High-latitude peatlands are changing rapidly in response to climate change, including permafrost thaw. Here, we reconstruct hydrological conditions since the seventeenth century using testate amoeba data from 103 high-latitude peat archives. We show that 54% of the peatlands have been drying and 32% have been wetting over this period, illustrating the complex ecohydrological dynamics of high latitude peatlands and their highly uncertain responses to a warming climate.
The U.S. Geological Survey maintains a network of hydrologic monitoring stations across Kansas in cooperation with Federal, State, Tribal, and local agencies. During water year 2021, this network included 230 real-time surface water data collection sites, referred to as “streamgages.” A water year is the 12-month period from October 1 through September 30 and is designated by the calendar year in which it ends. These real-time data are routinely collected and calibrated by U.S. Geological Survey personnel via regular measurements of streamflow and water levels. Analyses of these data provide an understanding of hydrologic conditions in the State critical to the management of water resources, industrial and agricultural uses, protection of life and property during flooding, operation of reservoirs, recreation, and other activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223071","usgsCitation":"Puls, K.A., 2022, Hydrologic conditions in Kansas, water year 2021: U.S. Geological Survey Fact Sheet 2022–3071, 6 p., https://doi.org/10.3133/fs20223071.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-141048","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":405358,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3071/images"},{"id":405356,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3071/fs20223071.pdf","text":"Report","size":"4.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3071"},{"id":405355,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3071/coverthb.jpg"},{"id":501508,"rank":6,"type":{"id":36,"text":"NGMDB Index 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\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Wetland ecosystems are diverse, productive habitats that are essential reservoirs of biodiversity. Not only are they home to numerous wetland-specialist species, but they also provide food, water, and shelter that support terrestrial wildlife populations. However, like observed patterns of biodiversity loss, wetland habitats have experienced widespread loss and degradation. In order to conserve and restore wetlands, and thereby the biodiversity they support, it is important to understand how biodiversity in wetland habitats is maintained. Habitat heterogeneity and connectivity are thought to be predominate drivers of wetland biodiversity. We quantified temporal coherence (i.e., spatial synchrony) of wetland invertebrate communities using intra-class correlations among 16 wetlands sampled continuously over 24 years to better understand the relative influences wetland heterogeneity (i.e., internal processes specific to individual wetlands and spatial connectivity and external processes occurring on the landscape) on wetland biodiversity. We found that while wetlands with different ponded-water regimes (temporarily ponded or permanently ponded) often hosted different invertebrate communities, temporal shifts in invertebrate composition were synchronous. We also found the relative importance of internal versus external forces in determining community assembly vary depending on a wetland’s hydrologic function and climate influences. Our results confirm that heterogeneity and spatial connectivity of wetland landscapes are important drivers of wetland biodiversity.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.897872","usgsCitation":"McLean, K., Mushet, D., and Sweetman, J.N., 2022, Temporal coherence patterns of prairie pothole wetlands indicate the importance of landscape linkages and wetland heterogeneity in maintaining biodiversity: Frontiers in Ecology and Evolution, v. 10, 897872, 16 p., https://doi.org/10.3389/fevo.2022.897872.","productDescription":"897872, 16 p.","ipdsId":"IP-123627","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.897872","text":"Publisher Index Page"},{"id":408491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1056,\n 47.0944\n ],\n [\n -99.088889,\n 47.0944\n ],\n [\n -99.088889,\n 47.1027\n ],\n [\n -99.1056,\n 47.1027\n ],\n [\n -99.1056,\n 47.0944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon N. 0000-0002-9849-7355","orcid":"https://orcid.org/0000-0002-9849-7355","contributorId":221489,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":854925,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243220,"text":"70243220 - 2022 - New projections of 21st century climate and hydrology for Alaska and Hawaiʻi","interactions":[],"lastModifiedDate":"2023-05-04T11:52:28.55815","indexId":"70243220","displayToPublicDate":"2022-08-07T06:50:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5567,"text":"Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"New projections of 21st century climate and hydrology for Alaska and Hawaiʻi","docAbstract":"In the United States, high-resolution, century-long, hydroclimate projection datasets have been developed for water resources planning, focusing on the contiguous United States (CONUS) domain. However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.
Coastal wetlands provide key ecosystem services, including substantial long-term storage of atmospheric CO2 in soil organic carbon pools. This accumulation of soil organic matter is a vital component of elevation gain in coastal wetlands responding to sea-level rise. Anthropogenic activities that alter coastal wetland function through disruption of tidal exchange and wetland water levels are ubiquitous. This study assesses soil vertical accretion and organic carbon accretion across five coastal wetlands that experienced over a century of impounded hydrology, followed by restoration of tidal exchange 5 to 14 years prior to sampling. Nearby marshes that never experienced tidal impoundment served as controls with natural hydrology to assess the impact of impoundment and restoration. Dated soil cores indicate that elevation gain and carbon storage were suppressed 30–70 % during impoundment, accounting for the majority of elevation deficit between impacted and natural sites. Only one site had substantial subsidence, likely due to oxidation of soil organic matter. Vertical and carbon accretion gains were achieved at all restored sites, with carbon burial increasing from 96 ± 33 to 197 ± 64 g C m−2 y−1. The site with subsidence was able to accrete at double the rate (13 ± 5.6 mm y−1) of the natural complement, due predominantly to organic matter accumulation rather than mineral deposition, indicating these ecosystems are capable of large dynamic responses to restoration when conditions are optimized for vegetation growth. Hydrologic restoration enhanced elevation resilience and climate benefits of these coastal wetlands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157682","usgsCitation":"Eagle, M.J., Kroeger, K.D., Spivak, A.C., Wang, F., Tang, J., Abdul-Aziz, O.I., Ishtiaq, K.S., O’Keefe Suttles, J.A., and Mann, A.G., 2022, Soil carbon consequences of historic hydrologic impairment and recent restoration in coastal wetlands: Science of the Total Environment, v. 848, 157682, 12 p., https://doi.org/10.1016/j.scitotenv.2022.157682.","productDescription":"157682, 12 p.","ipdsId":"IP-140249","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446890,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157682","text":"Publisher Index Page"},{"id":405111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.56655883789062,\n 41.693424216151314\n ],\n [\n -69.96231079101562,\n 41.693424216151314\n ],\n [\n -69.96231079101562,\n 41.87262868373214\n ],\n [\n -70.56655883789062,\n 41.87262868373214\n ],\n [\n -70.56655883789062,\n 41.693424216151314\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"848","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":848843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spivak, Amanda C.","contributorId":191376,"corporation":false,"usgs":false,"family":"Spivak","given":"Amanda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":848844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Faming","contributorId":216959,"corporation":false,"usgs":false,"family":"Wang","given":"Faming","email":"","affiliations":[{"id":39553,"text":"The Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA","active":true,"usgs":false}],"preferred":false,"id":848845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tang, Jianwu","contributorId":174890,"corporation":false,"usgs":false,"family":"Tang","given":"Jianwu","email":"","affiliations":[{"id":27818,"text":"The Ecosystems Center, Marine Biological Laboratory. Woods Hole, MA 02543.","active":true,"usgs":false}],"preferred":false,"id":848846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abdul-Aziz, Omar I.","contributorId":192386,"corporation":false,"usgs":false,"family":"Abdul-Aziz","given":"Omar","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":848847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ishtiaq, Khandker S.","contributorId":211669,"corporation":false,"usgs":false,"family":"Ishtiaq","given":"Khandker","email":"","middleInitial":"S.","affiliations":[{"id":38311,"text":"Department of Civil and Environmental Engineering, West Virginia University, PO Box 6103, Morgantown, WV 26506","active":true,"usgs":false}],"preferred":false,"id":848848,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"O’Keefe Suttles, Jennifer A. 0000-0003-2345-5633","orcid":"https://orcid.org/0000-0003-2345-5633","contributorId":202609,"corporation":false,"usgs":true,"family":"O’Keefe Suttles","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848849,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mann, Adrian G. 0000-0003-1689-8524 adriangreen@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-8524","contributorId":4328,"corporation":false,"usgs":true,"family":"Mann","given":"Adrian","email":"adriangreen@usgs.gov","middleInitial":"G.