Since 1952, wastewater discharged to infiltration ponds (also called “percolation ponds”) and disposal wells at the Idaho National Laboratory (INL) has affected water quality in the eastern Snake River Plain (ESRP) aquifer and perched groundwater zones underlying the INL. The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy (DOE), maintains groundwater-monitoring networks at the INL to determine hydrologic trends and to delineate the movement of radiochemical and chemical wastes in both the aquifer and perched groundwater zones. This report presents an analysis of water-level and water-quality data collected from the ESRP aquifer and perched groundwater wells from the USGS groundwater monitoring networks during 2019–21.
From March–May 2018 to March–May 2021, water levels in wells completed in the ESRP aquifer increased in the northern part of the INL and decreased in the southwestern part. Water-level increases ranged from 0.02 to 1.04 feet in the northern part and decreases ranged from 0.03 to 2.94 feet in the southwestern part of the INL.
Detectable concentrations of radiochemical constituents in water samples from wells in the ESRP aquifer at the INL generally decreased or remained constant during 2019–21. Decreases in concentrations were attributed to radioactive decay, changes in waste-disposal methods, and dilution from recharge and underflow.
In 2021, tritium was detected above reporting levels in water samples collected from 46 of 105 aquifer wells and ranged from 150±50 to 4,280±150 picocuries per liter (pCi/L). Tritium concentrations from eight wells completed in deep perched groundwater near the Advanced Test Reactor Complex (ATRC) generally were greater than or equal to the reporting level during at least one sampling event during 2019–21, and concentrations ranged from 160±50 to 2,097±107 pCi/L. Concentrations of strontium-90 in water from 12 of 45 aquifer wells sampled in 2021 exceeded the reporting level, and concentrations ranged from 2.5±0.7 to 299±6 pCi/L. During 2021, concentrations of strontium-90 from five wells completed in deep perched groundwater at the ATRC equaled or exceeded the reporting levels, and concentrations ranged from 3±0.9 pCi/L to 27.8±1.3 pCi/L. Concentrations of cesium-137 were less than the reporting level in all but one aquifer well, and concentrations of plutonium-238, plutonium-239, -240 (undivided), and americium-241 were less than the reporting level in water samples from all aquifer wells sampled during this study period.
Dissolved chromium concentrations in water samples from 64 ESRP aquifer wells ranged from less than (<) 0.5 to 76.4 micrograms per liter (μg/L). During 2019–21, dissolved chromium was detected in water from wells completed in deep perched groundwater above the ESRP aquifer at the ATRC, and concentrations ranged from <1 to 82.1 μg/L.
In 2021, concentrations of dissolved sodium in water from most ESRP aquifer wells in the southern part of the INL were greater than the western tributary groundwater background concentration of 8.3 milligrams per liter (mg/L). During 2021, dissolved sodium concentrations in water from 15 wells completed in deep perched groundwater ranged from 11.7 to 122.5 mg/L. Variations in sodium concentrations in aquifer wells and perched groundwater zones are attributed to either migration of remnant water from the former chemical-waste ponds or disposal volume and composition variability in percolation ponds installed in 2008.
In 2021, concentrations of chloride in most water samples from ESRP aquifer wells south of the Idaho Nuclear Technology and Engineering Center (INTEC) and at the Central Facilities Area (CFA) exceeded background concentrations. Chloride concentrations in water from wells south of the INTEC have generally decreased because of discontinued chloride disposal to the legacy percolation ponds since 2002 when the discharge of wastewater was discontinued. During 2019–21, dissolved chloride concentrations in deep perched groundwater above the ESRP aquifer from 18 wells at the ATRC ranged from 8.15 to 231 mg/L.
In 2021, sulfate concentrations in water samples from ESRP aquifer wells in the south-central part of the INL that exceeded the background concentration of sulfate, ranged from 21 to 141 mg/L. The greater-than-background concentrations in water from these wells are attributed to sulfate disposal at the ATRC infiltration ponds or the legacy INTEC percolation ponds. In 2021, sulfate concentrations in water samples from aquifer wells near the Radioactive Waste Management Complex (RWMC) were mostly greater than background concentrations. The maximum dissolved sulfate concentration in shallow perched groundwater near the ATRC was 575 mg/L in 2021. During 2021, dissolved sulfate concentrations in water from wells completed in deep perched groundwater near the cold waste ponds at the ATRC ranged from 22.3 to 519 mg/L.
In 2021, concentrations of nitrate in water from most ESRP aquifer wells at and near the INTEC exceeded the western tributary groundwater background concentration of 0.655 mg/L. Concentrations of nitrate in aquifer wells southwest of INTEC and farther away from the influence of disposal areas and the Big Lost River, in intermittent source of surface water recharge to the aquifer, show a general decrease in nitrate concentration over time. Two aquifer wells south of INTEC show increasing trends that could result from wastewater beneath the INTEC tank farm being mobilized to the aquifer.
During 2019–21, water samples from several ESRP aquifer wells were collected and analyzed for volatile organic compounds (VOCs). Twelve VOCs were detected, and 1–4 VOCs were detected in water samples from 10 wells. The most frequently detected VOCs include carbon tetrachloride (tetrachloromethane), trichloromethane, tetrachloroethene, 1,1,1-trichloroethane, and trichloroethene. In 2019–21, concentrations for all VOCs were less than their respective maximum contaminant levels (MCLs) for drinking water, except carbon tetrachloride in one well, trichloroethene in two wells, and vinyl chloride in one well.
During 2019–21, variability and bias were evaluated from 34 replicate and 14 blank quality-assurance samples. Results from replicate analyses were investigated to evaluate sample variability. Constituents with acceptable reproducibility were major ions, trace elements, nutrients, and VOCs. All radiochemical constituents including gross alpha- and beta- radioactivity, strontium-90, cesium-137, and tritium, had acceptable reproducibility. Bias from sample contamination was evaluated from equipment, field, and source-solution blanks. Chloride and sulfate were detected slightly above their respective method detection limits in equipment and field blanks, but at concentrations well below the co-collected sample for that well. These chloride and sulfate detections in the field and equipment blanks were inconsequential because they weren’t detected above the analysis-specific variability for those constituents as determined by replicate sample result evaluation. None of the detections of nutrients and trace inorganic constituents were high enough to indicate environmental sample or analytical procedure bias.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235128","collaboration":"DOE/ID-22261Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
Adverse ecological and water-quality effects associated with industrial land-use changes are common for littoral wetlands connected to river mouth ecosystems in the Grand Calumet River-Indiana Harbor Canal Area of Concern. These effects can be exacerbated by recent high Lake Michigan water levels that are problematic for wetland restoration. Wetlands in the adjacent Clark and Pine Nature Preserve and Pine Station Nature Preserve are intended to mitigate wetland destruction in the area of concern by restoring residual dune-and-swale wetlands and preserving habitat for endangered and threatened plant species. Physical hydrology and water-quality monitoring of restored wetland cells at the preserves were initiated during 2019 to evaluate changes after wetland restoration efforts in 2015 and near record-low water levels in early 2013. Lake Michigan water levels rose steadily between late 2013 and 2018 to record-high water levels in 2019 and 2020. In this report, precipitation, evapotranspiration, and groundwater and surface-water levels are analyzed to better understand wetland inundation controls and flow directions in restored northern dune-and-swale wetland settings relative to the Grand Calumet River. Continuous specific conductance data and discrete water-quality samples were collected and analyzed to provide a synoptic view of water quality for the restored wetlands.
