Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.
Larval lampreys are filter feeders that live for several years burrowed in fine sediments in freshwater streams. Stream side channels and edges, where larval lampreys gather, are vulnerable to natural and human-caused dewatering. Water level reductions can strand and kill thousands of larval lampreys, in part because many remain burrowed until their habitats are exposed, at which point larvae must emerge and attempt to move over dewatered substrate to locate wetted habitat. Dewatering for restoration efforts or seasonal closures of irrigation canals can be done slowly to reduce lamprey strandings, but in some settings, mechanisms are lacking to control the dewatering rate. Phased dewatering, where water level is reduced in stages separated by periods of static water level, could provide options when dewatering rate cannot be tightly controlled. To guide this phased approach, information is needed on the movement capability of larval lampreys. We examined larval lamprey (Entosphenus tridentatus and Lampetra spp.) movement distance and rate over dewatered substrate at shoreline slopes of 1%, 5%, 10% and 20% in a laboratory setting and modelled results using gamma regression models. Model results suggest both movement distance and movement rate increased with increasing slope and increasing larval length. We used the models to predict minimum distances and rates that 90%, 75% and 50% of medium-sized (75 mm) lampreys would move over dewatered substrates on slopes of 1%–20%. The models predicted that 50% of larvae could move distances of ≥31 cm at rates of ≥0.7 mm/s on a 1% slope and distances of ≥502 cm at rates of ≥8.6 mm/s on a 20% slope. We present an example scenario of how information on larval movement capabilities and shoreline slope could guide phased dewatering events to limit impacts to lampreys.
The United States (U.S.) coastal plain is subject to rising sea levels, land subsidence, more severe coastal storms, and more intense droughts. These changes lead to inputs of marine salts into freshwater-dependent coastal systems, creating saltwater intrusion. The penetration of salinity into the coastal interior is exacerbated by groundwater extraction and the high density of agricultural canals and ditches throughout much of the rural U.S. landscape. Together saltwater intrusion and sea level rise (SWISLR) create substantial changes to the social-ecological systems situated along the coastal plain. Many scholars and practitioners are engaged in studying and managing SWISLR impacts on social, economic, and ecological systems. However, most efforts are localized and disconnected, despite a widespread desire to understand this common threat. In addition to variable rates of sea level rise across the U.S. outer coastal plain, differences in geomorphic setting, water resources infrastructure and management, and climate extremes are resulting in different patterns of saltwater intrusion. Understanding both the absolute magnitude of this rapid environmental change, and the causes and consequences for its spatial and temporal variation presents an opportunity to build new mechanistic models to link directional climate change to temporally and spatially dynamic socio-environmental impacts. The diverse trajectories of change offer rich opportunities to test and refine modern theories of ecosystem state change in systems with exceptionally strong socioecological feedbacks.
Sand Hollow Reservoir in Washington County, Utah, was completed in March 2002 and is operated primarily for managed aquifer recharge by the Washington County Water Conservancy District. Sand Hollow Reservoir has remained nearly full since 2006 because of surface-water diversions of about 288,000 acre-feet (acre-ft) from 2002 through 2018. Groundwater levels in monitoring wells near the reservoir rose through 2006 and have fluctuated since then because of variations in reservoir stage and nearby pumping from production wells. Between 2004 and 2018, about 46,000 acre-ft of groundwater was withdrawn by these wells for municipal supply. In addition, about 45,000 acre-ft of shallow seepage was captured by French drains adjacent to the North and West Dams and used for municipal supply, irrigation, or returned to the reservoir. From 2002 through 2018, about 159,000 acre-ft of water seeped beneath the reservoir to recharge the underlying Navajo Sandstone aquifer, which includes about 18,500 acre-ft of recharge in the 2017–18 period since the last report.
