Tidal freshwater forests are usually located at or above the level of mean high water. Some Louisiana coastal forests are below mean high water, especially bald cypress (Taxodium distichum (L.) Rich.) forests because flooding has increased due to the combined effects of global sea level rise and local subsidence. In addition, constructed channels from the coast inland act as conduits for saltwater. As a result, saltwater intrusion affects the productivity of Louisiana’s coastal bald cypress forests. To study the long-term effects of hydrology and salinity on the health of these systems, we fitted dendrometer bands on selected trees to record basal area increment as a measure of growth in permanent forest productivity plots established within six bald cypress stands. Three stands were in freshwater sites with low salinity rooting zone groundwater (0.1–1.3 ppt), while the other three had higher salinity rooting zone groundwater (0.2–4.9 ppt). Water level was logged continuously, and salinity was measured monthly to quarterly on the surface and in groundwater wells. Higher groundwater salinity levels were related to decreased bald cypress radial growth, while higher freshwater flooding increased radial growth. With these data, coastal managers can model rates of bald cypress forest change as a function of salinity and flooding.
","language":"English","publisher":"MDPI","doi":"10.3390/f15071258","usgsCitation":"Day, R., From, A., Johnson, D., and Krauss, K., 2024, Interactive effects of salinity and hydrology on radial growth of bald cypress (Taxodium distichum (L.) Rich.) in coastal Louisiana, USA: Forests, v. 15, no. 7, 1258, 16 p., https://doi.org/10.3390/f15071258.","productDescription":"1258, 16 p.","ipdsId":"IP-102177","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439267,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3390/f15071258","text":"Publisher Index Page"},{"id":431608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.19521095490502,\n 31.239460576333215\n ],\n [\n -94.19521095490502,\n 28.592602619005845\n ],\n [\n -88.87782814240524,\n 28.592602619005845\n ],\n [\n -88.87782814240524,\n 31.239460576333215\n ],\n [\n -94.19521095490502,\n 31.239460576333215\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":221895,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":907218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"From, Andrew 0000-0002-6543-2627","orcid":"https://orcid.org/0000-0002-6543-2627","contributorId":221935,"corporation":false,"usgs":true,"family":"From","given":"Andrew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":907219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Darren 0000-0002-0502-6045","orcid":"https://orcid.org/0000-0002-0502-6045","contributorId":203921,"corporation":false,"usgs":true,"family":"Johnson","given":"Darren","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":907220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":907221,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255986,"text":"tm1D12 - 2024 - Guidelines for the use of automatic samplers in collecting surface-water quality and sediment data","interactions":[],"lastModifiedDate":"2024-07-18T11:28:38.435545","indexId":"tm1D12","displayToPublicDate":"2024-07-18T06:50:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1-D12","displayTitle":"Guidelines for the Use of Automatic Samplers in Collecting Surface-Water Quality and Sediment Data","title":"Guidelines for the use of automatic samplers in collecting surface-water quality and sediment data","docAbstract":"The importance of fluvial systems in the transport of sediment, dissolved and suspended contaminants, nutrients, and bacteria through the environment is well established. The U.S. Environmental Protection Agency (EPA) identifies sediment as the single most widespread water contaminant affecting the beneficial uses of the Nation’s rivers and streams. The evaluation of water-quality as it relates to agriculture, urbanization, highway and residential construction, mining, industrial and human wastes, and other activities requires an extensive data and sample-collection effort. This is especially the case when studying urbanized river basins, where during hydrologic events, concentration of suspended sediment and contaminants can vary rapidly and over large ranges. Where synoptic studies of watersheds are called for, sampling may be needed at many sites throughout the basin; a complicated and difficult task in some settings. Automatic pumping samplers (autosamplers) are one method for conducting intensive time-varying sampling throughout watersheds.
This report presents guidelines for the use of autosamplers for collecting surface-water samples by the U.S. Geological Survey. An autosampler is an automatic, pump-based sampler that collects a prescribed volume of water from streams, lakes, reservoirs, storm drains, or other bodies of water after receiving a command from an internal or external control unit. It deposits this sample into a specified container for later analysis of physical, chemical, or biological constituents. This report provides a general background on types of autosamplers and how they work; guidance for designing, selecting, installing, servicing, and calibrating autosamplers; guidance on standardized operating procedures, and guidance on quality-assurance and quality-control efforts when using an autosampler.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm1D12","usgsCitation":"Wilson, T.P., Miller, C.V., and Lechner, E.A., 2024, Guidelines for the use of automatic samplers in collecting surface-water quality and sediment data: U.S. Geological Survey Techniques and Methods 1–D12, 89 p., https://doi.org/10.3133/tm1D12","productDescription":"ix, 89 p.","numberOfPages":"89","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131202","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":430984,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/01/d12/images/"},{"id":430983,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/01/d12/tm1d12.XML","linkFileType":{"id":8,"text":"xml"},"description":"TM 1-D12 XML"},{"id":430982,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm1D12/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"TM 1-D12 HTML"},{"id":430980,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/01/d12/coverthb.jpg"},{"id":430981,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/01/d12/tm1d12.pdf","text":"Report","size":"19.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 1-D12 PDF"}],"contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike Suite 110
Lawrenceville, New Jersey 08648
We recently developed a dynamically coupled hydrological-ocean modeling system that provides seamless coverage across the land-ocean continuum during hurricane-induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF-Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR036455","usgsCitation":"Bao, D., Xue, Z.G., and Warner, J.C., 2024, Quantifying compound and nonlinear effects of hurricane-induced flooding using a dynamically coupled hydrological-ocean model: Water Resources Research, v. 60, no. 7, e2023WR036455, 21 p., https://doi.org/10.1029/2023WR036455.","productDescription":"e2023WR036455, 21 p.","ipdsId":"IP-162739","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466982,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr036455","text":"Publisher Index Page"},{"id":465288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Bao, Daoyang","contributorId":294534,"corporation":false,"usgs":false,"family":"Bao","given":"Daoyang","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":921432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xue, Z. George","contributorId":347342,"corporation":false,"usgs":false,"family":"Xue","given":"Z.","email":"","middleInitial":"George","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":921433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":921434,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255933,"text":"sir20245059 - 2024 - Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa","interactions":[],"lastModifiedDate":"2026-02-03T19:47:34.689565","indexId":"sir20245059","displayToPublicDate":"2024-07-12T12:20:43","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5059","displayTitle":"Groundwater