High Lake Michigan water levels affected Grand Calumet River stage and shallow groundwater elevations in the study area after the onset of peak lake levels in June 2019, that persisted through summer 2020, before finally receding in September 2020. Grand Calumet River stage peaked soon after lake levels in July 2019, whereas groundwater elevations in the study area peaked in October 2019. Specific conductance values in closed-basin wetland cells in the western and central parts of the nature preserves indicated a dilution trend and contrasted those of interconnected wetland cells along an eastern corridor, where alterations to wetland cells were more pronounced. Monitoring results indicate that varying seasonal wetland inundation trends with low stands in autumn have returned after high water table conditions owing to high water levels on Lake Michigan. Wetland water balance results during the study period indicated that the wetland ecosystem partially moderated flooding during high lake levels through summer evapotranspiration.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235122","collaboration":"Prepared in cooperation with the Indiana Department of Natural Resources","usgsCitation":"Naylor, S., and Gahala, A.M., 2024, Hydrology and water quality of a dune-and-swale wetland adjacent to the Grand Calumet River, Indiana, 2019–22: U.S. Geological Survey Scientific Investigations Report 2023–5122, 29 p., https://doi.org/10.3133/sir20235122.","productDescription":"Report: vii, 29 p.; Dataset","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149471","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":499387,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116023.htm","linkFileType":{"id":5,"text":"html"}},{"id":425295,"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":425294,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5122/images/"},{"id":425293,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5122/sir20235122.XML"},{"id":425292,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235122/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5122"},{"id":425291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5122/sir20235122.pdf","text":"Report","size":"3.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5122"},{"id":425290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5122/coverthb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Grand Calumet River-Indiana Harbor Canal Area of Concern","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.4167,\n 41.6278\n ],\n [\n -87.4167,\n 41.6056\n ],\n [\n -87.35,\n 41.6056\n ],\n [\n -87.35,\n 41.6278\n ],\n [\n -87.4167,\n 41.6278\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
The Mississippi Alluvial Plain (MAP) in the United States (US).
Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km3/year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately.
We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014–2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)— 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.
There is growing interest in incorporating higher-resolution groundwater modeling within the framework of large-scale land surface models (LSMs), including processes such as three-dimensional flow, variable soil saturation, and surface water/groundwater interactions. Conversely, complex groundwater models (e.g., the U.S. Geological Survey Groundwater-Flow Model, MODFLOW) often use simpler representations of land surface dynamics (e.g., surface vegetation, evapotranspiration, recharge) and may benefit from higher process fidelity and temporal resolutions in these inputs. This study investigates the potential of improving groundwater representation in LSMs and land surface dynamics in MODFLOW through forcing MODFLOW with recharge from LSMs. Groundwater simulations build on an existing and well-calibrated MODFLOW model of the U.S. Northern High Plains aquifer, a hydrologically complex basin under the dual impacts of conversion of native vegetation to intense irrigated agricultural fields and climate change. Simulated groundwater recharge from four different land models are used to drive MODFLOW groundwater simulations. Results show relatively large discrepancies between recharge estimates among simulations. Forcing MODFLOW using recharge simulated by some of the LSMs in place of a simple water balance model marginally improves MODFLOW groundwater simulation. Further, our results support the efficacy of coupling LSMs to a sophisticated groundwater model such as MODFLOW. The coupling results in notable improvements in matching the historical groundwater levels through reduction of the skewness coefficient in percent bias histogram (from 1.50 and 1.41 in original LSMs to 0.44 and 0.27, respectively, when MODFLOW is forced by groundwater recharge from LSMs) and reduction of bias. This modeling effort seeks to identify the best compromise between comprehensive land surface processes from global LSMs and advanced representation of groundwater from regional models.
Millions of small (< 10 ha) waterbodies embedded in grassland and agroecosystems in midcontinental North America provide breeding habitat to an estimated 50–80% of North America’s migratory ducks. Tens of millions of dollars are invested annually to conserve and enhance upland and wetland habitats for breeding ducks by prioritizing locations predicted to have high densities of breeding pairs under average precipitation conditions. An implicit assumption of this approach is that the distribution of breeding habitat remains relatively static. Climate change is an identified risk to this strategy. To assess this assumption and plan for potential forthcoming conditions, we estimated changes in potential breeding duck pairs under different climate scenarios by combining results of 1) a mechanistic hydrology model that simulates ecosystem processes for a subset of wetlands distributed across the U.S. Prairie Pothole Region (USPPR); 2) four downscaled climate model projections at mid- and late-century time horizons; and 3) U.S. Fish and Wildlife Service multi-decadal datasets and predictive breeding waterfowl pair statistical models. We conducted virtual and in-person informational sessions with partners to inform them on the best practices of using downscaled global circulation models and approaches for climate scenario planning. This close coordination led to a joint presentation at a monthly North Central Climate Adaptation Science Center seminar. We are also co-developing simulated wetland- waterfowl responses under different climate futures for wetlands. Information from these robust predictions of waterfowl habitat and settling patterns in this region provides land-management agencies insights in prioritizing current conservation actions given uncertainty. In addition, understanding how many breeding pairs the USPPR might support in coming decades will likely influence overall breeding population sizes and sustainable harvest objectives across North America.
","language":"English","publisher":"North Central Climate Adaptation Science Center","usgsCitation":"McKenna, O.P., and Rangwala, I., 2024, The impact of future changes in climate on breeding waterfowl pairs in the US Prairie Pothole Region: Final Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-160169","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":501397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501396,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/65c3d314d34ef4b119cae715"}],"country":"United States","state":"Iowa, Minnesota, Nebraska, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.05393441199925,\n 48.93957527305318\n ],\n [\n -108.27686560641123,\n 49.13252538326782\n ],\n [\n -108.40976144212098,\n 47.91192273687099\n ],\n [\n -106.07828064784712,\n 47.88578029277039\n ],\n [\n -101.59819304168207,\n 47.10144151067459\n ],\n [\n -100.41457783182686,\n 42.307798494527646\n ],\n [\n -96.89275640038099,\n 41.01374950027804\n ],\n [\n -96.60513562002711,\n 43.75532782507912\n ],\n [\n -94.98321580753591,\n 41.5817220442664\n ],\n [\n -94.20767561860225,\n 41.328376780271384\n ],\n [\n -93.63954575827131,\n 42.51490897209834\n ],\n [\n -93.78977025276507,\n 43.36437770770755\n ],\n [\n -94.24637551611141,\n 47.33263848178828\n ],\n [\n -95.05393441199925,\n 48.93957527305318\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":893736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangwala, Imtiaz 0000-0002-4313-9374","orcid":"https://orcid.org/0000-0002-4313-9374","contributorId":148973,"corporation":false,"usgs":false,"family":"Rangwala","given":"Imtiaz","email":"","affiliations":[{"id":34534,"text":"Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado","active":true,"usgs":false}],"preferred":true,"id":957215,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252273,"text":"70252273 - 2024 - Ratingcurve: A Python package for fitting streamflow rating curves","interactions":[],"lastModifiedDate":"2024-03-22T11:39:32.825833","indexId":"70252273","displayToPublicDate":"2024-01-28T06:38:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Ratingcurve: A Python package for fitting streamflow rating curves","docAbstract":"Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. This work is part of that reexamination.
