAD) framework. In this article, we provide a conceptual framework for how to think about this new decision space that managers must navigate. We identify internal factors (mental models) and external factors (social feasibility, institutional context, and scientific uncertainty) that shape management decisions. We then apply this conceptual framework to the RAD strategies (resist, accept, direct) to illuminate how internal and external factors shape those decisions. Finally, we conclude with a discussion of how this conceptual framework shapes our understanding of management decisions, especially how these decisions are not just ecological but also social, and the implications for research and management.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biab086","usgsCitation":"Clifford, K.R., Cravens, A.E., and Knapp, C.N., 2021, Responding to ecological transformation: Mental models, external constraints, and manager decision-making: BioScience, v. 72, no. 1, p. 57-70, https://doi.org/10.1093/biosci/biab086.","productDescription":"14 p.","startPage":"57","endPage":"70","ipdsId":"IP-127232","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab086","text":"Publisher Index Page"},{"id":394010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clifford, Katherine R. 0000-0002-1385-8765","orcid":"https://orcid.org/0000-0002-1385-8765","contributorId":259886,"corporation":false,"usgs":true,"family":"Clifford","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knapp, Corrine N.","contributorId":270993,"corporation":false,"usgs":false,"family":"Knapp","given":"Corrine","email":"","middleInitial":"N.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":830321,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226753,"text":"70226753 - 2021 - Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","interactions":[],"lastModifiedDate":"2021-12-09T12:35:17.454202","indexId":"70226753","displayToPublicDate":"2021-11-17T06:32:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","docAbstract":"Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m2) and coarse (100 m2) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under-canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100-m and 1-m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine-scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance.
In a scientifically-transformative project, South Africa implemented a decade-long field experiment to understand how fisheries may be affecting its most iconic seabird, the African penguin Spheniscus demersus. This unique effort prohibits the take of anchovy and sardine within relatively small areas around four African penguin breeding colonies, two in the Benguela upwelling ecosystem and two in the adjacent Agulhas region. For the Benguela, fisheries closures within the birds’ primary foraging range increased their breeding productivity and perhaps reduced parental foraging efforts, indicating that the fisheries are competing with the birds for food. Results were less clear for foraging behaviour in the Agulhas, but no data on breeding success were collected there. The African penguin is endangered, its population continues to decline, and fisheries closures have been demonstrated to improve demographic traits that contribute to population growth. Therefore, given the critical status of the species, fisheries closures should be maintained, at least at Dassen Island where the population has great capacity to expand and support other nearby colonies. Continuing or implementing corresponding fisheries closures in the Agulhas region is also warranted, as well as creating and testing the value of pelagic closed areas during the non-breeding season when the penguins disperse widely across these ecosystems. These management actions would increase penguin food supplies and may help to meet societal goals of halting the decline of the penguin population, as well as maintaining the economic and cultural services provided by fisheries and ecotourism.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsab231","usgsCitation":"Sydeman, W., Hunt, G.L., Pikitch, E., Parrish, J.K., Piatt, J.F., Boersma, P.D., Kaufman, L., Anderson, D.W., Thompson, S.A., and Sherley, R.B., 2021, South Africa's experimental fisheries closures and recovery of the endangered African penguin: ICES Journal of Marine Science, v. 78, no. 10, p. 3538-3543, https://doi.org/10.1093/icesjms/fsab231.","productDescription":"5 p.","startPage":"3538","endPage":"3543","ipdsId":"IP-129144","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsab231","text":"Publisher Index Page"},{"id":398878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Africa","otherGeospatial":"Agulhas, Benguela, Dassen Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 18.17138671875,\n -35.7465122599185\n ],\n [\n 27.6416015625,\n -35.7465122599185\n ],\n [\n 27.6416015625,\n -33.41310221370828\n ],\n [\n 18.17138671875,\n -33.41310221370828\n ],\n [\n 18.17138671875,\n -35.7465122599185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sydeman, William J.","contributorId":172574,"corporation":false,"usgs":false,"family":"Sydeman","given":"William J.","affiliations":[],"preferred":false,"id":840711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunt, George L. Jr.","contributorId":56953,"corporation":false,"usgs":true,"family":"Hunt","given":"George","suffix":"Jr.