Flow Model for the Des Moines River Alluvial Aquifer near Des Moines, Iowa","title":"Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa","docAbstract":"Des Moines Water Works (DMWW) is a regional municipal water utility that provides residential and commercial water resources to about 600,000 customers in Des Moines, Iowa, and surrounding municipalities in central Iowa. DMWW has identified a need for increased water supply and is exploring the potential for expanding groundwater production capabilities in the Des Moines River alluvial aquifer, where it operates two radial collector wells (RCWs). The U.S. Geological Survey, in cooperation with DMWW, completed a study of the Des Moines River alluvial aquifer and interactions of the RCWs with the aquifer; no previously published model has included the existing well locations, which is the focus of this model. A conceptual and numerical groundwater flow model have been developed to characterize the Des Moines River alluvial aquifer under existing conditions, to simulate water levels observed in the RCWs, and to provide publicly accessible hydrologic data and research that advance understanding of the regional hydrologic system and can potentially be used in the future to evaluate groundwater production scenarios. Model performance was assessed by comparing observed and simulated groundwater levels that included water level elevations, water level changes, water level inequality observations, surface water streamflow, and change in surface water volume from upstream to downstream. Water table elevation in the aquifer layers is on average slightly overestimated with average absolute value error less than 1.5 meters at both RCWs and less than 2.5 meters for all observation wells in the alluvial aquifer layers. The model also accurately simulated water tables greater than the RCW design minimum (a water level threshold at which RCW pumping is reduced) in all timesteps for which water level observation data existed. Water table elevation error was higher in other model layers that were not the focus of the study, and the model did not accurately match streamflow targets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245059","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"Bristow, E.L., and Davis, K.W., 2024, Groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa: U.S. Geological Survey Scientific Investigations Report 2024–5059, 47 p., https://doi.org/10.3133/sir20245059.","productDescription":"Report: ix, 47 p.; 3 Data Releases; 1 Dataset","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154246","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":430905,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5059/sir20245059.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5059"},{"id":430904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5059/coverthb.jpg"},{"id":430906,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5059/sir20245059.XML"},{"id":430907,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5059/images/"},{"id":430908,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245059/full"},{"id":430909,"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":430910,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZDDVY","text":"USGS data release","linkHelpText":"MODFLOW 6 groundwater flow model for the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":430911,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B9AVKJ","text":"USGS data release","linkHelpText":"Geophysical data collected in the Des Moines River, Beaver Creek, and the Des Moines River floodplain, Des Moines, Iowa, 2018"},{"id":430912,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":499480,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117123.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Iowa","city":"Des Moines","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.75578446475713,\n 41.70743368403336\n ],\n [\n -93.75578446475713,\n 41.53433869670215\n ],\n [\n -93.54349781702975,\n 41.53433869670215\n ],\n [\n -93.54349781702975,\n 41.70743368403336\n ],\n [\n -93.75578446475713,\n 41.70743368403336\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The effects of timber harvest practices and climate change have altered forest ecosystems in southeast Alaska. However, quantification of patterns and trends in stream habitats associated with these forests is limited owing to a paucity of data available in remote watersheds. Here, we analyzed a 30-year dataset from southeast Alaska's Tongass National Forest to understand how these factors shape stream habitats. First, we examined differences between broad management classes (i.e., harvested and non-harvested) that have been used to guide stream channel restoration goals. Second, we assessed associations between intrinsic landscape characteristics, watershed management, and timber harvest legacies on aquatic habitat metrics. And third, we examined trends in stream habitat metrics over the duration of the dataset to anticipate future management challenges for these systems. Small effect sizes for some harvest-related predictors suggest that some stream habitat metrics, such as pool densities, are less responsive than others, and management practices such as protecting riparian buffers as well as post-harvest restoration may help conserve fish habitats. Large wood densities increased with time since harvest at sites harvested >50 years ago, indicating that multiple decades of post-harvest forest regrowth may contribute large wood to streams (possibly alder), but that it is not enough time for old-growth trees (e.g., spruce, Picea, or hemlock, Tsuga,), classified as key wood, to develop and be delivered to streams. The declining trend in key wood (i.e., the largest size class of wood) regardless of management history may reflect that pre-harvest legacy old-growth trees are declining along streams, with low replacement. The introduction of wood to maintain complex stream habitats may fill this gap until riparian stands again contribute structural key wood to streams. Trend analyses indicate an increasing spatial extent of undercut banks that may also be influenced by shifting hydrologic regimes under climate change.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0301723","usgsCitation":"Moore, M.J., Flitcroft, R., Tucker, E., Prussian, K.K., and Claeson, S.M., 2024, Same streams in a different forest? Investigations of forest harvest legacies and future trajectories across 30 years of stream habitat monitoring on the Tongass National Forest, Alaska: PLoS ONE, v. 19, no. 7, e0301723, 28 p., https://doi.org/10.1371/journal.pone.0301723.","productDescription":"e0301723, 28 p.","ipdsId":"IP-146418","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439287,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1371/journal.pone.0301723","text":"Publisher Index Page"},{"id":433632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Tongass National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.79598611853271,\n 35.785590501974994\n ],\n [\n -120.79598611853271,\n 35.758103908925094\n ],\n [\n -120.7544854283608,\n 35.758103908925094\n ],\n [\n -120.7544854283608,\n 35.785590501974994\n ],\n [\n -120.79598611853271,\n 35.785590501974994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -136.02998750758678,\n 57.464783614185876\n ],\n [\n -136.02998750758678,\n 55.846134928392786\n ],\n [\n -133.47293766914498,\n 55.846134928392786\n ],\n [\n -133.47293766914498,\n 57.464783614185876\n ],\n [\n -136.02998750758678,\n 57.464783614185876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Michael J. 0000-0002-5495-7049","orcid":"https://orcid.org/0000-0002-5495-7049","contributorId":304258,"corporation":false,"usgs":true,"family":"Moore","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flitcroft, R.","contributorId":342974,"corporation":false,"usgs":false,"family":"Flitcroft","given":"R.","email":"","affiliations":[{"id":81962,"text":"Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":910553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, E.","contributorId":342975,"corporation":false,"usgs":false,"family":"Tucker","given":"E.","email":"","affiliations":[{"id":81965,"text":"Tongass National Forest","active":true,"usgs":false}],"preferred":false,"id":910554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prussian, K. K.","contributorId":204860,"corporation":false,"usgs":false,"family":"Prussian","given":"K.","email":"","middleInitial":"K.