The stationarity assumption is a concern because flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done to assess potential nonstationarity in peak flows in the north-central United States.
This report summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064","collaboration":"Prepared in cooperation with Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Ryberg, K.R., comp., 2024, Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, [variously paged], https://doi.org/10.3133/sir20235064.","productDescription":"1 p.","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":424953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5064/sir20235064.pdf","text":"Abstract","size":"522 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5064"},{"id":424911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5064/coverthb.jpg"}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. Flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done by the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, to assess potential nonstationarity in peak flows in the north-central United States.
This chapter summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flow. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The climate data consist of monthly time series estimates of temperature, precipitation, potential evapotranspiration, actual evapotranspiration, snowfall, soil moisture storage, snow water equivalent, and runoff on a 3.1-mile by 3.1-mile grid for the conterminous United States.
Statistical and graphical analyses were used to investigate potential changes in hydrology and climate. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. To examine potential causal drivers of changes, the climate data were analyzed graphically and statistically. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Peak Streamflow Trends and Their Relation to Changes in Climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235064A","collaboration":"Prepared in cooperation with Illinois Department of Transportation, Iowa Department of Transportation, Michigan Department of Transportation, Minnesota Department of Transportation, Missouri Department of Transportation, Montana Department of Natural Resources and Conservation, North Dakota Department of Water Resources, South Dakota Department of Transportation, and Wisconsin Department of Transportation","usgsCitation":"Ryberg, K.R., Over, T.M., Levin, S.B., Heimann, D.C., Barth, N.A., Marti, M.K., O’Shea, P.S., Sanocki, C.A., Williams-Sether, T.J., Wavra, H.N., Sando, T.R., Sando, S.K., and Liu, M.S., 2024, Introduction and methods of analysis for peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin, chap. A of Ryberg, K.R., comp., Peak streamflow trends and their relation to changes in climate in Illinois, Iowa, Michigan, Minnesota, Missouri, Montana, North Dakota, South Dakota, and Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5064, 27 p., https://doi.org/10.3133/sir20235064A.","productDescription":"Report: vi, 27 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-145865","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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\"}}]}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Marsh environments, characterized by their flora and fauna, change laterally in response to shoreline erosion, water levels and inundation, and anthropogenic activities. The Grand Bay coastal system (USA) has undergone multiple large-scale geomorphic and hydrologic changes resulting in altered sediment supply, depositional patterns, and degraded barrier islands, leaving wetland salt marshes vulnerable to increased wave activity. Two shore-perpendicular transect sites, one along a low-activity shoreline and the other in a high activity area of the same bay-marsh complex, were sampled to investigate how the marshes within 50 m of the modern shoreline have responded to different levels of increased wave activity over the past century. Surface sediments graded finer and more organic with increased distance from the shoreline while cores generally exhibited a coarsening upwards grain-size trend; all cores contained multiple large sedimentological shifts. 210Pb-based mass accumulation rates over the last two decades were greater than the long-term (centurial) average at each site with the fastest accumulation rates of 7.81 ± 1.58 and 7.79 ± 1.63 kg/m2/year at the sites nearest the shoreline. A shoreline change analysis of three time-slices (1848–2017, 1957–2017, 2016–2017) shows increased erosion at both sites since 1848 with modern rates of −0.95 and −0.88 m/year. Downcore sedimentology, mass accumulation rates, and shoreline change rates paired with foraminiferal biofacies and identification of local estuarine indicator species, Paratrochammina simplissima, aided in identifying paleo marsh types, their relative proximity to the shoreline, and sediment provenance. The high-energy marsh site transitioned from middle marsh to low marsh in the 1960s, and the low-energy marsh site transitioned later, at the end of the twentieth and early twenty-first century, due to its more protected location. Marsh type transition corresponds chronologically with the coarsening upwards grain-size trend observed and the degradation of Grand Batture Island; since its submergence, signatures of multiple storm event have been preserved downcore.
The U.S. Geological Survey, in cooperation with the Confederated Tribes of the Umatilla Indian Reservation (CTUIR), (1) estimated water-level change from a multiple-well aquifer test centered on CTUIR well number 422 and (2) evaluated hydraulic connections between the pumping and observation wells on the Umatilla Indian Reservation near Mission, northeastern Oregon to improve the understanding of aquifer characteristics and hydrologic flow boundaries. Water-level changes, or pumping responses, were determined by distinguishing the pumping signal from environmental fluctuations in groundwater levels using analytical water-level models. The pumping well produces water from basalt units from a depth of 450 to 1,057 feet below land surface and was intermittently pumped during February 1–April 18, 2016. Water-level responses to pumping were estimated in the pumping well and in seven observation wells within 4 miles (mi) of the pumping well. The observation wells are open to basalt and some observation wells are either separated from the pumping well by faults and other structural features, within structural zones, or adjacent to structural features. Pumping responses at the observation wells were classified as detected in two wells, ambiguous in one well, and not detected in four wells. Observation-well open-interval elevations overlapped with the pumping-well open interval in both wells with detected pumping responses. Observation wells with detections are 1.8 mi east of the pumping well and across a fault, and 1.4 mi south of the pumping well. The pumping response was classified as ambiguous in an observation well located 1.4 mi west of the pumping well, where the dip of the basalt unit steepens, and adjacent to the Agency syncline. Pumping responses were not detected in observation wells within 0.3 mi of the pumping well where observation-well open-interval elevations are above the top of the pumping well open interval. Analysis of pumping responses indicates (1) a more permeable zone of basalt is adjacent to the lower portion of the pumping-well open interval and extends eastward, (2) basalt adjacent to the upper portion of the pumping-well open-interval is less permeable than the lower portion or separated from the lower portion by a less permeable zone, and (or) (3) a less permeable zone limits vertical hydraulic connectivity between the pumping well and the overlying basalt.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231081","collaboration":"Prepared in cooperation with Confederated Tribes of the Umatilla Indian Reservation","usgsCitation":"Garcia, C.A., Kennedy, J.J., and Ely, K., 2024, Water-level change from a multiple-well aquifer test in volcanic rocks, Umatilla Indian Reservation near Mission, northeastern Oregon, 2016: U.S. Geological Survey Open-File Report 2023–1081, 16 p., https://doi.org/10.3133/ofr20231081.","productDescription":"Report: vii, 16 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-149402","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499191,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115942.htm","linkFileType":{"id":5,"text":"html"}},{"id":424231,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1081/ofr20231081.XML"},{"id":424229,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q1122I","text":"USGS data release","description":"USGS data release","linkHelpText":"Multiple-well aquifer-test data and results, Umatilla Indian Reservation near Mission, northeastern Oregon"},{"id":424228,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231081/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1081"},{"id":424227,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1081/ofr20231081.pdf","text":"Report","size":"3.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1081"},{"id":424230,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1081/images"},{"id":424226,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1081/ofr20231081.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umatilla Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5,\n 45.44\n ],\n [\n -118.5,\n 45.36\n ],\n [\n -118.36,\n 45.36\n ],\n [\n -118.36,\n 45.44\n ],\n [\n -118.5,\n 45.44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director , Oregon Water Science Center
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The Bureau of Reclamation (Reclamation) operates the Central Valley Project (CVP), one of the nation’s largest water projects. Reclamation has an ongoing need to improve the scientific basis for adaptive management of the CVP and, by extension, joint operations with California’s State Water Project. The U.S. Geological Survey (USGS) works cooperatively with the Bureau of Reclamation to provide scientific support for the management of Reclamation’s CVP project. Major habitat restoration efforts and a new water-diversion point are planned to benefit delta smelt (Hypomesus transpacificus) and other species of concern while ensuring the reliability of water supply. In addition, various flow actions and management activities have been identified as possible methods to increase populations of delta smelt and salmonid (Oncorhynchus spp.) runs of concern. The overarching goal of this cooperative project was to provide Reclamation with the scientific information needed to evaluate the efficacy of ongoing and future adaptive management actions and to improve the scientific basis for more flexible CVP operations that would achieve water-supply reliability and fish protection. The research and monitoring described in this report comprises the period 2015–19 and focuses on management issues related to native fish species of concern, especially delta smelt. Conserving the delta smelt population while providing a reliable water supply is a primary management and policy issue in California.