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848850,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70234246,"text":"70234246 - 2022 - Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams","interactions":[],"lastModifiedDate":"2022-08-05T13:15:34.056536","indexId":"70234246","displayToPublicDate":"2022-08-05T08:08:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams","docAbstract":"In mountain headwater streams, the quality and resilience of summer cold-water habitat is generally regulated by stream discharge, longitudinal stream channel connectivity and groundwater exchange. These critical hydrologic processes are thought to be influenced by the stream corridor bedrock contact depth (sediment thickness), a parameter often inferred from sparse hillslope borehole information, piezometer refusal and remotely sensed data. To investigate how local bedrock depth might control summer stream temperature and channel disconnection (dewatering) patterns, we measured stream corridor bedrock depth by collecting and interpreting 191 passive seismic datasets along eight headwater streams in Shenandoah National Park (Virginia, USA). In addition, we used multi-year stream temperature and streamflow records to calculate several baseflow-related metrics along and among the study streams. Finally, comprehensive visual surveys of stream channel dewatering were conducted in 2016, 2019 and 2021 during summer low flow conditions (124 total km of stream length). We found that measured bedrock depths along the study streams were not well-characterized by soils maps or an existing global-scale geologic dataset where the latter overpredicted measured depths by 12.2 m (mean) or approximately four times the average bedrock depth of 2.9 m. Half of the eight study stream corridors had an average bedrock depth of less than 2 m. Of the eight study streams, Staunton River had the deepest average bedrock depth (3.4 m), the coldest summer temperature profiles and substantially higher summer baseflow indices compared to the other study steams. Staunton River also exhibited paired air and water annual temperature signals suggesting deeper groundwater influence, and the stream channel did not dewater in lower sections during any baseflow survey. In contrast, Paine Run and Piney River did show pronounced, patchy channel dewatering, with Paine Run having dozens of discrete dry channel sections ranging from 1 to greater than 300 m in length. Stream dewatering patterns were apparently influenced by a combination of discrete deep bedrock (20+ m) features and more subtle sediment thickness variation (1–4 m) depending on local stream valley hydrogeology. In combination, these unique datasets show the first large-scale empirical support for existing conceptual models of headwater stream disconnection based on spatially variable underflow capacity and shallow groundwater supply.","language":"English","publisher":"Copernicus","doi":"10.5194/hess-26-3989-2022","usgsCitation":"Briggs, M., Goodling, P.J., Johnson, Z., Rogers, K., Hitt, N.P., Fair, J.H., and Snyder, C.D., 2022, Bedrock depth influences spatial patterns of summer baseflow, temperature and flow disconnection for mountainous headwater streams: Hydrology and Earth System Sciences, v. 26, no. 15, p. 3989-4011, https://doi.org/10.5194/hess-26-3989-2022.","productDescription":"23 p.","startPage":"3989","endPage":"4011","ipdsId":"IP-132407","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":446904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-26-3989-2022","text":"Publisher Index Page"},{"id":404871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Blue Ridge Mountains, Shenandoah National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.85711669921875,\n 38.098901948321256\n ],\n [\n -78.8433837890625,\n 38.039438891821746\n ],\n [\n -78.71978759765625,\n 38.090255780611486\n ],\n [\n -78.69369506835938,\n 38.182068998322094\n ],\n [\n -78.64151000976562,\n 38.19718009396176\n ],\n [\n -78.60580444335938,\n 38.26945406815749\n ],\n [\n -78.50830078125,\n 38.312568460056966\n ],\n [\n -78.38333129882812,\n 38.33734763569314\n ],\n [\n -78.33663940429688,\n 38.43745529233546\n ],\n [\n -78.26385498046875,\n 38.53957267203905\n ],\n [\n -78.233642578125,\n 38.65119833229951\n ],\n [\n -78.2281494140625,\n 38.716590286734494\n ],\n [\n -78.1402587890625,\n 38.74551518488265\n ],\n [\n -78.13888549804686,\n 38.8407772667165\n ],\n [\n -78.15536499023438,\n 38.89423942194029\n ],\n [\n -78.2061767578125,\n 38.93698019310818\n ],\n [\n -78.23089599609375,\n 38.872859384572244\n ],\n [\n -78.22128295898438,\n 38.81296105899589\n ],\n [\n -78.25698852539062,\n 38.79476766282312\n ],\n [\n -78.26522827148438,\n 38.8225909761771\n ],\n [\n -78.31878662109375,\n 38.82901019751963\n ],\n [\n -78.34625244140625,\n 38.810820900566135\n ],\n [\n -78.41354370117188,\n 38.71980474264237\n ],\n [\n -78.40667724609375,\n 38.63081814300356\n ],\n [\n -78.49868774414062,\n 38.5213096674994\n ],\n [\n -78.59619140625,\n 38.541720956040386\n ],\n [\n -78.55636596679688,\n 38.43960662292255\n ],\n [\n -78.6181640625,\n 38.40302528453207\n ],\n [\n -78.82278442382812,\n 38.25543637637947\n ],\n [\n -78.85711669921875,\n 38.098901948321256\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":848323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":848324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Karli M. 0000-0002-6188-7405","orcid":"https://orcid.org/0000-0002-6188-7405","contributorId":205635,"corporation":false,"usgs":true,"family":"Rogers","given":"Karli M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848326,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":238185,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848327,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":848328,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848329,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70267454,"text":"70267454 - 2022 - Modeled streamflow response to scenarios of Tundra Lake water withdrawal and seasonal climate extremes, Arctic Coastal Plain, Alaska","interactions":[],"lastModifiedDate":"2025-05-23T15:19:02.186238","indexId":"70267454","displayToPublicDate":"2022-08-04T00:00:00","publicationYear":"2022","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":"Modeled streamflow response to scenarios of Tundra Lake water withdrawal and seasonal climate extremes, Arctic Coastal Plain, Alaska","docAbstract":"On the Arctic Coastal Plain (ACP) in Northern Alaska (USA), permafrost and abundant surface-water storage define watershed hydrological processes, which are increasingly subject to changes both in climate and land-use. In the last decades, the ACP landscape experienced extreme climate events and increased lake water withdrawal (LWW) for construction of infrastructure related to resource extraction (primarily ice roads and industrial operations). However, their potential (combined) effects on streamflow are relatively underexplored. Here, we applied the process-based, spatially distributed hydrological and thermal Water Balance Simulation Model (WaSiM) (10 m spatial resolution) to the 30 km² Crea Creek watershed located on the ACP. The impacts of documented seasonal climate extremes and LWW were evaluated on seasonal runoff (May-August), including minimum 7-day mean flow (MQ7), the recovery time of MQ7 to pre-perturbation conditions and the duration of streamflow conditions that prevents fish passage. Low-rainfall scenarios (21% of normal, 1 to 3 summers in a row) caused a larger reduction in MQ7 (56 - 69%) than LWW alone (44 - 58%). Decadal-long consecutive LWW resulted in a new equilibrium in low-flow and seasonal runoff after the third year of LWW that included a disconnected stream network, a reduced contributing area (54% of the watershed area) and limited fish passage throughout summer. Our results highlight that LWW is not offset by same-year snowmelt for lake water levels and streamflow as currently assumed in land management regulations. Effective land management would therefore benefit from considering the combined impact of climate change and industrial lake water withdrawals. \n\n ","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022wr032119","usgsCitation":"Gädeke, A., Arp, C., Liljedahl, A., Daanen, R., Cai, L., Alexeev, V., Jones, B., Wipfli, M.S., and Schulla, J., 2022, Modeled streamflow response to scenarios of Tundra Lake water withdrawal and seasonal climate extremes, Arctic Coastal Plain, Alaska: Water Resources Research, v. 58, no. 8, e2022WR032119, 19 p., https://doi.org/10.1029/2022wr032119.","productDescription":"e2022WR032119, 19 p.","ipdsId":"IP-126871","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr032119","text":"Publisher Index Page"},{"id":486512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.42580071682042,\n 70.81109388282138\n ],\n [\n -155.42580071682042,\n 69.72800714139643\n ],\n [\n -150.42311039636033,\n 69.72800714139643\n ],\n [\n -150.42311039636033,\n 70.81109388282138\n ],\n [\n -155.42580071682042,\n 70.81109388282138\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Gädeke, Anne","contributorId":355846,"corporation":false,"usgs":false,"family":"Gädeke","given":"Anne","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arp, Christopher","contributorId":355847,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liljedahl, Anna K.","contributorId":355848,"corporation":false,"usgs":false,"family":"Liljedahl","given":"Anna K.