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":910555,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Claeson, S. M.","contributorId":342976,"corporation":false,"usgs":false,"family":"Claeson","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":81962,"text":"Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":910556,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255837,"text":"sir20245053 - 2024 - Assessment of nutrient load estimation approaches for small urban streams in Durham, North Carolina","interactions":[],"lastModifiedDate":"2026-02-03T19:37:42.115649","indexId":"sir20245053","displayToPublicDate":"2024-07-08T16:42:39","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5053","displayTitle":"Assessment of Nutrient Load Estimation Approaches for Small Urban Streams in Durham, North Carolina","title":"Assessment of nutrient load estimation approaches for small urban streams in Durham, North Carolina","docAbstract":"This cooperative study between the City of Durham Public Works Department, Stormwater Division and U.S. Geological Survey evaluated whether alternate monitoring strategies that incorporated samples collected across an increased range of streamflows would improve nutrient load estimates for Ellerbe and Sandy Creeks, two small, highly urbanized streams in the City of Durham, North Carolina. Water-quality and streamflow data collected between January 2009 and December 2020 were used to develop instream nutrient-load models using the U.S. Geological Survey R-LOADEST program. This study compared model results from two sampling scenarios: routine monthly (fixed frequency) sampling combined with targeted high-streamflow sampling (scenario A), and fixed frequency sampling only (scenario B).
Calibration diagnostic results were used to select the final, or most optimal, models. Most final models included seasonality terms to compensate for intra-annual variability in the data. Storm-runoff samples provided better definition at higher streamflows and improved the overall concentration versus flow relations for all constituents, except nitrate + nitrite. Uncertainties in the nutrient load estimates were lower and less variable for the scenario A tests compared to the scenario B tests.
Five time steps representing 12-, 9-, 7-, 6-, and 5-year subsets of the overall dataset were used to examine the effect of prediction period length on the computed loads and uncertainties. In focusing on the scenario A results, nutrient loads tended to be higher for the shorter time steps. These shorter time steps also produced higher errors, or uncertainty, in the load estimates compared to longer time steps. Evaluations of annual nutrient loads during 2016–20 indicated that the most consistent load estimates and tightest confidence intervals were obtained for longer 12- and 9-year time steps. Estimated loads were more variable and uncertain when based on the shorter 6- and 5-year time steps. The degree of uncertainty (standard error of prediction) in the nutrient load estimation results was influenced by sampling approach, calibration time step, and hydrologic characteristics during the model period of interest.
Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, suite 500
Norcross, GA 30093
Contact Us- USGS Publications Warehouse
","tableOfContents":"Machine learning (ML) models are increasingly popular in environmental and hydrologic modeling, but they typically contain uncertainties resulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approach that combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisy data, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distinguish between outliers and erroneous data. The new algorithm recognizes that there is no unique way to split the training data into noisy and clean data, and thus produces an ensemble of plausible splits. The algorithm successfully detected noisy data in synthetic benchmark problems with varying complexity and a real-world public supply water withdrawal dataset. The algorithm is generic and flexible, making it suitable for application across a broad range of hydrologic and environmental disciplines.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106133","usgsCitation":"Alzraiee, A.H., and Niswonger, R.G., 2024, A probabilistic approach to training machine learning models using noisy data: Environmental Modelling & Software, v. 179, 106133, 15 p., https://doi.org/10.1016/j.envsoft.2024.106133.","productDescription":"106133, 15 p.","ipdsId":"IP-151600","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106133","text":"Publisher Index Page"},{"id":433694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912926,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912927,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70261225,"text":"70261225 - 2024 - Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios","interactions":[],"lastModifiedDate":"2024-12-02T16:04:23.669174","indexId":"70261225","displayToPublicDate":"2024-07-08T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios","docAbstract":"Northern peatlands are a globally significant source of methane (CH4), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH4 production in these systems will be key to predicting annual emissions changes, with stable carbon isotopes (δ13C-CH4) being a powerful tool for characterizing these drivers. Given that δ13C signatures of CH4 are used in top-down atmospheric inversion models to partition sources, our ability to model CH4 production pathways and associated δ13C-CH4 signatures in peatland types impacted by a changing climate is critical. We sought to characterize the role of environmental conditions, including both hydrologic and vegetation patterns associated with permafrost thaw, on δ13C-CH4 signatures from a diverse set of high-latitude peatlands. We measured porewater and emitted CH4 stable isotopes, pH, and vegetation composition from five boreal-Arctic peatlands. Porewater δ13C-CH4 was strongly associated with peatland type, with δ13C enriched values obtained from more minerotrophic fens (-61.2 ± 9.1‰) compared to permafrost-free bogs (-74.1 ± 9.4‰) and raised permafrost bogs (-81.6 ± 11.5‰). Variation in porewater δ13C-CH4 was best explained by sedge cover, CH4 concentration, and the interactive effect of peatland type and pH (r2 = 0.50, p < 0.001). Emitted δ13C-CH4 varied greatly but was positively correlated with porewater δ13C-CH4, suggesting that porewater data can be used to predict changing emissions signatures from these systems. We calculated a weighted mean mixed atmospheric CH4 signature for northern peatlands of -65.3 ± 7‰ and show that this signature is more sensitive to landscape drying (4 to 10 % depletion in δ13C) than wetting (1.5 to 5% enrichment in δ13C) under permafrost thaw scenarios. Our results suggest northern peatland δ13C-CH4 signatures are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007837","usgsCitation":"Kuhn, M.A., Varner, R.K., McCalley, C.K., Perryman, C.R., Aurela, M., Burke, S.A., Chanton, J., Crill, P., DelGreco, J., Deng, J., Heffernan, L., Herrick, C., Hodgkins, S.B., Jones, C.P., Juutinen, S., Kane, E., Lamit, L.J., Larmola, T., Lilleskov, E., Olefeldt, D., Palace, M.W., Rich, V.I., Schulze, C., Shorter, J.H., Sullivan, F., Sonnentag, O., Turetsky, M., and Waldrop, M., 2024, Controls on stable methane isotope signatures in northern peatlands and potential shifts in signatures under permafrost thaw scenarios: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007837, 17 p., https://doi.org/10.1029/2023JG007837.","productDescription":"e2023JG007837, 17 p.","ipdsId":"IP-162425","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466987,"rank":2,"type":{"id":40,"text":"Open 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Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US.