Our approach for this cooperative project is based on the “physics to fish” concept, the idea that high-quality habitat is generated and sustained by the interaction between physical processes and the landscape. These interactions create a template for chemical and biological processes that can change across a variety of spatial and temporal scales. Following this concept, this project (hereafter referred to as “the physics to fish project”) included monitoring and studies of water flows, sediments, water quality, and invertebrate and fish dynamics across a range of spatial and temporal scales and in regions relevant to resource managers tasked with managing water supplies and ecosystem health in the San Francisco Estuary. The intent of this approach was to document the habitat conditions, important processes, and interactions among them that create high-quality habitat for native fishes so that the likely effects of future management actions (for example, habitat restoration) can be objectively assessed at the local (site-specific), regional (within subregions of the estuary), and landscape (across the entire estuary and beyond) scales.
Hydrodynamically, the upper estuary (landward of Carquinez Strait) is characterized by a fixed volume of tidally exchanged water (for example, tidal prism) that interacts with the existing channel network and bathymetry to create regions with differing hydrodynamics. Our results indicate that careful study of construction or reoperation of existing infrastructure to perform management actions can help (1) improve the accuracy of hydrodynamic models; (2) further understanding of ecological effects; and (3) enhance abilities to predict ecological outcomes. At the local scale, we developed a new concept called the Lagrangian to Eulerian (LE) ratio that can be used as a tool for understanding the importance of various hydrodynamic processes in specific channels or channel networks and for forecasting transport dynamics. Channels with LE ratios<1 in a channel network or in a dead-end slough are hydrodynamically able to develop an exchange zone between two parcels of water that may have different chemical and physical properties. In a dead-end channel, there is a landward region with long residence time (no-exchange zone) and a seaward region with short residence time (high-exchange zone) that are well mixed with seaward waters. At the transition (exchange zone) between the high and no-exchange regions, a gradient will form in water-quality constituents that differ in concentration between the landward and seaward waters.
Turbidity affects fish habitat and has declined through time in the San Francisco Estuary. Average turbidity across the Sacramento–San Joaquin Delta (hereafter referred to as “the Delta”) is dependent on annual hydrology. In dry years, the region around Cache Slough (known regionally as the “Cache Slough Complex”) in the northern Delta is generally more turbid than Suisun Bay and the lower Sacramento River. When the Yolo By-Pass (known regionally as “Yolo Bypass”), a large flood bypass that runs parallel to the Sacramento River in the northern Delta, is not flooding and river flows are lower, sediment is usually transported into the Cache Slough Complex because flood tides dominate ebb tides, resulting in transport of suspended sediment from seaward areas of the upper estuary into the Cache Slough Complex. These hydrodynamic conditions also favor the formation of turbidity maximums (TMs) in the Cache Slough Complex. The TMs are areas of higher suspended-sediment concentration, providing higher-turbidity habitat favored by some fishes, including delta smelt, and they can also concentrate other constituents, including phytoplankton and organic carbon that can be important in food webs.
Pelagic primary production by phytoplankton is the basis for Delta food webs supporting pelagic fishes such as delta smelt; however, phytoplankton abundance in the Delta has declined during recent decades. We examined how nutrients, hydrodynamics, and other factors affect phytoplankton blooms. Based on our results, we developed three new concepts of phytoplankton bloom formation in the Delta, each associated with a distinct set of hydrologic conditions. First, productivity cascades highlighted how local processes can contribute to phytoplankton blooms observed at the regional scale. Second, we observed phytoplankton blooms in the upper San Francisco Estuary that were associated with transport out of Yolo By-Pass (transport blooms). Third, we also documented a series of phytoplankton blooms that were in the confluence area at the landward edge of Suisun Bay. The conditions leading to creation of confluence phytoplankton blooms are not yet understood, but the confluence region connects the Cache Slough Complex with Suisun Marsh. Therefore, blooms in this area have the potential to spread to large areas of the Delta.
At the landscape scale, the distribution of the invasive clams (Potamocorbula amurensis and Corbicula fluminea, hereafter referred to as “Corbicula”) is driven by salinity. At smaller spatial scales, the distribution of either species is sensitive to multiple factors affecting survival and reproduction, complicating efforts to predict distribution and abundance without considering local-scale conditions across the area of interest. In the Cache Slough Complex, the area landward of the exchange zone in regions with LE ratio<1 were characterized by low abundances of Corbicula probably because recruits from seaward areas are not transported past the exchange zone and because there are no landward tributaries with adult Corbicula to provide an upstream source of recruits. Corbicula biomass was highest near or downstream from the exchange zone consistent with Corbicula grazing on phytoplankton produced in the exchange zone or transported from the no-exchange zone. The severity of Corbicula grazing could be reduced by manipulating the hydrodynamic characteristics of waterways; however, the beneficial and harmful effects on the organisms meant to benefit from increased phytoplankton production, including zooplankton and fish species of concern, should be thoroughly examined before manipulating hydrodynamic characteristics.
The distribution of fishes at the landscape scale is generally driven by the position of the salinity field in the estuary. The physics to fish project compared distributions of fishes at Ryer Island, a tidal wetland in Suisun Bay and a region of variable salinity, with fish distributions at the Cache Slough Complex, a freshwater region. At Ryer Island, there was an absence of freshwater invasive species and an abundance of native species, such as Sacramento splittail (Pogonichthys macrolepidotus), tule perch (Hysterocarpus traskii), and Sacramento pikeminnow (Ptychocheilus grandis). The native species were almost exclusively captured in wetland and nearshore shallow-water habitat regardless of water-quality conditions. In the Cache Slough Complex, our regional scale objective was to elucidate how hydrodynamic-physical habitat interactions drive fish-community structure. Our studies showed that dendritic channel systems were better able to support native species, while intertidal habitats supported those species best able to exploit the transient character of the habitat. Habitats upstream from the exchange zone were especially important in supporting high numbers of native fishes relative to within or downstream from the exchange zone. Many of the native species were associated with tidal marsh in the no-exchange zone. More pelagic-oriented, mobile species, such as Striped Bass (Morone saxatilis), threadfin shad (Dorosoma petenense), and Sacramento pikeminnow, were more affected by water-quality conditions, such as turbidity.