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daanen, Ronald P.","contributorId":355849,"corporation":false,"usgs":false,"family":"Daanen","given":"Ronald P.","affiliations":[{"id":84845,"text":"Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":938267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cai, Lei","contributorId":355850,"corporation":false,"usgs":false,"family":"Cai","given":"Lei","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938268,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alexeev, Vladimir","contributorId":355851,"corporation":false,"usgs":false,"family":"Alexeev","given":"Vladimir","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938269,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Benjamin","contributorId":355852,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":938270,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wipfli, Mark S. 0000-0002-4856-6068 mwipfli@usgs.gov","orcid":"https://orcid.org/0000-0002-4856-6068","contributorId":1425,"corporation":false,"usgs":true,"family":"Wipfli","given":"Mark","email":"mwipfli@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938263,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schulla, Jörg","contributorId":355853,"corporation":false,"usgs":false,"family":"Schulla","given":"Jörg","affiliations":[{"id":84846,"text":"Hydrology Software Consulting","active":true,"usgs":false}],"preferred":false,"id":938271,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236526,"text":"70236526 - 2022 - Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA","interactions":[],"lastModifiedDate":"2022-11-16T17:03:55.108551","indexId":"70236526","displayToPublicDate":"2022-08-03T07:21:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating hydrologic region assignment techniques for ungaged basins in Alaska, USA","docAbstract":"Building continental-scale hydrologic models in data-sparse regions requires an understanding of spatial variation in hydrologic processes. Extending these models to ungaged locations requires techniques to group ungaged locations with gaged ones to make process importance and model parameter transfer decisions to ungaged locations. This analysis (1) tested the utility of fundamental streamflow statistics (FDSS) in defining hydrologic regions across Alaska, USA; (2) evaluated if the hydrologic regions represented different hydrologic processes; and (3) tested the ability of random forest and direct assignment techniques, informed by statistically estimated FDSS (FDSSest) and basin characteristics (BCs), to correctly assign ungaged locations to hydrologic regions. Six hydrologic regions were identified across the domain using FDSS. Differences in mean flow, phase shift of the seasonal cycle, and skewness were the primary characteristics defining each region. Two regions represented arctic and continental climates, generally in the northern portion of the domain; four regions represented the southern, maritime portion of the domain. Random forest modeling with BCs (67% success rate) outperformed FDSSest (58% success rate) suggesting that no statistically estimated streamflow was needed to assign ungaged locations to a region. For regions with many sites, most region assignment techniques performed similarly. Random forest modeling performance declined when BCs and FDSSest were both used to predict region membership, suggesting FDSSest had little information in addition to BCs. This analysis demonstrated that FDSS-based hydrologic regions discern process differences across a data-sparse and hydrologically diverse landscape. Process importance rankings from random forest-derived BCs provided model-independent information for making modeling decisions.
Movement and dispersal of migratory fish species is an important life-history characteristics that can be impeded by navigation dams. Although habitat fragmentation may be detrimental to native fish species, it might act as an effective and economical barrier for controlling the spread of invasive species in riverine systems. Various technologies have been proposed as potential fish deterrents at locks and dams to reduce bigheaded carp (i.e., silver carp and bighead carp (Hypophthalmichthys spp.)) range expansion in the Upper Mississippi River (UMR). Lock and Dam (LD) 15 is infrequently at open-river condition (spillway gates completely open; hydraulic head across the dam <0.4 m) and has been identified as a potential location for fish deterrent implementation. We used acoustic telemetry to evaluate paddlefish passage at UMR dams and to evaluate seasonal and diel movement of paddlefish and bigheaded carp relative to environmental conditions and lock operations at LD 15. We observed successful paddlefish passage at all dams, with the highest number of passages occurring at LDs 17 and 16. Paddlefish residency events in the downstream lock approach of LD 15 occurred more frequently and for longer durations than residency events of bigheaded carp. We documented upstream passages completed by two individual paddlefish through the lock chamber at LD 15, and a single bighead carp completed upstream passage through the lock chamber during two separate years of this study. We identified four bigheaded carp and 19 paddlefish that made upstream passages through the spillway gates at LD 15 during this study. The majority of the upstream passages through the spillway gates for both species occurred during open river conditions. When hydraulic head was approximately 1-m or greater, we observed these taxa opt for upstream passage through the lock chamber more often than the dam gates. In years with infrequent open-river condition, a deterrent placed in the downstream lock approach may assist in meeting the management goal of reducing upstream passage of bigheaded carps but could also potentially affect paddlefish residency and passage. Continued study to understand the effects of deterrents on native fish could be beneficial for implementing an integrated bigheaded carp control strategy. Understanding fish behavior at UMR dams is a critical information need for river managers as they evaluate potential tools or technologies to control upstream expansion of bigheaded carp in the UMR.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.13822","usgsCitation":"Turney, D.D., Fritts, A.K., Knights, B.C., Vallazza, J.M., Appel, D., and Lamar, J.T., 2022, Hydrological and lock operation conditions associated with paddlefish and bigheaded carp dam passage on a large and small scale in the Upper Mississippi River (Pools 14–18): PeerJ, v. 10, e13822, 31 p., https://doi.org/10.7717/peerj.13822.","productDescription":"e13822, 31 p.","ipdsId":"IP-135535","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":446958,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13822","text":"Publisher Index Page"},{"id":435747,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CHJ8OG","text":"USGS data release","linkHelpText":"2017-2019 Telemetry data for invasive carp and paddlefish surrounding Lock and Dam 15 in the Upper Mississippi River Basin"},{"id":408536,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.7138671875,\n 40.41767833585549\n ],\n [\n -89.8736572265625,\n 40.41767833585549\n ],\n [\n -89.8736572265625,\n 41.9921602333763\n ],\n [\n -91.7138671875,\n 41.9921602333763\n ],\n [\n -91.7138671875,\n 40.41767833585549\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Turney, Dominique D.","contributorId":298069,"corporation":false,"usgs":false,"family":"Turney","given":"Dominique","email":"","middleInitial":"D.","affiliations":[{"id":49637,"text":"Western Illinois University","active":true,"usgs":false}],"preferred":false,"id":855012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":855013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":855014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vallazza, Jonathan M. 0000-0003-2367-4887 jvallazza@usgs.gov","orcid":"https://orcid.org/0000-0003-2367-4887","contributorId":149362,"corporation":false,"usgs":true,"family":"Vallazza","given":"Jonathan","email":"jvallazza@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":855015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appel, Douglas 0000-0001-8775-1058","orcid":"https://orcid.org/0000-0001-8775-1058","contributorId":268159,"corporation":false,"usgs":true,"family":"Appel","given":"Douglas","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":855016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lamar, James T.","contributorId":298070,"corporation":false,"usgs":false,"family":"Lamar","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":855017,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237270,"text":"70237270 - 2022 - Floodplains and climate change","interactions":[],"lastModifiedDate":"2022-10-06T15:00:23.931708","indexId":"70237270","displayToPublicDate":"2022-08-01T11:35:37","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":12617,"text":"IEP Technical Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"99","chapter":"4","title":"Floodplains and climate change","docAbstract":"Floodplains are landscape features that are periodically inundated by water from adjacent rivers (Opperman et al. 2010). Ecologically, functional floodplains are characterized by three primary elements: connectivity, flow regime, and spatial scale. Water quantity flowing over floodplains can vary greatly. Based on a flood’s effects on the floodplain, three flood categories have been defined: floodplain-activation floods, floodplain-maintenance floods, and floodplainresetting floods (Box 1). Several physical parameters determine the types of ecosystems on floodplains and the species they will support; these include temperature, water depth, water velocity, and hydrologic connectivity (Opperman et al. 2010). Natural ecosystems commonly found on floodplains include annual vegetation, forests, seasonal wetlands, and permanent ponds or wetlands (Whipple et al. 2012). Floodplains provide many valuable ecosystem services: attenuation of flood flows which reduces flood risk, filtration of surface water, recreation, fisheries, agriculture, biodiversity, food availability, and groundwater recharge, which contributes to more-sustained and cooler dry-season flows (Opperman et al. 2010).