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McKenzie","contributorId":361359,"corporation":false,"usgs":false,"family":"Skiles","given":"S.","middleInitial":"McKenzie","affiliations":[{"id":86245,"text":"Department of Geography, University of Utah, Salt Lake City, UT, USA","active":true,"usgs":false}],"preferred":false,"id":948664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Naple, Patrick","contributorId":361360,"corporation":false,"usgs":false,"family":"Naple","given":"Patrick","affiliations":[{"id":86245,"text":"Department of Geography, University of Utah, Salt Lake City, UT, USA","active":true,"usgs":false}],"preferred":false,"id":948665,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Chaney, Nathaniel W.","contributorId":361361,"corporation":false,"usgs":false,"family":"Chaney","given":"Nathaniel","middleInitial":"W.","affiliations":[{"id":86246,"text":"Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA","active":true,"usgs":false}],"preferred":false,"id":948666,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Cai, Jiaxuan","contributorId":361362,"corporation":false,"usgs":false,"family":"Cai","given":"Jiaxuan","affiliations":[{"id":86246,"text":"Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA","active":true,"usgs":false}],"preferred":false,"id":948667,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70272709,"text":"70272709 - 2024 - Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape","interactions":[],"lastModifiedDate":"2025-12-08T14:19:02.218112","indexId":"70272709","displayToPublicDate":"2024-07-01T08:57:37","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape","docAbstract":"Permafrost holds more than twice the amount of carbon currently in the atmosphere, but this large carbon reservoir is vulnerable to thaw and erosion under a rapidly changing Arctic climate. Convective storms are becoming increasingly common during Arctic summers and can amplify runoff and erosion. These extreme events, in concert with active layer deepening, may accelerate carbon loss from the Arctic landscape. However, we lack measurements of carbon fluxes during these events.\nRivers are sensitive to physical, chemical, and hydrological perturbations, and thus are excellent systems for studying landscape responses to thunderstorms. We present observations from the Canning River, Alaska, which drains the northern Brooks Range and flows across a continuous permafrost landscape to the Beaufort Sea. During summer 2022 and 2023 field campaigns, we opportunistically monitored river discharge, sediment, and organic carbon fluxes during several thunderstorms. During one notable storm, river discharge nearly doubled from ~130 m3/s to ~240 m3/s, suspended sediment flux increased 70-fold, and the particulate organic carbon (POC) flux increased 90-fold relative to non-storm conditions. Taken together, the river exported ~16 metric tons of POC over one hour of this sustained event, not including the additional flux of woody debris. Furthermore, the dissolved organic carbon (DOC) flux nearly doubled. Although these thunderstorm-driven fluxes are short-lived (hours to days), they play an outsized role in exporting organic carbon from Arctic rivers. Understanding how these extreme events impact river water, sediment, and carbon dynamics will help predict how Arctic climate change will modify the global carbon cycle.","conferenceTitle":"12th International Conference on Permafrost","conferenceDate":"June 16-20, 2024","conferenceLocation":"Whitehorse, Yukon","language":"English","publisher":"International Conference on Permafrost","doi":"10.52381/ICOP2024.104.1","usgsCitation":"Repasch, M., Arcuri, J., Overeem, I., Anderson, S.P., Anderson, R.G., and Koch, J.C., 2024, Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape, 12th International Conference on Permafrost, Whitehorse, Yukon, June 16-20, 2024, p. 341-348, https://doi.org/10.52381/ICOP2024.104.1.","productDescription":"8 p.","startPage":"341","endPage":"348","ipdsId":"IP-157679","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":497137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Canning River, North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.75,\n 70.25\n ],\n [\n -146.5,\n 70.25\n ],\n [\n -146.5,\n 68.5\n ],\n [\n -144.75,\n 68.5\n ],\n [\n -144.75,\n 70.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Repasch, Marisa 0000-0003-2636-9896","orcid":"https://orcid.org/0000-0003-2636-9896","contributorId":334190,"corporation":false,"usgs":false,"family":"Repasch","given":"Marisa","email":"","affiliations":[],"preferred":false,"id":951399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arcuri, Josie","contributorId":363269,"corporation":false,"usgs":false,"family":"Arcuri","given":"Josie","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overeem, Irina","contributorId":197487,"corporation":false,"usgs":false,"family":"Overeem","given":"Irina","email":"","affiliations":[],"preferred":false,"id":951401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Suzanne P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":951402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Robert G.","contributorId":197569,"corporation":false,"usgs":false,"family":"Anderson","given":"Robert","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":951403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":951404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70258442,"text":"70258442 - 2024 - Application of a workflow to determine the feasibility of using simulated streamflow for estimation of streamflow frequency statistics","interactions":[],"lastModifiedDate":"2024-09-17T11:40:02.349995","indexId":"70258442","displayToPublicDate":"2024-07-01T06:35:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Application of a workflow to determine the feasibility of using simulated streamflow for estimation of streamflow frequency statistics","docAbstract":"The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (MTBS, 2020).
Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m3/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity (
Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage.
The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2024.100180","usgsCitation":"Fulton, J.W., Hall, N.G., Hempel, L.A., Gourley, J., Henneberg, M.F., Kohn, M.S., Farmer, W., Asquith, W.H., Wasielewski, D., Stecklein, A.S., Mommandi, A., and Khan, A., 2024, Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins: Journal of Hydrology X, v. 24, 100180, 17 p., https://doi.org/10.1016/j.hydroa.2024.100180.","productDescription":"100180, 17 p.","ipdsId":"IP-112029","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":439317,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2024.100180","text":"Publisher Index Page"},{"id":430892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Waldo Canyon basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105,\n 39\n ],\n [\n -105,\n 38.8333\n ],\n [\n -104.8167,\n 38.8333\n ],\n [\n -104.8167,\n 39\n ],\n [\n -105,\n 39\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Nicholas Graff 0000-0002-7331-8947","orcid":"https://orcid.org/0000-0002-7331-8947","contributorId":315497,"corporation":false,"usgs":true,"family":"Hall","given":"Nicholas","email":"","middleInitial":"Graff","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gourley, J.J.","contributorId":340018,"corporation":false,"usgs":false,"family":"Gourley","given":"J.J.","email":"","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":905916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henneberg, Mark F. 0000-0002-6991-1211 mfhenneb@usgs.gov","orcid":"https://orcid.org/0000-0002-6991-1211","contributorId":187481,"corporation":false,"usgs":true,"family":"Henneberg","given":"Mark","email":"mfhenneb@usgs.gov","middleInitial":"F.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":905919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wasielewski, Daniel","contributorId":340019,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Daniel","email":"","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":905921,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stecklein, Andrew S.","contributorId":340020,"corporation":false,"usgs":false,"family":"Stecklein","given":"Andrew","email":"","middleInitial":"S.","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905922,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mommandi, Amanullah","contributorId":340021,"corporation":false,"usgs":false,"family":"Mommandi","given":"Amanullah","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Khan, Aziz","contributorId":340022,"corporation":false,"usgs":false,"family":"Khan","given":"Aziz","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905924,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70255599,"text":"sir20245056 - 2024 - Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","interactions":[],"lastModifiedDate":"2026-02-03T19:43:57.736197","indexId":"sir20245056","displayToPublicDate":"2024-06-25T15:43:18","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5056","displayTitle":"Two-Dimensional Hydraulic Model for the Chain of Lakes on the Fox River near McHenry, Illinois","title":"Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","docAbstract":"Forecasts of flows entering and leaving the Chain of Lakes on the Fox River in northeastern Illinois are critical information to water-resource managers operating the Stratton Dam at McHenry, Illinois. These managers determine the optimal operation of the Stratton Dam at McHenry, Ill., to manage Chain of Lakes pool levels and to help mitigate flooding in the Chain of Lakes system. In 2020, the U.S. Geological Survey (USGS) and the Illinois Department of Natural Resources–Office of Water Resources (IDNR–OWR) began a cooperative study to develop a system to enable engineers and planners to simulate and communicate water-surface elevations and flows and to proactively prepare for runoff events forecasted for the Chain of Lakes. The hydraulic model described in this report may be helpful to the IDNR–OWR for optimizing the operation of the Stratton Dam and includes the implementation of three newly installed torque-tube crest gates that became operational in 2020.
The hydraulic model for the Chain of Lakes was developed using the Hydrologic Engineering Center–River Analysis System program (version 6.5). The hydraulic model was used to simulate water-surface elevations and flows through the 18.5-mile Chain of Lakes system to 1.7 miles downstream from the Stratton Dam. Five USGS streamgages within the study area were used as reference points for model calibration and initial water-surface elevations for beginning a simulation. The hydraulic model was calibrated to three runoff events that incorporated the design specifications and observed gate operations of the Stratton Dam; furthermore, the hydraulic model simulated a validation event and a substantial flooding event during July 2017. The July 2017 event predated the torque-tube crest gate installation but nevertheless tested the performance of the model for such a substantial event. The model simulation results were a good fit to observed records at USGS streamgages with simulated peak water-surface elevations within −0.36–0.15 foot of observed events. The hydraulic model was then implemented into a forecast workflow that streamlines implementation of model inputs and documents the model outputs tailored to IDNR–OWS Stratton Dam operations and interpretations of simulated water-surface elevations and flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245056","collaboration":"Prepared in cooperation with the Illinois Department of Natural Resources–Office of Water Resources","usgsCitation":"Cigrand, C.V., and Ament, M.R., 2024, Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois: U.S. Geological Survey Scientific Investigations Report 2024–5056, 20 p., https://doi.org/10.3133/sir20245056.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137180","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499478,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117099.htm","linkFileType":{"id":5,"text":"html"}},{"id":430505,"rank":7,"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":430504,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P16H3TDH","text":"USGS data release","linkHelpText":"Archive of the hydraulic model used in the two-dimensional simulation of the Chain of Lakes on the Fox River near McHenry, Illinois:"},{"id":430503,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245056/full"},{"id":430502,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5056/images/"},{"id":430501,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.XML"},{"id":430500,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.pdf","text":"Report","size":"3.5 MB","description":"SIR 2024–5056"},{"id":430499,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5056/coverthb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Fox River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.3061687403806,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.29838954847517\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