The physics to fish concept developed in this project provides a framework for designing individual projects and for considering the cumulative effects of multiple projects in a region, using the LE ratio as a guiding metric. The physics to fish concept may also provide a suitable framework for coordinating management actions. Tidal wetlands can function in several ways in the hydrodynamic framework. Relatively small tidal wetlands with short channel networks and with LE ratios>1 are not able to maintain a landward no-exchange zone or an exchange zone. This likely means that any contributions to pelagic food webs would be limited to resources derived from wetland vegetation, which can include dissolved and particulate organic matter (detritus) and populations of consumers that can increase in abundance based on those resources. The fate of the contributed production from these channels depends on the characteristics of the receiving waters seaward of the tidal wetland. If these channels join a large system such as Suisun Bay, then any contribution is likely to be rapidly dispersed in the larger volume; however, the channel junction might provide a focal point for consumers, such as fishes, to congregate and feed on material leaving the wetland on ebb tides before it is dispersed in the larger volume. Fishes might also access these resources by entering the wetland.
The physics to fish project has established a foundation and several new concepts for understanding how habitat restoration can benefit native fish populations at the local and regional levels. Many of the ideas regarding habitat restoration and channel modifications outlined in this report could help guide management actions that could improve conditions for native fishes at little or no water cost beyond water already dedicated to other management actions. A complete list of products originating from this work is provided in appendix 1.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231087","collaboration":"Prepared in cooperation with the Bureau of Reclamation","programNote":"Water Availability and Use Science Program","usgsCitation":"Brown, L.R., Ayers, D.E., Bergamaschi, B., Burau, J.R., Dailey, E.T., Downing, B., Downing-Kunz, M., Feyrer, F.V., Huntsman, B.M., Kraus, T., Morgan, T., Lacy, J.R., Parchaso, F., Ruhl, C.A., Stumpner, E., Stumpner, P., Thompson, J., and Young, M.J., 2024, Physics to fish—Understanding the factors that create and sustain native fish habitat in the San Francisco Estuary: U.S. Geological Survey Open-File Report 2023–1087, 150 p., https://doi.org/10.3133/ofr20231087.","productDescription":"xiv, 150 p.","numberOfPages":"150","onlineOnly":"Y","ipdsId":"IP-117031","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499195,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115941.htm","linkFileType":{"id":5,"text":"html"}},{"id":424433,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1087/ofr20231087.xml"},{"id":424432,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1087/ofr20231087.pdf","text":"Report","size":"40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":424431,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1087/covrthb.jpg"},{"id":424434,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231087/full"},{"id":424435,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1087/images"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.24215111193803,\n 38.050843566230355\n ],\n [\n -122.16421759414595,\n 38.01748278998312\n ],\n [\n -122.05748255890899,\n 38.029494419842194\n ],\n [\n -121.8829792473305,\n 38.02282153523592\n ],\n [\n -121.79488048808702,\n 38.00947394286857\n ],\n [\n -121.63393083177675,\n 37.77148277334254\n ],\n [\n -121.54921998272637,\n 37.596454712018335\n ],\n [\n -121.20021285594603,\n 37.58778236337899\n ],\n [\n -121.14091561414786,\n 37.619995940468854\n ],\n [\n -121.23579120102502,\n 37.827705761824106\n ],\n [\n -121.31203051190909,\n 37.956059539803434\n ],\n [\n -121.3035594291396,\n 38.16548812176558\n ],\n [\n -121.37302191238913,\n 38.51099377471624\n ],\n [\n -121.5271947410649,\n 38.51497075813927\n ],\n [\n -121.70253648740135,\n 38.53378691521087\n ],\n [\n -121.75591267371628,\n 38.311864662587084\n ],\n [\n -121.77454894970978,\n 38.13351227227636\n ],\n [\n -121.89992026094106,\n 38.24802750266139\n ],\n [\n -122.04562205507428,\n 38.2653222757001\n ],\n [\n -122.17437842723578,\n 38.19478754877218\n ],\n [\n -122.24215111193803,\n 38.050843566230355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Urban stream restoration requires a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Urban stream restoration benefits from a quantitative understanding of hydromodification to provide a scientific basis for establishing, prioritizing, and monitoring stream quality improvement goals. A study by the U.S. Geological Survey, in cooperation with the Johnson County Stormwater Management Program, began in 2017 to assess streamflow conditions at U.S. Geological Survey streamgages along Indian and Kill Creeks in Johnson County, Kansas. These streams represent the most urban (Indian Creek) and least urban (Kill Creek) drainage basins in the county. The assessment used 40 streamflow indicators to characterize streamflow conditions for both streams and quantify the degree of hydromodification for Indian Creek. The 40 streamflow indicators consisted of 35 commonly used indicators for characterizing streamflow, 2 less common seasonality indicators, and 3 other indicators based on duration curves, runoff hydrographs, and streamflow percentile classes. The indicators represented five key components of the natural streamflow regime: magnitude, frequency, duration, timing, and rate of change. As part of the study, indicators were evaluated as to general utility for characterizing streamflow conditions, quantifying hydromodification, and assessing the effectiveness of implemented management practices intended to restore urban streams. Results identifying indicators that serve these purposes could be applied more generally to other streams in Johnson County to assess hydromodification and potential restoration opportunities. Although the same set of streamflow indicators may not apply to other regions, methods and results presented in this report provide guidance, techniques, and perspective for future related or similar studies elsewhere, particularly those designed to quantify hydromodification of urban streams and monitor the effectiveness of restoration efforts.
Compared to Kill Creek, which, for the purposes of this study, was considered representative of a least disturbed rural reference condition, Indian Creek hydrology was determined to be substantially modified because of urbanization. Of the 35 streamflow indicators evaluated, 19 indicated a generally consistent and substantial difference between the 2 streams. Hydromodification of Indian Creek was characterized by larger annual mean and monthly mean streamflows (and, thus, larger streamflow volumes), larger low streamflows of shorter duration, larger high streamflows with increased frequency and shorter duration, faster rise and fall rates, and decreased seasonality of high and low streamflows. For the two seasonality indicators, seasonality of high and low streamflows decreased. Duration curves, runoff event hydrographs, and streamflow percentile classes also indicated differences between the two streams for specific ranges of streamflow.
Indicators that were useful in identifying generally consistent and substantial differences between the two streams, and therefore demonstrating they collectively or individually may be indicators of hydromodification, included annual median and mean flows; monthly mean flows for February, July, August, September, October, November, and December; all the minimum mean flow indictors (1-day, 3-day, 7-day, 30-day, and 90-day); annual number and mean magnitude of peak flows; some of the flow pulse indicators; and rise and fall rates. Indicators determined to be marginally useful or not useful for identifying consistent and substantial streamflow differences between streams included the flashiness indicators Richards-Baker flashiness index and the fraction of the year the daily mean flow is greater than the annual mean flow, which was not expected.