--------------------------------------------------------------------------------------------------------------
Box 1
Floodplain-activation flood
A small magnitude flood that occurs relatively frequently and produces characteristic ecological benefits such as food-web productivity and habitat creation for native fish spawning and rearing.
Floodplain-maintenance flood
A higher magnitude flood that, in addition to providing ecological benefits, results in geomorphic changes including bank erosion and deposition on the floodplain.
Floodplain-resetting flood
A very high-magnitude flood that occurs rarely and results in extensive geomorphic changes, such as the scouring of floodplain surfaces and changes in channel location due to avulsion.
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","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Synthesis of data and studies related to the effect of climate change on the ecosystems and biota of the Upper San Francisco Estuary Year 2022","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Interagency Ecological Program","usgsCitation":"Keeley, A., Khanna, S., Kwan, N., Matthias, B.G., Pien, C., and Wulff, M.L., 2022, Floodplains and climate change: IEP Technical Report 99, 51 p.","productDescription":"51 p.","startPage":"188","endPage":"238","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":408038,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407971,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://iep.ca.gov/Publications/Library","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Cosumnes River floodplain, Yolo Bypass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.9317626953125,\n 37.709899354855125\n ],\n [\n -120.3936767578125,\n 37.709899354855125\n ],\n [\n -120.3936767578125,\n 39.21523130910491\n ],\n [\n -121.9317626953125,\n 39.21523130910491\n ],\n [\n -121.9317626953125,\n 37.709899354855125\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Bashevkin, Samuel M.","contributorId":288941,"corporation":false,"usgs":false,"family":"Bashevkin","given":"Samuel M.","affiliations":[{"id":61910,"text":"Delta Science Program, Delta Stewardship Council","active":true,"usgs":false}],"preferred":false,"id":854020,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854021,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Bush, Eva","contributorId":297190,"corporation":false,"usgs":false,"family":"Bush","given":"Eva","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":854022,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Castillo, Gonzalo","contributorId":269408,"corporation":false,"usgs":false,"family":"Castillo","given":"Gonzalo","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife 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Shruti","contributorId":74287,"corporation":false,"usgs":true,"family":"Khanna","given":"Shruti","affiliations":[],"preferred":false,"id":854027,"contributorType":{"id":2,"text":"Editors"},"rank":8},{"text":"Keeley, Annika","contributorId":297191,"corporation":false,"usgs":false,"family":"Keeley","given":"Annika","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":854028,"contributorType":{"id":2,"text":"Editors"},"rank":9},{"text":"Kwan, Nicole","contributorId":297192,"corporation":false,"usgs":false,"family":"Kwan","given":"Nicole","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":854029,"contributorType":{"id":2,"text":"Editors"},"rank":10},{"text":"Lehman, Peggy 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Davis","active":true,"usgs":false}],"preferred":false,"id":854031,"contributorType":{"id":2,"text":"Editors"},"rank":12},{"text":"Malinich, Timothy D.","contributorId":7583,"corporation":false,"usgs":true,"family":"Malinich","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":854032,"contributorType":{"id":2,"text":"Editors"},"rank":13},{"text":"McKenzie, Ryan","contributorId":297366,"corporation":false,"usgs":false,"family":"McKenzie","given":"Ryan","email":"","affiliations":[],"preferred":false,"id":854033,"contributorType":{"id":2,"text":"Editors"},"rank":14},{"text":"Matthias, Bryan G.","contributorId":240763,"corporation":false,"usgs":false,"family":"Matthias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":854034,"contributorType":{"id":2,"text":"Editors"},"rank":15},{"text":"Pien, Catarina","contributorId":297193,"corporation":false,"usgs":false,"family":"Pien","given":"Catarina","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":854035,"contributorType":{"id":2,"text":"Editors"},"rank":16},{"text":"Wulff, Marissa L. 0000-0003-0121-9066 mwulff@usgs.gov","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":1719,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"mwulff@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854036,"contributorType":{"id":2,"text":"Editors"},"rank":17}],"authors":[{"text":"Keeley, Annika","contributorId":297191,"corporation":false,"usgs":false,"family":"Keeley","given":"Annika","email":"","affiliations":[{"id":64315,"text":"Delta Stewardship Council Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":854014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khanna, Shruti","contributorId":74287,"corporation":false,"usgs":true,"family":"Khanna","given":"Shruti","affiliations":[],"preferred":false,"id":854015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwan, Nicole","contributorId":297192,"corporation":false,"usgs":false,"family":"Kwan","given":"Nicole","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":854016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matthias, Bryan G.","contributorId":240763,"corporation":false,"usgs":false,"family":"Matthias","given":"Bryan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":854017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pien, Catarina","contributorId":297193,"corporation":false,"usgs":false,"family":"Pien","given":"Catarina","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":854018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wulff, Marissa L. 0000-0003-0121-9066 mwulff@usgs.gov","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":1719,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"mwulff@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854019,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237283,"text":"70237283 - 2022 - Influence of surface- and ground-water hydrology on riparian tree growth and mortality in the Limitrophe segment of the Colorado River","interactions":[],"lastModifiedDate":"2022-11-29T16:53:40.800191","indexId":"70237283","displayToPublicDate":"2022-08-01T10:03:14","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":12975,"text":"Biennial Report","active":true,"publicationSubtype":{"id":4}},"chapter":"4","title":"Influence of surface- and ground-water hydrology on riparian tree growth and mortality in the Limitrophe segment of the Colorado River","docAbstract":"Branch sections and cores of cottonwood and willow trees were collected from two sites in the Limitrophe. Tree-ring analyses may reveal the relationships among tree growth, streamflow and groundwater.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Minute 323, first biennial report 2018, of monitoring of environmental flows in the Limitrophe and delta of the Colorado River","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"International Boundary & Water Commission","usgsCitation":"Shafroth, P.B., 2022, Influence of surface- and ground-water hydrology on riparian tree growth and mortality in the Limitrophe segment of the Colorado River: Biennial Report, 3 p.","productDescription":"3 p.","startPage":"36","endPage":"38","ipdsId":"IP-112545","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":409796,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":408016,"type":{"id":15,"text":"Index Page"},"url":"https://www.ibwc.gov/EMD/Minute323workgroup.html","linkFileType":{"id":5,"text":"html"}}],"country":"Mexico, United States","state":"Arizona, Baja California","otherGeospatial":"Limitrophe of the Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.81399184252159,\n 32.7413427567745\n ],\n [\n -114.81399184252159,\n 32.47087175054729\n ],\n [\n -114.6129630599179,\n 32.47087175054729\n ],\n [\n -114.6129630599179,\n 32.7413427567745\n ],\n [\n -114.81399184252159,\n 32.7413427567745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":297380,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":853975,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237091,"text":"70237091 - 2022 - Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018","interactions":[],"lastModifiedDate":"2026-01-12T16:42:05.865776","indexId":"70237091","displayToPublicDate":"2022-08-01T09:21:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018","docAbstract":"This remote sensing section is based on Nagler et al. (in preparation for the journal Hydrological Processes) and is a summary of the USGS preliminary findings to date.
This report documents the changes in green foliage density (greenness) as measured by satellite vegetation index (VI) data and corresponding evapotranspiration (ET) in the riparian corridor of the Colorado River delta associated with the Minutes 319 and 323 environmental water deliveries using time-series data from 2013 through 2018. The report focuses on what happened only within the riparian corridor’s seven reaches since the 2014 flows, and despite being a continuation of measuring greenness and ET after the 2017 end of Minute 319, this study continued the tracking of these two variables, greenness and ET, in these original riparian corridor focal areas. Two spatial scales are used here: (1) Landsat satellite imagery at 30 m pixels and (2) the EOS-1 satellite sensor the Moderate Resolution Imaging Spectrometer (MODIS) with a resolution of 250 m pixels. The focal period includes 2013 (prepulse flow) and the years 2014-2018, with a focus on imagery collected from the Summer growing seasons 2014 through 2018 (one-year, pre-pulse and several post-pulse years, respectively).