We present a hydroclimate synthesis of the southern Great Basin over the last two glacial-interglacial cycles focused on paleolakes in Death Valley (core DV93-1), Searles Valley (core SLAPP-SRLS17), Owens Valley (core OL92), and the Devils Hole cave. There is close agreement between the occurrence of lakes in Death Valley and the height of the water table in the Devils Hole (50 km east of Death Valley) during the last 200 kyr. Death Valley and Devils Hole have adjacent, partly overlapping, drainage areas and most likely did over the last 200 kyr. When the water table in the Devils Hole was above the threshold level of ∼5 m higher than the modern, permanent lakes existed in Death Valley. At water table elevations less than 5 m above the modern, ephemeral lakes, saline pans, and mudflats occurred in Death Valley. The close temporal agreement between inferred paleoenvironments from the sediments in the Death Valley core and the paleowater table elevation in Devils Hole suggests a common forcing and provides insight into climate variability in the southwestern United States over the last 200 kyr. Owens Valley and Searles Valley, which derived inflow waters from the Sierra Nevada via the Owens River, contain paleohydrologic records which match those from Death Valley and the Devils Hole in terms of timing and direction of water availability over the last 200 kyr, indicating a similar paleohydrologic history for the entire southern Great Basin region. Near the end of Marine Oxygen Isotope Stage 6 (MIS 6), 140 ka - 130 ka, Lake Manly in Death Valley became shallow and hypersaline, and ultimately dried up at 127.1 ka ±4.3 ka. The transition from glacial to interglacial vegetation, which involved the loss of Juniperus pollen and an increase in Quercus (oak) pollen, occurred in Death Valley core DV93-1 at 131.3 ka ±4.0 ka. Following the glacial to interglacial pollen shift, a large alkaline lake formed in Death Valley. Similar conditions (freshwater, high productivity, and a mixed, deeply oxygenated water column indicated by biomarkers) existed in Searles Lake between 135.3 +2.7/-2.9 ka and 130.1+2.7/-2.6 ka, also following the juniper-oak pollen transition. Sr isotopes in calcite and sulfate minerals (gypsum, glauberite, thenardite), and the rare occurrence of the sodium carbonate mineral northupite with a low 87Sr/86Sr ratio in core DV93-1, together with organic geochemical proxies from Searles core SLAPP-SRLS17, all suggest that at this time, late MIS 6 Lake Manly in Death Valley received alkaline water via spillover from Searles Valley into Death Valley through Panamint Valley. The hydrologic connection between Searles Valley, Panamint Valley, and Death Valley at Termination II (130 ka) is documented here for this system of pluvial lakes for the first time. The Devils Hole water table decreased to +6.5 m at 140.8 ka ±3.2 ka, rose briefly to +8 m at 137.6 ka ±0.5 ka, and then dropped 8 m by 120.36 ka ±0.45 ka, when it reached an elevation similar to the modern. The pluvial lakes in Death Valley and Searles Valley may have coincided with the rise of the Devils Hole water table at ∼137.6 ka ±0.5 ka years ago, although the age models for core DV93-1 and core SLAPP-SLRS17 during the end of MIS 6 carry large uncertainties.
Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916–2016) site occurrence records (n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable (n = 77), groundwater (n = 67), groundwater flashy (n = 175), perennial runoff (n = 141), runoff flashy (n = 255), and intermittent (n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.
Exposure to per- and polyfluoroalkyl substances (PFAS) has been associated with toxicity in wildlife and negative health effects in humans. Decades of fire training activity at Joint Base Cape Cod (MA, USA) incorporated the use of aqueous film-forming foam (AFFF), which resulted in long-term PFAS contamination of sediments, groundwater, and hydrologically connected surface waters. To explore the bioconcentration potential of PFAS in complex environmental mixtures, a mobile laboratory was established to evaluate the bioconcentration of PFAS from AFFF-impacted groundwater by flow-through design. Fathead minnows (n = 24) were exposed to PFAS in groundwater over a 21-day period and tissue-specific PFAS burdens in liver, kidney, and gonad were derived at three different time points. The ∑PFAS concentrations in groundwater increased from approximately 10,000 ng/L at day 1 to 36,000 ng/L at day 21. The relative abundance of PFAS in liver, kidney, and gonad shifted temporally from majority perfluoroalkyl sulfonamides (FASAs) to perfluoroalkyl sulfonates (PFSAs). By day 21, mean ∑PFAS concentrations in tissues displayed a predominance in the order of liver > kidney > gonad. Generally, bioconcentration factors (BCFs) for FASAs, perfluoroalkyl carboxylates (PFCAs), and fluorotelomer sulfonates (FTS) increased with degree of fluorinated carbon chain length, but this was not evident for PFSAs. Perfluorooctane sulfonamide (FOSA) displayed the highest mean BCF (8700 L/kg) in day 21 kidney. Suspect screening results revealed the presence of several perfluoroalkyl sulfinate and FASA compounds present in groundwater and in liver for which pseudo-bioconcentration factors are also reported. The bioconcentration observed for precursor compounds and PFSA derivatives detected suggests alternative pathways for terminal PFAS exposure in aquatic wildlife and humans.