Municipalities are challenged by the need to restore stream quality in urbanized areas where options are limited because of existing development. Understanding hydromodification effects and implications for stream quality can help managers plan urban development that minimizes degradation of stream quality and provides insights for implementing effective management practices. Streamflow indicators identified in this report can be used to guide urban stream restoration. In particular, the most useful indicators could form the basis of numeric criteria for restoration goals aimed at achieving or progressing toward more natural streamflow conditions—and, by extension, more healthy ecosystems—by characterizing flow conditions, quantifying hydromodification, establishing stream-restoration goals, and monitoring progress toward achieving those goals as management practices are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235063","collaboration":"Prepared in cooperation with the Johnson County Stormwater Management Program","usgsCitation":"Rasmussen, T.J., Juracek, K.E., Eslick, P.J., Eng, K., and Kellenberger, L.J., 2024, Streamflow characterization and hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018: U.S. Geological Survey Scientific Investigations Report 2023–5063, 44 p., https://doi.org/10.3133/sir20235063.","productDescription":"Report: v, 44 p.; 1 Appendix; 2 Tables; Dataset","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-114771","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":424135,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5063/sir20235063.XML"},{"id":424140,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table3.1.xlsx","text":"Table 3.1","size":"112 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":424139,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table2.1.csv","text":"Table 2.1","size":"10.5 kB","linkFileType":{"id":7,"text":"csv"}},{"id":424133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5063/coverthb.jpg"},{"id":424134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5063/sir20235063.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5063"},{"id":499323,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115947.htm","linkFileType":{"id":5,"text":"html"}},{"id":424143,"rank":11,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235063/full"},{"id":424142,"rank":10,"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":424136,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5063/images/"},{"id":424137,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_appendix1.pdf","text":"Appendix 1","size":"606 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":424138,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table2.1.xlsx","text":"Table 2.1","size":"40.8 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":424141,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5063/downloads/sir20235063_table3.1.csv","text":"Table 3.1","size":"51.4 kB","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"Kansas","county":"Johnson County","otherGeospatial":"Indian and Kill Creek Basins","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.6075,39.0437],[-94.6075,39.0399],[-94.6082,38.8463],[-94.6084,38.8341],[-94.6102,38.7376],[-95.0572,38.7395],[-95.0558,38.9816],[-95.0477,38.9778],[-95.0383,38.9771],[-95.0312,38.9773],[-95.0292,38.9813],[-95.0271,38.9881],[-95.0249,38.9962],[-95.0189,38.9987],[-95.0135,38.9991],[-95.0077,38.998],[-94.9946,38.9976],[-94.9899,38.997],[-94.9841,38.995],[-94.9789,38.9926],[-94.9755,38.9885],[-94.9704,38.9851],[-94.9645,38.9832],[-94.9575,38.982],[-94.9527,38.9828],[-94.9479,38.9845],[-94.9448,38.9871],[-94.9423,38.9898],[-94.9386,38.9933],[-94.9367,38.9964],[-94.9335,38.9995],[-94.9264,38.9998],[-94.9217,38.9996],[-94.9176,38.9977],[-94.9209,38.9919],[-94.923,38.9856],[-94.9207,38.9837],[-94.9164,38.9859],[-94.9115,38.9889],[-94.9078,38.9924],[-94.9014,39.0022],[-94.8989,39.0053],[-94.8945,39.0102],[-94.8919,39.0155],[-94.891,39.021],[-94.8875,39.0313],[-94.8824,39.0379],[-94.8768,39.0441],[-94.8681,39.052],[-94.8631,39.0564],[-94.8488,39.0578],[-94.8318,39.0546],[-94.8131,39.0486],[-94.8038,39.0456],[-94.7197,39.0435],[-94.6693,39.0433],[-94.6075,39.0437]]]},\"properties\":{\"name\":\"Johnson\",\"state\":\"KS\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The hydrological restoration of coastal wetlands is an emerging approach for mitigating and adapting to climate change and enhancing ecosystem services such as improved water quality and biodiversity. This paper synthesises current knowledge on selecting appropriate modelling approaches for hydrological restoration projects. The selection of a modelling approach is based on project-specific factors, such as costs, risks, and uncertainties, and aligns with the overall project objectives. We provide guidance on model selection, emphasising the use of simpler and less expensive modelling approaches when appropriate, and identifying situations when models may not be required for project managers to make informed decisions. This paper recognises and supports the widespread use of hydrological restoration in coastal wetlands by bridging the gap between hydrological science and restoration practices. It underscores the significance of project objectives, budget, and available data and offers decision-making frameworks, such as decision trees, to aid in matching modelling methods with specific project outcomes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.169881","usgsCitation":"Twomey, A., Nunez, K., Carr, J., Crooks, S., Friess, D., Glamore, W., Orr, M., Reef, R., Rogers, K., Waltham, N., and Lovelock, C.E., 2024, Planning hydrological restoration of coastal wetlands: Key model considerations and solutions: Science of the Total Environment, v. 915, 169881, 16 p., https://doi.org/10.1016/j.scitotenv.2024.169881.","productDescription":"169881, 16 p.","ipdsId":"IP-156710","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":440785,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.169881","text":"Publisher Index Page"},{"id":424276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"915","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Twomey, Alice","contributorId":333063,"corporation":false,"usgs":false,"family":"Twomey","given":"Alice","email":"","affiliations":[{"id":13335,"text":"The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":891826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nunez, Karinna","contributorId":333064,"corporation":false,"usgs":false,"family":"Nunez","given":"Karinna","email":"","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":891827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":891828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crooks, Steve","contributorId":333065,"corporation":false,"usgs":false,"family":"Crooks","given":"Steve","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":891829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Friess, Daniel A.","contributorId":35454,"corporation":false,"usgs":false,"family":"Friess","given":"Daniel A.","affiliations":[{"id":25407,"text":"Department of Geography, National University of Singapore","active":true,"usgs":false}],"preferred":false,"id":891830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glamore, William","contributorId":333067,"corporation":false,"usgs":false,"family":"Glamore","given":"William","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":891831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Orr, Michelle","contributorId":197537,"corporation":false,"usgs":false,"family":"Orr","given":"Michelle","email":"","affiliations":[],"preferred":false,"id":891832,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reef, Ruth","contributorId":298614,"corporation":false,"usgs":false,"family":"Reef","given":"Ruth","affiliations":[{"id":64623,"text":"Monash University, Australia","active":true,"usgs":false}],"preferred":false,"id":891833,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Kerrylee","contributorId":64151,"corporation":false,"usgs":false,"family":"Rogers","given":"Kerrylee","email":"","affiliations":[{"id":16754,"text":"University of Wollongong, Australia","active":true,"usgs":false}],"preferred":false,"id":891834,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Waltham, Nathan","contributorId":333070,"corporation":false,"usgs":false,"family":"Waltham","given":"Nathan","email":"","affiliations":[{"id":40403,"text":"James Cook University","active":true,"usgs":false}],"preferred":false,"id":891835,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":891836,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70251052,"text":"70251052 - 2024 - Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","interactions":[],"lastModifiedDate":"2024-01-19T15:15:53.833192","indexId":"70251052","displayToPublicDate":"2024-01-04T09:15:25","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":17129,"text":"STAC Workshop Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"24-002","title":"Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","docAbstract":"The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL). However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale.\n\nThe workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM. Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. \n\nConfidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM.\nLocal monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools. The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices. New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.","language":"English","publisher":"Chesapeake Bay Program STAC (Scientific and Technical Advisory Committee)","usgsCitation":"Berger, K., Filippino, K.C., Shenk, G.W., Goulet, N., Lookenbill, M., Moyer, D.L., Noe, G.E., Porter, A.J., Shallenberger, J., Thomas, B., and Yactayo, G., 2024, Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model: STAC Workshop Report 24-002, 35 p.","productDescription":"35 p.","ipdsId":"IP-160274","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":424622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424607,"rank":1,"type":{"id":15,"text":"Index 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Environment","active":true,"usgs":false}],"preferred":false,"id":892902,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70255769,"text":"70255769 - 2024 - Contrasting demographic responses under future climate for two populations of a montane amphibian","interactions":[],"lastModifiedDate":"2024-07-03T12:04:54.242749","indexId":"70255769","displayToPublicDate":"2024-01-04T07:03:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting demographic responses under future climate for two populations of a montane amphibian","docAbstract":"For species with complex life histories, climate change can have contrasting effects for different life stages within locally adapted populations and may result in responses counter to general climate change predictions. Using data from two, 14-year demographic studies for a North American montane amphibian, Cascades frog (Rana cascadae), we quantified how aspects of current climate influenced annual survival of larvae and adult stages and modeled the stochastic population growth rate (λs) of each population for current (1980–2006) and future periods (2080s). Climate drivers of survival for the populations were similar for larvae (i.e., decreases in precipitation lead to pond drying and mortality), but diverged for terrestrial stages where decreases in winter length and summer precipitation had opposite effects. By the 2080s, we predict one population will be in sharp decline (λs = 0.90), while the other population will remain nearly stable (λs = 0.99) in the absence of other stressors, such as mortality due to disease. Our case study demonstrates a result counter to many climate envelope predictions in that stage-specific responses to local climate and hydrology result in a higher extinction risk for the more northern population.
Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47–65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose–response to low (<30 μg/L) concentrations of Li.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c03315","usgsCitation":"Lombard, M.A., Brown, E.E., Saftner, D., Arienzo, M.M., Fuller-Thomson, E., Brown, C., and Ayotte, J.D., 2024, Estimating lithium concentrations in groundwater used as drinking water for the conterminous United States: Environmental Science and Technology, v. 58, no. 2, p. 1255-1264, https://doi.org/10.1021/acs.est.3c03315.","productDescription":"10 p.","startPage":"1255","endPage":"1264","ipdsId":"IP-152446","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":440811,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c03315","text":"Publisher Index Page"},{"id":435066,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90DZA9M","text":"USGS data release","linkHelpText":"Data and model archive used to model and map lithium concentrations 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0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":891896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Eric E.","contributorId":333096,"corporation":false,"usgs":false,"family":"Brown","given":"Eric","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":891897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saftner, Daniel","contributorId":333090,"corporation":false,"usgs":false,"family":"Saftner","given":"Daniel","email":"","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":891898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arienzo, Monica M.","contributorId":333091,"corporation":false,"usgs":false,"family":"Arienzo","given":"Monica","email":"","middleInitial":"M.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":891899,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller-Thomson, Esme","contributorId":333092,"corporation":false,"usgs":false,"family":"Fuller-Thomson","given":"Esme","email":"","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":891900,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891901,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891902,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70251342,"text":"70251342 - 2024 - Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios","interactions":[],"lastModifiedDate":"2024-02-06T13:19:35.568516","indexId":"70251342","displayToPublicDate":"2024-01-02T07:15:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios","docAbstract":"Most of the world’s river-floodplain ecosystems are simultaneously undergoing modifications to their hydrological regimes and experiencing species invasions, making it unclear whether invasive species are the main drivers of ecosystem change or simply responding to changes in the hydrological regime.
We simulated patterns of forest recruitment and succession in a 2500-ha portion of the Upper Mississippi River floodplain with and without removal of invasive Phalaris arundinacea and under two different future 100-year hydrological scenarios: a future maintaining the average flooding conditions of the past 40 years (random) and a future that projects an observed upward 40-year trend in flooding conditions forward (trending). By comparing scenarios that included Phalaris removal and ones that did not, we were able to identify the conditions where Phalaris was the main driver of forest loss vs. the conditions where hydrology was the main driver of forest loss. Areas where Phalaris was the main driver of forest loss had mean annual flood inundation durations that were similar to areas that did not lose forest cover (60–90 growing season days), while areas where flooding was the main driver of forest loss had longer mean inundation durations (102–124 growing season days). In comparison to the random hydrology scenario, the trending scenario produced a decrease in the area over which Phalaris was identified as the main driver of forest loss and an increase in the area over which flood inundation was identified as the main driver of forest loss. Thus, if the observed trends in flooding continue, our model projects an increase in the area over which eradicating Phalaris is unlikely to result in the maintenance of forest cover. We used the Resist-Accept-Direct (RAD) framework to discuss potential management options to resist changes and maintain forest cover where Phalaris is likely to be the main driver of forest loss and to accept or direct changes in areas where forest loss is likely driven by hydrological change.
Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide-triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a ≤6-h window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all Northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs), while Southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides; thus, atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps define future research directions relating to atmospheric conditions and landslides, and supports interdisciplinary research efforts.
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\"}}]}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Oakley, Nina S.","contributorId":197885,"corporation":false,"usgs":false,"family":"Oakley","given":"Nina","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":921116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartlett, Samuel M.","contributorId":347239,"corporation":false,"usgs":false,"family":"Bartlett","given":"Samuel","email":"","middleInitial":"M.","affiliations":[{"id":83104,"text":"Center for Western Weather and Water Extremes, Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":921118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comstock, Karimah Halona 0009-0003-3662-5678","orcid":"https://orcid.org/0009-0003-3662-5678","contributorId":335639,"corporation":false,"usgs":true,"family":"Comstock","given":"Karimah","email":"","middleInitial":"Halona","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":921120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":921121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burgess, W.P.","contributorId":347240,"corporation":false,"usgs":false,"family":"Burgess","given":"W.P.","email":"","affiliations":[{"id":83107,"text":"California Geological Survey, Sacramento","active":true,"usgs":false}],"preferred":false,"id":921122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Corbett, Skye C. 0000-0003-3277-1021 scorbett@usgs.gov","orcid":"https://orcid.org/0000-0003-3277-1021","contributorId":200617,"corporation":false,"usgs":true,"family":"Corbett","given":"Skye","email":"scorbett@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921123,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70250999,"text":"70250999 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-02-02T14:59:59.031674","indexId":"70250999","displayToPublicDate":"2024-01-01T08:50:09","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","docAbstract":"RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Colorado River Basin.
The RiverWare model described in this chapter presents development of the Colorado River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer gage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Colorado River Basin POR hydrology will be in a later section.
Calibration results will also be shown that illustrate the overall model performance for the POR. The time window simulation run is for January 01, 1930 – September 30, 2019. This time window captures all big events occurred over the Colorado River basin. Each simulated water year was inspected individually to better validate the results.