This report re-creates the 2013-2017 Landsat-based results from Jarchow et al. (2017a, b) by using the same region of interest (ROI). The report now provides revised and re-created results using all new imagery acquisition and processing techniques, as well as extraction code, created by the Vegetation Index and Phenology (VIP) Lab of the Biosystems Engineering Department of the University of Arizona (UofA). In 2018, methods employed by the VIP lab (and not ArcGIS) were used. ArcGIS was only used in the newly processed data to display the final difference maps. The entire spatial tile data from NASA was downloaded and processed at the VIP Lab using satellite imagery at two resolutions: 250 m MODIS and 30 m Landsat using three sensors, Landsat 5, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI), with added scenes for each year based on new clear atmosphere requirements. The VIP lab clipped the river boundary and seven riparian reaches from the previously existing ROI used in Jarchow et al. (2017 a, b) for the analyses done under Minute 319. The NASA image datasets for this riparian corridor ROI in seven reaches were re-processed to produce additional vegetation index (VI) information for years 2013 to 2018 for this report. At the same time, the report acquired and processed imagery from 2000- 2018 (data outside the scope of this report and data not shown here). The additional VIs (NDVI, scaled NDVI, EVI, EVI2) were analyzed so that new assessments of greenness and ET could be produced from the imagery datasets following methods in Nagler et al. (2013). These VI choices were based on previous performance comparisons between biophysical ground-based data and radiometric satellite-based data collected from this riparian ecosystem (Nagler et al., 2001) as well as performance related to ET estimation (Nagler et al., 2005a, b) and current advancements in VIs such as EVI2.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Minute 323: Colorado River limitrophe and delta environmental flows monitoring interim report for 2018","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"International Boundary and Water Commission United States and Mexico","usgsCitation":"Nagler, P.L., Barreto-Munoz, A., Jarchow, C., and Didan, K., 2022, Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018, 10 p.","productDescription":"10 p.","startPage":"39","endPage":"48","ipdsId":"IP-114755","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":407594,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.17517089843749,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 31.587894464070395\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":853314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":853316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241555,"text":"70241555 - 2022 - Editorial: Fire regimes in desert ecosystems: Drivers, impacts and changes","interactions":[],"lastModifiedDate":"2023-03-23T13:59:45.335315","indexId":"70241555","displayToPublicDate":"2022-08-01T08:56:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Editorial: Fire regimes in desert ecosystems: Drivers, impacts and changes","docAbstract":"Although not commonly associated with fire, many desert ecosystems across the globe do occasionally burn, and there is evidence that fire incidences are increasing, leading to altered fire regimes in this biome. The increased prevalence of megafires (wildfires >10,000 ha in size and typically damaging) in most global biomes is linked to climate change, although those occurring in deserts have received far less attention, from both a research and policy perspective, than that of forested ecosystems (Linley et al., 2022). Understanding the drivers of desert fires, from climate to landscape patterns of hydrology and soil, and how these may be changing in the face of anthropogenic pressures, such as invasive species, livestock grazing, and global climate change, is imperative. This Research Topic has published nine papers addressing these drivers, how they have changed, and their impacts on desert biodiversity.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.968031","usgsCitation":"van Etten, E.J., Brooks, M.L., Greenville, A.C., and Wardel, G.M., 2022, Editorial: Fire regimes in desert ecosystems: Drivers, impacts and changes: Frontiers in Ecology and Evolution, v. 10, 968031, 3 p., https://doi.org/10.3389/fevo.2022.968031.","productDescription":"968031, 3 p.","ipdsId":"IP-143374","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446971,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.968031","text":"Publisher Index Page"},{"id":414611,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"van Etten, Eddie J. B.","contributorId":303343,"corporation":false,"usgs":false,"family":"van Etten","given":"Eddie","email":"","middleInitial":"J. B.","affiliations":[{"id":65770,"text":"Edith Cowan University, Australia","active":true,"usgs":false}],"preferred":false,"id":867294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greenville, Aaron C.","contributorId":300416,"corporation":false,"usgs":false,"family":"Greenville","given":"Aaron","email":"","middleInitial":"C.","affiliations":[{"id":65131,"text":"Desert Ecology Research Group, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia.","active":true,"usgs":false}],"preferred":false,"id":867296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wardel, Glenda M.","contributorId":303344,"corporation":false,"usgs":false,"family":"Wardel","given":"Glenda","email":"","middleInitial":"M.","affiliations":[{"id":33318,"text":"University of Sydney, Australia","active":true,"usgs":false}],"preferred":false,"id":867297,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232598,"text":"ofr20221030 - 2022 - Mapping structural control through analysis of land-surface deformation for the Rialto-Colton groundwater subbasin, San Bernardino County, California, 1992–2010","interactions":[],"lastModifiedDate":"2026-03-27T20:06:42.204886","indexId":"ofr20221030","displayToPublicDate":"2022-07-29T10:58:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1030","displayTitle":"Mapping Structural Control Through Analysis of Land-Surface Deformation for the Rialto-Colton Groundwater Subbasin, San Bernardino County, California, 1992–2010","title":"Mapping structural control through analysis of land-surface deformation for the Rialto-Colton groundwater subbasin, San Bernardino County, California, 1992–2010","docAbstract":"The locations of many faults in and near the Rialto-Colton groundwater subbasin are not precisely known because the spatial density of existing lithologic and hydrologic data used to infer the locations of faults can be sparse. The U.S. Geological Survey, in cooperation with the San Bernardino Valley Municipal Water District, analyzed structural control of groundwater flow in and near the Rialto-Colton groundwater subbasin using Interferometric Synthetic Aperture Radar (InSAR) methods. Faults commonly are barriers to groundwater flow, and the high spatial resolution of InSAR imagery can be used to infer the locations of buried faults where groundwater pumping occurs. InSAR results have revealed three areas in and near the Rialto-Colton groundwater subbasin where buried faults are interpreted as groundwater-flow barriers: the northwestern area about 3 miles northwest of the City of Rialto, the San Jacinto fault area west of the City of San Bernardino, and the southeastern area about 2 miles southeast of the City of Colton. The InSAR results were combined with knowledge gained from previous studies to better define the location and extent of faults acting as groundwater-flow barriers. New data about faults acting as groundwater-flow barriers can be incorporated into future conceptual and hydrologic models of the Rialto-Colton groundwater subbasin and provide water managers information to help effectively manage groundwater resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221030","collaboration":"Prepared in cooperation with the San Bernardino Valley Municipal Water District","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., 2022, Mapping structural control through analysis of land-surface deformation for the Rialto-Colton groundwater subbasin, San Bernardino County, California, 1992–2010: U.S. Geological Survey Open-File Report 2022–1030, 11 p., https://doi.org/10.3133/ofr20221030.","productDescription":"Report: vi, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-084965","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":501769,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113347.htm","linkFileType":{"id":5,"text":"html"}},{"id":403230,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1030/images"},{"id":403228,"rank":1,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.xml"},{"id":403229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1030/ofr20221030.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":403232,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"Data release","description":"U.S. Geological Survey, 2014, Web interface: U.S. Geological Survey National Water Information System web page, accessed June 11, 2014, at https://doi.org/10.5066/F7P55KJN.","linkHelpText":"Web interface: U.S. Geological Survey National Water Information System web page"},{"id":404520,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1030/covrthb.jpg"},{"id":404546,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221030/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1030"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Rialto-Colton groundwater subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.51319885253905,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.01851844336969\n ],\n [\n -117.2138214111328,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.19362958613085\n ],\n [\n -117.51319885253905,\n 34.01851844336969\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey (USGS) evaluated nitrate and orthophosphate concentrations in groundwater for temporal trends (monotonic and step trends) for the middle Snake River region (Cassia, Gooding, Jerome, Lincoln, Minidoka, and Twin Falls Counties) in south-central Idaho using the Regional Kendall test (monotonic trends) and the Wilcoxon signed rank test (step trends). The study evaluated two trend periods: 2000–09 and 2010–19/20. The study area was divided into six hydrogeologic zones (HZs) that had similar geologic and hydrologic characteristics and that correlated with county boundaries where possible. Two well networks sampled by the USGS National Water Quality Program within the HZs were also evaluated.