Understanding temporal trends in low streamflows is important for water management and ecosystems. This work focuses on trends in the occurrence rate of extreme low-flow events (5- to 100-year return periods) for pooled groups of stations. We use data from 1,184 minimally altered catchments in Europe, North and South America, and Australia to discern historical climate-driven trends in extreme low flows (1976–2015 and 1946–2015). The understanding of low streamflows is complicated by different hydrological regimes in cold, transitional, and warm regions. We use a novel classification to define low-flow regimes using air temperature and monthly low-flow frequency. Trends in the annual occurrence rate of extreme low-flow events (proportion of pooled stations each year) were assessed for each regime. Most regimes on multiple continents did not have significant (p < 0.05) trends in the occurrence rate of extreme low streamflows from 1976 to 2015; however, occurrence rates for the cold-season low-flow regime in North America were found to be significantly decreasing for low return-period events. In contrast, there were statistically significant increases for this period in warm regions of NA which were associated with the variation in the Pacific Decadal Oscillation. Significant decreases in extreme low-flow occurrence rates were dominant from 1946 to 2015 in Europe and NA for both cold- and warm-season low-flow regimes; there were also some non-significant trends. The difference in the results between the shorter (40-year) and longer (70-year) records and between low-flow regimes highlights the complexities of low-flow response to changing climatic conditions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR034326","usgsCitation":"Hodgkins, G.A., Renard, B., Whitfield, P.H., Laaha, G., Stahl, K., Hannaford, J., Burn, D.H., Westra, S., Fleig, A.K., Lopes, W.T., Murphy, C., Mediero, L., and Hanel, M., 2024, Climate driven trends in historical extreme low streamflows on four continents: Water Resources Research, v. 60, no. 6, e2022WR034326, 25 p., https://doi.org/10.1029/2022WR034326.","productDescription":"e2022WR034326, 25 p.","ipdsId":"IP-147345","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":466994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr034326","text":"Publisher Index Page"},{"id":463765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":918097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":918098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitfield, Paul H.","contributorId":198041,"corporation":false,"usgs":false,"family":"Whitfield","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":918099,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laaha, Gregor","contributorId":335609,"corporation":false,"usgs":false,"family":"Laaha","given":"Gregor","email":"","affiliations":[{"id":80445,"text":"University of Natural Resources and Life Sciences, Austria","active":true,"usgs":false}],"preferred":false,"id":918100,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stahl, Kerstin","contributorId":198044,"corporation":false,"usgs":false,"family":"Stahl","given":"Kerstin","email":"","affiliations":[],"preferred":false,"id":918101,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hannaford, Jamie","contributorId":198043,"corporation":false,"usgs":false,"family":"Hannaford","given":"Jamie","email":"","affiliations":[],"preferred":false,"id":918102,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burn, Donald H.","contributorId":198042,"corporation":false,"usgs":false,"family":"Burn","given":"Donald","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":918103,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Westra, Seth","contributorId":335610,"corporation":false,"usgs":false,"family":"Westra","given":"Seth","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":918104,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fleig, Anne K.","contributorId":198045,"corporation":false,"usgs":false,"family":"Fleig","given":"Anne","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":918105,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lopes, Walsczon Terllizzie Araujo","contributorId":335611,"corporation":false,"usgs":false,"family":"Lopes","given":"Walsczon","email":"","middleInitial":"Terllizzie Araujo","affiliations":[{"id":80446,"text":"National Water and Sanitation Agency, Brazil","active":true,"usgs":false}],"preferred":false,"id":918106,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Murphy, Conor","contributorId":198049,"corporation":false,"usgs":false,"family":"Murphy","given":"Conor","email":"","affiliations":[],"preferred":false,"id":918108,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mediero, Luis","contributorId":198047,"corporation":false,"usgs":false,"family":"Mediero","given":"Luis","email":"","affiliations":[],"preferred":false,"id":918109,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hanel, Martin","contributorId":346109,"corporation":false,"usgs":false,"family":"Hanel","given":"Martin","email":"","affiliations":[],"preferred":false,"id":918115,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70256062,"text":"70256062 - 2024 - Solute export patterns across the contiguous USA","interactions":[],"lastModifiedDate":"2024-07-18T14:43:05.641206","indexId":"70256062","displayToPublicDate":"2024-06-17T09:38:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Solute export patterns across the contiguous USA","docAbstract":"Understanding controls on solute export to streams is challenging because heterogeneous catchments can respond uniquely to drivers of environmental change. To understand general solute export patterns, we used a large-scale inductive approach to evaluate concentration–discharge (C–Q) metrics across catchments spanning a broad range of catchment attributes and hydroclimatic drivers. We leveraged paired C–Q data for 11 solutes from CAMELS-Chem, a database built upon an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous USA. Because C–Q relationships with Q thresholds reflect a shift in solute export dynamics and are poorly characterized across solutes and diverse catchments, we analysed C–Q relationships using Bayesian segmented regression to quantify Q thresholds in the C–Q relationship. Threshold responses were rare, representing only 12% of C–Q relationships, 56% of which occurred for solutes predominantly sourced from bedrock. Further, solutes were dominated by one or two C–Q patterns that reflected vertical solute–source distributions. Specifically, solutes predominantly sourced from bedrock had diluting C–Q responses in 43%–70% of catchments, and solutes predominantly sourced from soils had more enrichment responses in 35%–51% of catchments. We also linked C–Q relationships to catchment and hydroclimatic attributes to understand controls on export patterns. The relationships were generally weak despite the diversity of solutes and attribute types considered. However, catchment and hydroclimatic attributes in the central USA typically drove the most divergent export behaviour for solutes. Further, we illustrate how our inductive approach generated new hypotheses that can be tested at discrete, representative catchments using deductive approaches to better understand the processes underlying solute export patterns. Finally, given these long-term C–Q relationships are from minimally disturbed catchments, our findings can be used as benchmarks for change in more disturbed catchments.