Historical pool elevations along with observed inflows and outflows were compared against the model simulated results.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE Fort Worth District, FEMA Region 6, NWS WGRFC","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 166 p.","productDescription":"166 p.","ipdsId":"IP-127610","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424561,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.8,\n 28.65\n ],\n [\n -95.8,\n 32\n ],\n [\n -101,\n 32\n ],\n [\n -101,\n 28.65\n ],\n [\n -95.8,\n 28.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892730,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70250996,"text":"70250996 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology","interactions":[],"lastModifiedDate":"2024-02-02T14:47:45.280372","indexId":"70250996","displayToPublicDate":"2024-01-01T08:26:24","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology","docAbstract":"Statistical analysis of the observational record from U.S. Geological Survey (USGS) streamgages and period of historical flow observations prior to the gage installation provides an informative means of estimating flood flow frequency. The U.S. Geological Survey contributed to the InFRM team’s efforts by performing the statistical analysis of the gaged record and authored this Appendix to the Lower Watershed Hydrology Assessment. Flood flow frequency is defined by values or quantiles of streamflow for selected annual exceedance probabilities (AEPs) (England and others, 2019). The annual peak streamflow data collected as part of the systematic operation of a streamgage provides the foundation for a detailed analysis of peak streamflow, but additional historical information pertaining to peak streamflows that predates the installation of a streamgage also can be used. An annual peak streamflow is defined as the maximum instantaneous streamflow for a streamgage for a given water year, and annual peak streamflow data for USGS streamgages can be acquired through the USGS National Water Information System (NWIS) database (USGS, 2022). The statistical analyses are based on water-year increments. A water year is the 12-month period from October 1 of a given year through September 30 of the following year designated by the calendar year in which it ends.
For the statistical hydrology portion of a multifaceted analysis, InFRM team members from the USGS analyzed annual peak streamflow records for the 45 USGS streamgages (gages) and 21 Lower Colorado River Authority (LCRA) streamgages (gages) in the lower Colorado River Basin listed in Table A.1 and Table A.8. The locations of USGS gages are also shown on Figure A.1, Figure A.2, and Figure A.3, and the locations of LCRA gages are shown in Section 1.4. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE-Fort Worth District, FEMA Region 6, NWS West Gulf River Forecast Center","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix A: Statistical hydrology: Interagency Flood Risk Management Report, 246 p.","productDescription":"246 p.","ipdsId":"IP-133413","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424560,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.8,\n 28.65\n ],\n [\n -95.8,\n 32\n ],\n [\n -101,\n 32\n ],\n [\n -101,\n 28.65\n ],\n [\n -95.8,\n 28.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892728,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259612,"text":"70259612 - 2024 - Hydrologisch informierte Murgangmodellierung mit RAMMS Kann das Erosionsverhalten von Murgängen anhand der Sättigungsbedindungen vorhergesagt werden?","interactions":[],"lastModifiedDate":"2024-10-22T16:47:24.208632","indexId":"70259612","displayToPublicDate":"2024-01-01T07:46:31","publicationYear":"2024","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":18750,"text":"FAN Agenda","active":true,"publicationSubtype":{"id":30}},"title":"Hydrologisch informierte Murgangmodellierung mit RAMMS Kann das Erosionsverhalten von Murgängen anhand der Sättigungsbedindungen vorhergesagt werden?","docAbstract":"Murgänge können durch Erosion entlang ihres Fliessweges erheblich an Volumen und damit an Gefährdungspotenzial gewinnen. Diese Erosionsprozesse hängen erwiesenermassen mit den Sättigungsbedingungen des Sediments vor dem Ereignis zusammen. Solche hydrologische Einflussfaktoren werden bei der Murgangmodellierung bisher jedoch nicht explizit berücksichtigt. Im vorliegenden Beitrag wird ein Ansatz vorgestellt, um diese Lücke zu schliessen. \nDas verwendete Simulationsprogramm (RAMMS) ist in der Lage, für eine Mehrheit der Murgangereignisse die Fliess- und Erosionseigenschaften gut abzubilden. Darüber hinaus kann in diesen Fällen ein klarer Zusammenhang zwischen der Erodierbarkeit und der vorhergehenden Sättigung des Fliesspfades festgestellt werden. Die vorgestellte Methode stellt eine vielversprechende Grundlage für eine umfassendere Beurteilung von Gefahrenprozessen sowie deren Beeinflussung durch den Klimawandel dar.","language":"German","publisher":"Fachleute Naturgefahren Schweiz","usgsCitation":"Koenz, A.L., Hirschberg, J., McArdell, B., Mirus, B., de Haas, T., Bartelt, P., and Molnar, P., 2024, Hydrologisch informierte Murgangmodellierung mit RAMMS Kann das Erosionsverhalten von Murgängen anhand der Sättigungsbedindungen vorhergesagt werden?: FAN Agenda, no. 1, p. 22-27.","productDescription":"6 p.","startPage":"22","endPage":"27","ipdsId":"IP-164424","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":462920,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.dora.lib4ri.ch/wsl/islandora/object/wsl%3A37479"},{"id":463068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Koenz, Anna L.","contributorId":345193,"corporation":false,"usgs":false,"family":"Koenz","given":"Anna","email":"","middleInitial":"L.","affiliations":[{"id":80865,"text":"WSL, ETH","active":true,"usgs":false}],"preferred":false,"id":915953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirschberg, Jacob","contributorId":345194,"corporation":false,"usgs":false,"family":"Hirschberg","given":"Jacob","email":"","affiliations":[{"id":80865,"text":"WSL, ETH","active":true,"usgs":false}],"preferred":false,"id":915954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McArdell, Brian","contributorId":345195,"corporation":false,"usgs":false,"family":"McArdell","given":"Brian","affiliations":[{"id":80280,"text":"WSL","active":true,"usgs":false}],"preferred":false,"id":915955,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":915956,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Haas, Tjalling","contributorId":345196,"corporation":false,"usgs":false,"family":"de Haas","given":"Tjalling","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":915957,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bartelt, Perry","contributorId":345197,"corporation":false,"usgs":false,"family":"Bartelt","given":"Perry","email":"","affiliations":[{"id":80867,"text":"SLF","active":true,"usgs":false}],"preferred":false,"id":915958,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Molnar, Peter","contributorId":345198,"corporation":false,"usgs":false,"family":"Molnar","given":"Peter","affiliations":[{"id":80868,"text":"ETH","active":true,"usgs":false}],"preferred":false,"id":915959,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238080,"text":"70238080 - 2024 - Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA","interactions":[],"lastModifiedDate":"2023-12-21T17:57:32.339716","indexId":"70238080","displayToPublicDate":"2023-12-15T11:51:08","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"7","title":"Forecasting water levels using machine (deep) learning to complement numerical modelling in the southern Everglades, USA","docAbstract":"Water level is an important guide for water resource management and wetland ecosystems, defining one of the most basic processes in hydrology. This research seeks to investigate the possibility of complementing numerical modeling with a Machine Learning (ML) model to forecast daily water levels in the southern Everglades in Florida, USA. An exact analytical solution to water level may not be possible, but using the computational methods afforded by ML, the traditional numerical techniques may be enhanced to generate more robust, scalable predictions. Five locations were chosen for application of the Time-Delayed Neural Network (TDNN) and Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) ML models, which were built to estimate water level with 1, 2, 3, 7 and 10 day forecasts using a simulation step of 1 day. The results showed that rainfall forecasts from weather models could improve water-level forecasts if the accuracy and performance of the weather models can be improved. The ML models presented here improve water-level predictions from a historical hydrologic model for a 24 hour forecast horizon.
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