The northern Gooding County HZ had statistically significant increasing nitrate concentration trends for both the monotonic and step trends in the early trend period, while the Cassia and Jerome/Southern Gooding County HZs only had one of the statistical tests with statistically significant increasing nitrate concentrations. The Minidoka County HZ had conflicting results between the two statistical tests for the early time period with a statistically significant increasing monotonic trend in nitrate concentration and a statistically significant decreasing step trend. The differing results between these two statistical tests indicates the significance of concentration data during the middle of the time period. Both the Lincoln and Twin Falls County HZs did not have statistically significant trends for either test during either time period as well as the Northern Gooding County HZ for the latter time period. The Minidoka County HZ had statistically significant nitrate trends for both tests in the latter time period along with one of the trend tests for the Cassia and Jerome/Southern Gooding County HZ. Most of the nitrate concentration trend rates are low from 0.01 to 0.12 milligram per liter per year (mg/L/year) with the northern Gooding County HZ having the highest trend rate during the early time period of 0.28 mg/L/year for the step trend and 0.55 mg/L/year for the monotonic trend.
All the HZs and both well networks had statistically significant increasing orthophosphate-concentrations trends in groundwater for the early time period except for the Lincoln County HZ and the step-trend for the Minidoka County HZ. Orthophosphate concentration trend rates for the early period were low, ranging from 0.001 to 0.015 mg/L/year. Only two HZs and the well networks had enough orthophosphate concentration data available in the latter time period to do statistical analysis. The two HZs (Minidoka and Southern Gooding/Jerome County) both have decreasing orthophosphate concentration trends, with only the monotonic trend for the Southern Gooding/Jerome County HZ being statistically significant at 90 percent with a rate of −0.001 mg/L/year.
Groundwater levels in two well networks in the eastern Snake River Plain aquifer were also evaluated for trends (monotonic and step), with both networks having statistically significant declining groundwater levels for the 1993–2009 trend period. The latter trend period (2010–20) had statistically significant declining groundwater levels for the A&B well network and statistically significant increasing groundwater levels for the Jerome/Gooding well network, which is downgradient from an aquifer recharge area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225060","collaboration":"Prepared in cooperation with the Idaho Department of Environmental Quality and the Middle Snake Regional Water Resource Commission","usgsCitation":"Skinner, K.D., 2022, Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2022–5060, 18 p., https://doi.org/10.3133/sir20225060.","productDescription":"vii, 18 p.","onlineOnly":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":404369,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5060"},{"id":404371,"rank":5,"type":{"id":31,"text":"Publication 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Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM) was developed through an interagency effort between the U.S. Geological Survey and the Bureau of Reclamation to provide a tool for analyzing the hydrologic system response to the historical evolution of water use and potential changes in water supplies and demands in the Hatch Valley (also known as Rincon Valley in the study area) and Mesilla Basin, New Mexico and Texas, United States, and northern Chihuahua, Mexico. Reclamation operates the Rio Grande Project (RGP) to store and deliver surface water for irrigation and municipal use within the study area and in the El Paso Valley south of the El Paso Narrows.
Biases in the RGTIHM’s simulation of streamflow and aquifer storage depletion and the availability of new estimates of historical agricultural consumptive use in the study area initiated an update and recalibration of the RGTIHM. In addition to the new estimates of historical agricultural consumptive use, updates were made to more accurately represent the natural system and included adjustments to the initial groundwater levels; streamflow rating tables; Rio Grande, canal, and drain streambed elevations; tributary streambed elevations; surface-water inflows and diversions; RGP surface-water deliveries and canal waste; on-farm efficiency; the routing of surface-water runoff within the MODFLOW Farm Process; and general head boundaries used to simulate interbasin groundwater flow. Model settings, including the assignment of hydraulic conductivity and storage properties to model layers and the MODFLOW solver package, were adjusted to improve numerical stability, and the model was recalibrated to better simulate the natural system. The updated and recalibrated RGTIHM demonstrates a robust ability to simulate the spatially and temporally variable measurements, estimates, or reports of hydraulic head, surface-water flows, agricultural pumping, RGP surface-water deliveries and canal waste, and decadal aquifer storage changes, with improvements over the previous version of the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225045","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Ritchie, A.B., Galanter, A.E., Flickinger, A.K., Shephard, Z.M., and Ferguson, I.M., 2022, Update and recalibration of the Rio Grande Transboundary Integrated Hydrologic Model, New Mexico and Texas, United States, and northern Chihuahua, Mexico: U.S. Geological Survey Scientific Investigations Report 2022–5045, 28 p., https://doi.org/10.3133/sir20225045.","productDescription":"Report: vi, 28 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132740","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":404022,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PLDXV","text":"USGS data release","linkHelpText":"MODFLOW One-Water Hydrologic Flow Model (MF-OWHM) used to simulate conjunctive use in the Hatch Valley and Mesilla Basin, New Mexico and Texas, United States, and northern Chihuahua, Mexico"},{"id":404024,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5045/sir20225045.xml"},{"id":404023,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5045/images"},{"id":404021,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5045/sir20225045.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5045"},{"id":404020,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5045/coverthb.jpg"},{"id":502400,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113311.htm","linkFileType":{"id":5,"text":"html"}}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108,\n 31.5\n ],\n [\n -106,\n 31.5\n ],\n [\n -106,\n 33.25\n ],\n [\n -108,\n 33.25\n ],\n [\n -108,\n 31.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Many areas are experiencing increasing stream temperatures due to climate change, and some are experiencing reduced summer stream flows and water availability. Because dam building and pond formation by beaver can increase water storage, stream cooling, and riparian ecosystem resilience, beaver have been proposed as a potential climate adaption tool. Despite the large number of studies that have evaluated how beaver activity may affect hydrology and water temperature, few experimental studies have quantified these outcomes following beaver relocation. We evaluated changes in temperature and water storage following the relocation of 69 beaver into 13 headwater stream reaches of the Skykomish River watershed within the Snohomish River basin, Washington, USA. We evaluated how beaver dams affected surface and groundwater storage and stream temperature. Successful relocations created 243 m3 of surface water storage per 100 m of stream in the first year following relocation. Dams raised water table elevations by up to 0.33 m and stored approximately 2.4 times as much groundwater as surface water per relocation reach. Stream reaches downstream of dams exhibited an average decrease of 2.3°C during summer base-flow conditions. We also assessed how dam age, condition, maintenance frequency, and pond morphology influenced stream temperature at naturally colonized wetland complexes. Our findings demonstrate that dam building can increase water storage and reduce stream temperatures in the first year following successful beaver relocation. Fluvial and floodplain morphology of candidate reaches for relocation is an important consideration because it determines the type and magnitude of response. Relocation to reaches with existing small, abandoned ponds may address thermal criteria by conversion from warming to cooling reaches, whereas relocation within large, abandoned complexes or vacant habitat may result in greater water storage. Although beaver relocation can be an effective climate adaptation strategy to retain more stable hydrologic regimes and water quality in our study area, there appear to be regionally specific environmental and geomorphic factors that influence how beaver affect water storage and temperature. More research is needed to investigate how and why these regional differences affect water storage and stream temperature response in beaver-influenced systems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4168","usgsCitation":"Dittbrenner, B.J., Schilling, J.W., Torgersen, C.E., and Lawler, J.J., 2022, Relocated beaver can increase water storage and decrease stream temperature in headwater streams: Ecosphere, v. 13, no. 7, e4168, 17 p., https://doi.org/10.1002/ecs2.4168.","productDescription":"e4168, 17 p.","ipdsId":"IP-134665","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4168","text":"Publisher Index Page"},{"id":404214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skykomish River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23800659179686,\n 47.6737103919566\n ],\n [\n -121.15,\n 47.6737103919566\n ],\n [\n -121.15,\n 48.026672195436014\n ],\n [\n -122.23800659179686,\n 48.026672195436014\n ],\n [\n -122.23800659179686,\n 47.6737103919566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Dittbrenner, Benjamin J.","contributorId":202890,"corporation":false,"usgs":false,"family":"Dittbrenner","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":847172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schilling, Jason W.","contributorId":202892,"corporation":false,"usgs":false,"family":"Schilling","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":36547,"text":"Tulalip Tribes Natural Resources","active":true,"usgs":false}],"preferred":false,"id":847173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":847175,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.
Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02222","usgsCitation":"Stevens, B., Conway, C.J., Luke, K., Weldon, A., Hand, C., Schwarzer, A., Smith, F., Watson, C., and Watts, B.D., 2022, Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems: Global Ecology and Conservation, v. 38, e02222, 12 p., https://doi.org/10.1016/j.gecco.2022.e02222.","productDescription":"e02222, 12 p.","ipdsId":"IP-136723","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02222","text":"Publisher Index Page"},{"id":430022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":903426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luke, Kirsten","contributorId":338653,"corporation":false,"usgs":false,"family":"Luke","given":"Kirsten","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weldon, Aimee","contributorId":338654,"corporation":false,"usgs":false,"family":"Weldon","given":"Aimee","email":"","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hand, Christy","contributorId":338655,"corporation":false,"usgs":false,"family":"Hand","given":"Christy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarzer, Amy","contributorId":338656,"corporation":false,"usgs":false,"family":"Schwarzer","given":"Amy","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":903431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Fletcher","contributorId":338657,"corporation":false,"usgs":false,"family":"Smith","given":"Fletcher","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Watson, Craig","contributorId":338659,"corporation":false,"usgs":false,"family":"Watson","given":"Craig","email":"","affiliations":[{"id":81184,"text":"Atlanti Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watts, Bryan D.","contributorId":338660,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":37406,"text":"College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":903434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256670,"text":"70256670 - 2022 - Trout responses to stocking rates and river discharge within a southeast U.S. hydropeaking tailwater","interactions":[],"lastModifiedDate":"2024-08-30T14:09:12.548651","indexId":"70256670","displayToPublicDate":"2022-07-12T09:02:53","publicationYear":"2022","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":"Trout responses to stocking rates and river discharge within a southeast U.S. hydropeaking tailwater","docAbstract":"Freshwater fish populations often exist in systems characterized by novel ecological processes resulting from human alteration. Salmonid populations embedded within coldwater sections of warmwater rivers are spatially constrained by habitat availability. Tailwater fish contend with fluctuating river discharges and density-dependent processes associated with fish stocking and exploitation. Salmonid populations sustained through stocking versus natural reproduction may respond differently to changes in hydrologic patterns (e.g., hydropeaking) as well as declines in fish abundance. We assessed differences between stocked (Rainbow Trout Oncorhynchus mykiss) and naturalized (Brown Trout Salmo trutta) trout populations in Greers Ferry tailwater, Arkansas, regarding (1) spatial and temporal patterns of mean length, electrofishing catch rates, and relative condition following reduced number of stocked Rainbow Trout and (2) evidence that hydrologic characteristics and fish stocking intensity influenced relative condition. A 56% reduction in Rainbow Trout stocking did not result in systemwide change in mean length or relative abundance for Rainbow Trout or Brown Trout over the 16-year study period. Hydrologic variability, where river discharge spanned both reduced and elevated levels, positively influenced condition of both Rainbow Trout and Brown Trout. Assessment of survival of stocked Rainbow Trout may aid in further refining the timing and amount of stocking needed to sustain the population at a desired abundance. Further, assessing the influence of stocking fewer but perhaps larger (in terms of mean length) fish to meet management goals may be warranted. The persistent differences in relative abundance among river sections can inform management actions directed at Brown Trout, including harvest regulations. Such regulations may aid in reaching desired management goals, including abundance and mean length targets not observed after reduced stocking.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10779","usgsCitation":"Spurgeon, J.J., Kaiser, J., Graham, C., and Lochmann, S., 2022, Trout responses to stocking rates and river discharge within a southeast U.S. hydropeaking tailwater: North American Journal of Fisheries Management, v. 42, no. 4, p. 926-938, https://doi.org/10.1002/nafm.10779.","productDescription":"13 p.","startPage":"926","endPage":"938","ipdsId":"IP-135390","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10779","text":"Publisher Index Page"},{"id":433360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Greers Ferry tailwater","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.12227720560406,\n 35.56794921986193\n ],\n [\n -92.12227720560406,\n 35.37950970740478\n ],\n [\n -91.72539494699616,\n 35.37950970740478\n ],\n [\n -91.72539494699616,\n 35.56794921986193\n ],\n [\n -92.12227720560406,\n 35.56794921986193\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":304259,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Joseph","contributorId":341541,"corporation":false,"usgs":false,"family":"Kaiser","given":"Joseph","email":"","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Christy","contributorId":341542,"corporation":false,"usgs":false,"family":"Graham","given":"Christy","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":908585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lochmann, Steve","contributorId":341543,"corporation":false,"usgs":false,"family":"Lochmann","given":"Steve","affiliations":[{"id":81661,"text":"University of Arkansas at Pine Bluff","active":true,"usgs":false}],"preferred":false,"id":908586,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254544,"text":"70254544 - 2022 - A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan","interactions":[],"lastModifiedDate":"2024-05-31T14:47:50.401231","indexId":"70254544","displayToPublicDate":"2022-07-08T09:41:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan","docAbstract":"From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56∘ N, 30–100∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).
","language":"English","publisher":"Copernicus","doi":"10.5194/essd-14-3115-2022","usgsCitation":"McNally, A., Jacob, J., Arsenault, K., Slinski, K., Sarmiento, D., Hoell, A., Pervez, S., Rowland, J., Budde, M., Kumar, S., Peters-Lidard, C., and Verdin, J., 2022, A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan: Earth System Science Data, v. 14, no. 7, p. 3115-3135, https://doi.org/10.5194/essd-14-3115-2022.","productDescription":"21 p.","startPage":"3115","endPage":"3135","ipdsId":"IP-134002","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-3115-2022","text":"Publisher Index Page"},{"id":429405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Afghanistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[61.21082,35.65007],[62.23065,35.27066],[62.98466,35.40404],[63.19354,35.85717],[63.9829,36.00796],[64.54648,36.31207],[64.74611,37.11182],[65.58895,37.30522],[65.74563,37.66116],[66.21738,37.39379],[66.51861,37.36278],[67.07578,37.35614],[67.83,37.14499],[68.13556,37.02312],[68.85945,37.34434],[69.19627,37.15114],[69.51879,37.609],[70.11658,37.58822],[70.27057,37.73516],[70.3763,38.1384],[70.80682,38.48628],[71.34813,38.25891],[71.2394,37.95327],[71.54192,37.90577],[71.44869,37.06564],[71.84464,36.73817],[72.19304,36.94829],[72.63689,37.04756],[73.26006,37.49526],[73.9487,37.42157],[74.98,37.41999],[75.15803,37.13303],[74.57589,37.02084],[74.06755,36.83618],[72.92002,36.72001],[71.84629,36.50994],[71.26235,36.07439],[71.49877,35.65056],[71.61308,35.1532],[71.11502,34.73313],[71.15677,34.34891],[70.8818,33.98886],[69.93054,34.02012],[70.32359,33.35853],[69.68715,33.1055],[69.26252,32.50194],[69.31776,31.90141],[68.92668,31.62019],[68.55693,31.71331],[67.79269,31.58293],[67.68339,31.30315],[66.93889,31.30491],[66.38146,30.7389],[66.34647,29.88794],[65.04686,29.47218],[64.35042,29.56003],[64.148,29.34082],[63.55026,29.46833],[62.54986,29.31857],[60.87425,29.82924],[61.78122,30.73585],[61.69931,31.37951],[60.94194,31.54807],[60.86365,32.18292],[60.53608,32.98127],[60.9637,33.52883],[60.52843,33.67645],[60.80319,34.4041],[61.21082,35.65007]]]},\"properties\":{\"name\":\"Afghanistan\"}}]}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"McNally, 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Kimberly","contributorId":337030,"corporation":false,"usgs":false,"family":"Slinski","given":"Kimberly","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sarmiento, Daniel","contributorId":337031,"corporation":false,"usgs":false,"family":"Sarmiento","given":"Daniel","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901825,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoell, Andrew","contributorId":337032,"corporation":false,"usgs":false,"family":"Hoell","given":"Andrew","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":901826,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pervez, Shahriar 0000-0003-3417-1871","orcid":"https://orcid.org/0000-0003-3417-1871","contributorId":337035,"corporation":false,"usgs":false,"family":"Pervez","given":"Shahriar","affiliations":[{"id":80954,"text":"AFDS contractor to USGS","active":true,"usgs":false}],"preferred":false,"id":901827,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":145846,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","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":901828,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Budde, Michael 0000-0002-9098-2751 mbudde@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":166756,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","email":"mbudde@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":901829,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kumar, Sujay","contributorId":337039,"corporation":false,"usgs":false,"family":"Kumar","given":"Sujay","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901830,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Peters-Lidard, Christa","contributorId":337041,"corporation":false,"usgs":false,"family":"Peters-Lidard","given":"Christa","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":901831,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Verdin, James","contributorId":337042,"corporation":false,"usgs":false,"family":"Verdin","given":"James","affiliations":[{"id":48664,"text":"USAID","active":true,"usgs":false}],"preferred":false,"id":901832,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70232597,"text":"ofr20221041 - 2022 - Geomorphic survey of North Fork Eagle Creek, New Mexico, 2019","interactions":[],"lastModifiedDate":"2026-03-27T20:14:24.5315","indexId":"ofr20221041","displayToPublicDate":"2022-07-08T06:52:35","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1041","displayTitle":"Geomorphic Survey of North Fork Eagle Creek, New Mexico, 2019","title":"Geomorphic survey of North Fork Eagle Creek, New Mexico, 2019","docAbstract":"The 2012 Little Bear Fire resulted in substantial loss of vegetation in the Eagle Creek Basin, south-central New Mexico, which has been expected to cause a variety of hydrologic responses that could influence geomorphic change to North Fork Eagle Creek. To monitor geomorphic change, surveys of a downstream study reach of North Fork Eagle Creek were conducted in 2017, 2018, and 2019 by the U.S. Geological Survey in cooperation with the Village of Ruidoso, N. Mex. The study included surveys of select cross sections, woody debris accumulations, and pools found in the channel of the study reach. During 2017–19, high-flow events resulting from both monsoonal rainfall and snowmelt runoff occurred in the study reach, and the events appeared to have caused some minor localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017, 2018, and 2019 survey results.