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":840712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pikitch, E.K.","contributorId":152152,"corporation":false,"usgs":false,"family":"Pikitch","given":"E.K.","email":"","affiliations":[],"preferred":false,"id":840713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parrish, Julia K.","contributorId":220055,"corporation":false,"usgs":false,"family":"Parrish","given":"Julia","email":"","middleInitial":"K.","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":840714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":840715,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boersma, P Dee","contributorId":290290,"corporation":false,"usgs":false,"family":"Boersma","given":"P","email":"","middleInitial":"Dee","affiliations":[],"preferred":false,"id":840720,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaufman, Les","contributorId":260242,"corporation":false,"usgs":false,"family":"Kaufman","given":"Les","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":840716,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Daniel W.","contributorId":74345,"corporation":false,"usgs":false,"family":"Anderson","given":"Daniel","email":"","middleInitial":"W.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":840717,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, Sarah Ann","contributorId":198394,"corporation":false,"usgs":false,"family":"Thompson","given":"Sarah","email":"","middleInitial":"Ann","affiliations":[],"preferred":false,"id":840718,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sherley, Richard B.","contributorId":198407,"corporation":false,"usgs":false,"family":"Sherley","given":"Richard","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":840719,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70226145,"text":"sir20215121 - 2021 - Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18","interactions":[],"lastModifiedDate":"2021-11-19T21:06:31.076465","indexId":"sir20215121","displayToPublicDate":"2021-11-16T13:40:00","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":"2021-5121","displayTitle":"Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18","title":"Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18","docAbstract":"The U.S. Geological Survey measured cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (anaC, cyrA, taxa specific mcyE, and sxtA), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey produces research quality, applications ready, Level-2 Science Products derived from Landsat Collection 2 Level-1 data. These products are used to monitor, assess, and project changes in land use, land cover, and environmental conditions affecting the human condition, natural processes, and biological habitats. Landsat Collection 2 Level-2 Science Products are time-series observational data processed for consistency and continuity to measure effects of environmental change and serve as input into Landsat essential climate variable Level-3 Science Products.
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U.S. Geological Survey
47914 252nd Street
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Occupancy models are often used to analyze long-term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non-detection data at a sample unit and many long-term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space-for-time substitution). Still, the efficacy of fitting occupancy models using a space-for-time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (Hypomesus transpacificus) and Longfin Smelt (Spirinchus thaleichthys) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay-Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space-for-time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space-for-time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long-term monitoring data to understand the distribution of species.
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U.S. Geological Survey
MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears (Ursus maritimus). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progressive decline in access to prey or an increase in the energetic cost of accessing prey has the potential to adversely affect polar bear population dynamics. We examined long-term (1983–2015) patterns of spring body condition (indexed using residual body mass) and maternal investment (i.e., litter mass of cubs-of-the-year and yearlings; COY and YRL) of polar bears from Alaska’s southern Beaufort Sea to evaluate potential relationships with regional- and circumpolar-scale sea ice conditions and atmospheric patterns. The length of the summer open-water (OW) season (i.e., the period of time the sea ice is mostly absent from the continental shelf) increased at a rate of 18 days decade-1 over the study period. However, the OW season duration was not a strong determinant of spring residual body mass or litter mass. Residual body mass of independent (i.e., subadults and adults) female bears varied relative to age class, reproductive status, and the strength of the prior winter’s Arctic Oscillation (i.e., a circumpolar-scale mode of climate variability driven by long-term atmospheric patterns). Spring residual mass of independent males varied with age class and variation in wind speed (i.e., regional-scale short-term atmospheric patterns) during the winter of the year preceding capture. Over the study period, mean annual body mass of adult females unaccompanied by COY declined by 4 kg/ decade-1, while no temporal trends were evident in the mean annual body mass of adult females with COY, adult males, and subadults. Litter mass of COY varied relative to capture date, maternal age class and mass, litter size, and year of capture. Litter mass of YRL varied with capture date, maternal age class and mass, litter size, variation in winter wind speed (the year of and year preceding capture), and the strength of the prior winter’s Arctic Oscillation. Mean annual litter mass of COY decreased at a rate of 2.6 kg decade-1 and declined 0.68 kg for every 10 kg reduction in maternal mass. No trend was evident in the mean annual litter mass of yearlings. These findings suggest a nuanced response of the southern Beaufort Sea polar bears to environmental change, where some demographic groups (e.g., adult males and subadults) are presently more resilient than others to changes in the Arctic marine ecosystem.
Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology
The U.S. Geological Survey (USGS) Water-Use Program is responsible for compiling and disseminating the Nation's water-use data. Working in cooperation with local, State, and Federal agencies, the USGS has collected and published national water-use estimates every 5 years, beginning in 1950. These water-use data may vary because of actual changes in water use, because of changes in estimation methods, or because of errors. Comparison and interpretation of these data is difficult without first determining the factors that contribute to data variability. This report describes factors that may affect data quality and documents ways to investigate the variability of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data for the 1985–2015 compilations.
The USGS produces national water-use estimates for various categories of water use for every county in the United States. Knowledge about the sources of data for county estimates is important because factors such as estimation methodology and reporting affect data uncertainty Determination of meaningful patterns and trends in the data are contingent on the use of consistent methodology throughout the period of interest. With the many ways that water-use data have been collected, assembled, and estimated, multiple factors likely contribute to data uncertainty, Data used to produce these estimates may be furnished from agencies that collect information from entities who report water use; gaps in reported data are typically estimated to achieve a comprehensive county estimate. For example, public supply and thermoelectric category data are based primarily on furnished site-specific data; whereas crop irrigation is often furnished or estimated at the county scale. Public supply deliveries for domestic use and self-supplied domestic withdrawals are most often estimated by USGS personnel using per capita use rate coefficients. Irrigation may be estimated using crop water requirements, application rates, or other soil water balance methods when furnished reported data are not available.
Rates, percentages, medians, and interquartile ranges were used to investigate variability in the water-use data among States, regions, and years. The purposes of these evaluations were to (1) identify extreme values that may reflect changes in information sources, estimation methods, or errors; (2) indicate areas of variable or consistent values that are unexpected; and (3) indicate areas where values change because of local climate or other factors. Where factors are identified that contribute to data variability, such as a change in methodology, additional work could determine uncertainty because of these factors.
These evaluations identified the availability of information that is needed to address data limitations. Factors such as estimation methodology affect data quality. Some updates to method codes assigned in 2015 and assignment of method codes to earlier compilation datasets for all categories would provide much needed metadata for users of the data. Improvements in data documentation describing sources of information and estimation methods and additional metadata information from agencies and entities that furnish water-use data, would enable a more complete understanding and depiction of water-use patterns and trends. Additional metadata are needed for users of the data to better understand the water-use data and interpret changes in water use across the United States and with time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215082","usgsCitation":"Luukkonen, C.L., Belitz, K., Sullivan, S.L., and Sargent, P., 2021, Factors affecting uncertainty of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data, 1985–2015—Evaluation of information sources, estimation methods, and data variability: U.S. Geological Survey Scientific Investigations Report 2021–5082, 78 p., https://doi.org/10.3133/sir20215082.","productDescription":"Report: ix, 78 p.; Database; Data Release","onlineOnly":"Y","ipdsId":"IP-123556","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391626,"rank":3,"type":{"id":30,"text":"Data 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U.S. Geological Survey
5840 Enterprise Drive
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Medicine Lake Highlands/Fall River Springs Aquifer System, located in northeastern California, is home to some of the largest first-order springs in the United States. This work assesses the likely effects of projected climate change on spring flow. Four anticipated climate futures (GFDL A2, GFDL B1, CCSM4 rcp 8.5, CNRM rcp 8.5) for California, which predict a range of conditions (generally warming and transitioning from snow to rain with variable amounts of total precipitation), are postulated to affect groundwater recharge primarily by changing evapotranspiration. The linkages between climate variables and spring flow are evaluated using a water balance model that represents the physics of evapotranspiration and recharge, the Basin Characterization Model. Three of the four climate scenarios (GFDL A2, GFDL B1, CCSM4 rcp 8.5) project that by the year 2100, groundwater recharge (and consequently decreased spring flow) will decrease by 27%, 21%, and 9%, respectively. The fourth scenario (CNRM rcp 8.5) showed an increase in recharge of 32% due to a significant increase in precipitation (27%). Evapotranspiration increases due to a shift in the type of precipitation and a longer growing season. While the likelihood of each scenario is outside the scope of this work, unless total precipitation increases dramatically in the future, increased temperatures and decreasing precipitation will likely result in reduced spring flows, along with warmer water temperatures in downstream habitats.