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15197","usgsCitation":"Kincaid, D.W., Underwood, K.L., Hamshaw, S.D., Li, L., Seybold, E.C., Stewart, B., Rizzo, D.M., Ul Haq, I., and Perdrial, J.N., 2024, Solute export patterns across the contiguous USA: Hydrological Processes, v. 38, no. 6, e15197, 17 p., https://doi.org/10.1002/hyp.15197.","productDescription":"e15197, 17 p.","ipdsId":"IP-155135","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":487496,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15197","text":"Publisher Index Page"},{"id":431219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n 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M.","contributorId":171679,"corporation":false,"usgs":false,"family":"Rizzo","given":"Donna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":906562,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ul Haq, Ijaz","contributorId":340203,"corporation":false,"usgs":false,"family":"Ul Haq","given":"Ijaz","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":906563,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Perdrial, Julia N.","contributorId":177340,"corporation":false,"usgs":false,"family":"Perdrial","given":"Julia","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":906564,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","interactions":[{"subject":{"id":70255277,"text":"sir20235064B - 2024 - Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","indexId":"sir20235064B","publicationYear":"2024","noYear":false,"chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020"},"predicate":"IS_PART_OF","object":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"id":1}],"isPartOf":{"id":70251152,"text":"sir20235064 - 2024 - Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","indexId":"sir20235064","publicationYear":"2024","noYear":false,"title":"Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin"},"lastModifiedDate":"2024-06-17T22:21:15.873668","indexId":"sir20235064B","displayToPublicDate":"2024-06-17T07:11:12","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5064","chapter":"B","displayTitle":"Peak Streamflow Trends in Illinois and Their Relation to Changes in Climate, Water Years 1921–2020","title":"Peak streamflow trends in Illinois and their relation to changes in climate, water years 1921–2020","docAbstract":"This report characterizes changes in peak streamflow in Illinois and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-flow frequency analysis in Illinois. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; 30 years 1991–2020) for trends, change points, and other statistical properties indicative of changing conditions. Median peak streamflows generally exhibit upward trends across the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 23-percent increase during the 30-year trend period to a 41-percent increase during the 100-year trend period. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. More than two-thirds of streamgages at the 100- and 75-year trend periods exhibit a trend and change point in median peak streamflow in the same direction. Temporally, clusters of change points are observed in the late 1960s through early 1980s for the 100- and 75-year trend periods and around 2006 for the 50- and 30-year trend periods. Trends in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, increased about the same amount as the 50-percent quantile peak streamflows, except at the 100-year trend period, where the 50-percent quantile peak flow increased more for almost all streamgages. The frequency of high flows has also increased in Illinois, with increases in peaks-over-threshold observed across much of the State for the 100- and 75-year trend periods and in northern and southern Illinois for the 50- and 30-year trend periods.
Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with clusters of likely upward trends observed in northern and southern Illinois at the 50- and 30-year trend periods. As expected in response to increasing temperature, the modeled proportion of precipitation falling as snow has largely decreased in the study basins across the State, and modeled potential evapotranspiration has increased. Upward trends in modeled annual runoff, which in this report incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation.
The widespread upward trends in the magnitude of median peak streamflows and the frequency with which high flows occur across the State at the 100- and 75-year trend periods and in northern and southern Illinois at the 50- and 30-year trend periods appear to be driven largely by increases in precipitation based on spatial patterns of these changes and statistical relations between streamflow and climate metrics. Other effects not considered in this report, like urbanization, may be important drivers for certain streamgages in the State.
The prevalence of nonstationarity in peak streamflow in Illinois has important implications for peak-flow frequency analysis. Average annual precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long-term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro-geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non-dendritic, trellis-like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro-geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long-term landscape evolution.
Groundwater contributions to streamflow sustain aquatic ecosystem resilience; streams without significant groundwater inputs often have well-coupled air and water temperatures that degrade cold-water habitat during warm low flow periods. Widespread uncertainty in stream-groundwater connectivity across space and time has created disparate predictions of energy and nutrient fluxes across headwater networks, hindering predictions of cold-water habitat resilience under climate change scenarios. Recently, annual paired air and water temperature signals have been harnessed to indicate stream water thermal sensitivity and the dominance of deep versus shallow groundwater influence, although the utility of diel air–water temperature signal metrics for hydrologic inference has remained unexplored. Here we analyzed two consecutive years of locally paired, air–water temperature data from 47 headwater stream sites in the Catskill Mountains, New York, USA, and discovered characteristic seasonal patterns in diel temperature signal sinusoid metrics (amplitude ratio, phase lag, and mean ratio) driven by shifts in streamflow generation mechanisms and stream network position. Hydrologic interpretations of observed patterns were supported by stream heat budget model scenarios and additional analysis of paired air–water temperature data from two streams in Shenandoah National Park, Virginia, USA, with well characterized stream-groundwater connectivity. We found that within smaller tributaries, streamflow generation transitions from runoff to groundwater dominance were driven by hillslope drying during seasonal periods of lower precipitation. This was evidenced by significant correlations (p < 0.01) between daily water:air temperature signal amplitudes (non-linear decreases of ∼ 50 %) and derived base-flow index at 22 of the 28 sites, indicating enhanced local groundwater influence on streamflow promotes decoupling of diel air–water temperature signals. Additionally, ratios between daily water:air temperature signal means were lower in tributaries (∼0.68) when compared to main-stem (∼0.8) sites, increasing linearly throughout the observational period. In conceptual stream heat budget models, groundwater inflow had minimal effects on daily phase lags (∼0.2 hr), but increases in fractional groundwater discharge (0–50 %) depressed daily amplitude (∼20 % to 50 %) and mean ratios (∼15 %), supporting the sensitivity of daily metrics to interpreted changes in seasonal groundwater contributions to streamflow. During observational periods (i.e., April through October 2021 and 2022), significant differences (p < 0.01) between tributary and main-stem air–water metrics occurred when base-flow contributions were highest (∼0.93 vs. ∼ 0.68), as sites lower in the network had daily temperature metrics dominated by stream channel thermal inertia, rather than local groundwater connectivity, showing enhanced air–water diel signal coupling during warmer, drier periods. Divergent air temperature coupling across the network was interpreted as being driven by distance from local groundwater source zones, additional lateral groundwater inflows do not contribute a meaningful fraction to channel discharge lower in the network. Given the growing footprint of stream temperature observations, diel air–water temperature signals can provide distributed metrics sensitive to upstream groundwater discharge. Consequently, these metrics can support ongoing efforts by resource managers and researchers seeking to forecast the resilience of cold-water habitat to climate warming and changing precipitation regimes in mountain headwater streams.