Comparisons of the cross-section survey results indicated that minor geomorphic changes had occurred in 4 of the 14 cross sections surveyed from 2017 to 2019. These geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections. During the 2019 survey, 164 distinct accumulations of woody debris and 228 pools were identified in the study reach. Of the woody debris accumulations identified during the 2019 survey, 67 were certain to have also been present during the 2018 survey, and 21 were certain to have also been present during all three surveys (2017–19), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys. Most woody debris accumulations identified in 2019 did not appear to have substantially influenced geomorphic change in the locations where they were found but may have driven local geomorphic changes.
Because the study began 5 years after the 2012 Little Bear Fire and the geomorphic scope of the study has so far been limited, it cannot be said that the changes observed between the 2017 and 2019 surveys are representative of a pattern of geomorphic change following the Little Bear Fire. Once geomorphic changes identified during the 2017 through 2019 surveys can be compared with results from the remaining planned geomorphic surveys, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221041","collaboration":"Prepared in cooperation with Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., and Chavarria, S.B., 2022, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2019: U.S. Geological Survey Open-File Report 2022–1041, 36 p., https://doi.org/10.3133/ofr20221041.","productDescription":"Report: v, 36 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-123645","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":403220,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97ALYNZ","text":"USGS data release","linkHelpText":"Data supporting the 2019 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":501773,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113257.htm","linkFileType":{"id":5,"text":"html"}},{"id":403221,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":403219,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1041/images"},{"id":403218,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1041/ofr20221041.XML"},{"id":403215,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1041/coverthb.jpg"},{"id":403216,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1041/ofr20221041.pdf","text":"Report","size":"2.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1041"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5,\n 33.0\n ],\n [\n -105.1,\n 33.0\n ],\n [\n -105.1,\n 33.4\n ],\n [\n -105.5,\n 33.4\n ],\n [\n -105.5,\n 33.0\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Urban forests are recognized as a nature-based solution for stormwater management. This study assessed the underlying processes and extent of runoff reduction due to street trees with a paired-catchment experiment conducted in two sewersheds of Fond du Lac, Wisconsin. Computer models are flexible, fast, and low-cost options to generalize and assess the hydrologic processes determined in field studies. A state-of-the-art, public-domain model, which explicitly simulates urban tree hydrology, i-Tree Hydro, was used to simulate the paired-catchment experiment, and results from field observations and simulation predictions were compared to assess model validity and suitability as per conditions in the broader Great Lakes basin. Model parameters were aligned with observed conditions using automatic and manual calibration. Model performance metrics were used to quantify the weekly performance of calibration and to validate predictions. Those calibration metrics differed substantially between the two periods simulated, but most calibration metrics remained positive, indicating the model was not fitting only the period used for calibration. Predicted avoided runoff for a five-month leaf-on period was 64 L/m2 of canopy, 4 % lower than the field-estimated avoided runoff of 66 L/m2 of canopy. Interception was the most directly comparable process between the model and field observations. Based on 5 storms sampled, field estimation of precipitation intercepted and retained on trees averaged 63 % and ranged from 22 % to 81 %, while model estimation averaged 61 % and ranged from 36 % to 99 %. This model was able to fit predictions to observed catchment discharge but required extensive manual calibration to do so. The i-Tree Hydro model predicted avoided runoff comparable with the field study and earlier assessments. Additional field studies in similar settings are needed to confirm findings and improve transferability to other tree species and environmental settings.
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The hurricane traversed the island from southeast to northwest and produced recorded 48-hour rainfall totals of up to 30.01 inches. Estimates of the human death toll range from 2,975 to 4,645, possibly more.
The U.S. Geological Survey (USGS) hydrologic monitoring network sustained substantial wind and flood damage during the hurricane. Eighty-five of the 300 hydrologic monitoring stations operating in Puerto Rico and the U.S. Virgin Islands prior to the passage of Hurricane Maria were destroyed or damaged. During the weeks and months after the hurricane, USGS field crews in Puerto Rico prioritized repair of the hydrologic monitoring network and collected hydrologic information to characterize the magnitude of observed peak streamflows at 20 streamgage and to develop new theoretical stage-streamflow relations for 58 streamgages where stream channels were substantially altered; the theoretical stage-streamflow relations were used to estimate Hurricane Maria peak streamflows for 39 of those sites. As part of a pilot program, USGS field crews installed continuous slope-area monitoring equipment at two remote streamgages to automate the collection of high-streamflow stage data.
Hurricane Maria peak streamflows and rankings were determined for 73 USGS streamgages in Puerto Rico. New rank 1 period-of-record peak streamflows occurred at 28 sites, rank 2 period-of-record peak streamflows occurred at 17 sites, and rank 3 period-of-record peak streamflows occurred at 9 sites; period-of-record peak streamflows at the remaining 19 sites either ranked from 4th to 20th or were not ranked. Annual exceedance probabilities for 53 unregulated peak streamflows ranged from greater than 50.0 percent (recurrence interval of less than 2 years) to 0.3 percent (recurrence interval of 333 years), with the majority (28 of 53) in the range of 10.0–2.1 percent (recurrence intervals of 10–48 years).
A comparison of period-of-record ranks for the largest flood events that have occurred in Puerto Rico since the 1960s indicated that Hurricane Maria produced more record peak streamflows than either Hurricane Hortense in 1996 or Hurricane Georges in 1998. Limited pre-1960s hydrologic data preclude quantitative comparison with earlier storms.
As part of this study, a maximum peak-streamflow envelope curve for Puerto Rico was developed using historical peak-streamflow information available through 2017. Other post-Hurricane Maria USGS activities summarized in this report include (1) Global Navigation Satellite System surveys at all stations in the USGS hydrologic monitoring network, used to tie the network to the Puerto Rico Vertical Datum of 2002; and (2) telemetered monitoring of the Lago Guajataca Dam in northwestern Puerto Rico, which was damaged and at risk of failure from October to December 2017.
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