Water quality continued to be monitored at various wells in Sand Hollow during 2017–18 to evaluate the timing and location of reservoir recharge as it moved through the aquifer. Changing geochemical conditions at monitoring well water district (WD) 12 indicated rising groundwater levels and mobilization of vadose-zone salts, which could be a precursor to the arrival of reservoir recharge. Changes to geochemical conditions and environmental tracers at monitoring well WD 22 indicated the arrival of reservoir recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221089","collaboration":"Prepared in cooperation with the Washington County Water Conservancy District","programNote":"Water Availability and Use Science Program","usgsCitation":"Marston, T.M., 2024, Assessment of managed aquifer recharge at Sand Hollow Reservoir, Washington County, Utah, updated to conditions through 2018: U.S. Geological Survey Open-File Report 2022–1089, 20 p., https://doi.org/10.3133/ofr20221089.","productDescription":"Report: v, 20 p.; 2 Tables","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-124201","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":499185,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115940.htm","linkFileType":{"id":5,"text":"html"}},{"id":424118,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1089/ofr20221089_table1.1.csv","text":"Appendix table 1.1","size":"20 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Field water-quality parameters, dissolved organic carbon, tritium, chlorofluorocarbons, sulfur hexaflouride in groundwater and surface water from Sand Hollow, Utah."},{"id":424116,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1089/images"},{"id":424115,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1089/ofr20221089.xml"},{"id":424114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1089/ofr20221089.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":424113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1089/covrthb.jpg"},{"id":424117,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221089/full"},{"id":424119,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1089/ofr20221089_table1.2.csv","text":"Appendix table 1.2","size":"25 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Major and minor chemical constituents in groundwater and surface water from selected sites in Sand Hollow, Utah."}],"country":"United States","state":"Utah","county":"Washington County","otherGeospatial":"Sand Hollow Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.43516202274216,\n 37.16544148428713\n ],\n [\n -113.43516202274216,\n 37.068527882070626\n ],\n [\n -113.32049222293712,\n 37.068527882070626\n ],\n [\n -113.32049222293712,\n 37.16544148428713\n ],\n [\n -113.43516202274216,\n 37.16544148428713\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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2329 West Orton Circle
Salt Lake City, Utah 84119-2047
In recent years, the Mississippi River Deltaic Plain (MRDP) has experienced the highest rates of wetland loss in the USA. Although the process of vertical drowning has been heavily studied in coastal wetlands, less is known about the relationship between elevation change and land loss in wetlands that are experiencing lateral erosion and the contribution of erosion to land loss in the MRDP. We quantified relationships of elevation change and land change in ten submerging tidal wetlands and found that, despite significant land loss, elevation trajectories in seven of the land loss study sites were positive. Furthermore, we observed an acceleration in elevation gain preceding the conversion from vegetated marsh to open water.
To identify regional contributions of lateral erosion to land loss, we quantified the relationship of elevation change and land change in 159 tidal marsh sites in the MRDP. Approximately half the sites were persistently losing land, and 82% of these sites were vulnerable to erosion, identifying erosion as a dominant mechanism of coastal wetland loss in this region. Notably, the sites that were vulnerable to erosion were experiencing land loss while also gaining elevation, and sites with the highest land loss exhibited accelerating elevation gain. Together, these data illustrate that (1) erosion is a dominant mechanism of wetland loss in the MRDP, (2) accelerated elevation gain is an indicator of erosion, and (3) consideration of elevation change trajectories within the context of land change is critical for providing accurate coastal wetland vulnerability assessments.
Groundwater flow and particle tracking models are critical tools to simulate the natural system, contaminant fate and transport, and effects of remediation. Constrained optimization uses models to systematically explore the interplay between remedial design and contaminant fate, considering uncertainty. Sequential Linear Programming (SLP) provides a design alternative addressing a single goal (e.g. maximum hydraulic containment, maximum mass removal). Multi-objective algorithms like Nondominated Sorting Genetic Algorithm (NSGA-II) explore the tradeoffs among such objectives and more (e.g. cost, public-supply well contamination). We explore both approaches at a contaminated site in Long Island, New York USA. We compare the algorithms and ramifications on results. NSGA-II explores, at additional computational cost, explicit tradeoffs among multiple objectives, providing additional insights relative to SLP. The NGSA-II algorithm allows for graphical consideration of three objectives. SLP decision variables often settle at predetermined bounds. Bounds assignment thus differs from parameter estimation; bounds must be acceptable rather than safeguards.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.105952","usgsCitation":"Fienen, M., Corson-Dosch, N., Jahn, K., and White, J., 2024, Comparing single and multiple objective constrained optimization algorithms for tuning a groundwater remediation system: Environmental Modelling & Software, v. 173, 105952, 12 p., https://doi.org/10.1016/j.envsoft.2024.105952.","productDescription":"105952, 12 p.","ipdsId":"IP-154816","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467038,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.105952","text":"Publisher Index Page"},{"id":424282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Navy Grumman Groundwater Plume site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.51175816259176,\n 40.65126946756959\n ],\n [\n -73.42534399670191,\n 40.68206493506739\n ],\n [\n -73.45886673346936,\n 40.78790400347398\n ],\n [\n -73.57470641274439,\n 40.76505702193873\n ],\n [\n -73.51175816259176,\n 40.65126946756959\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"173","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corson-Dosch, Nicholas 0000-0002-6776-6241","orcid":"https://orcid.org/0000-0002-6776-6241","contributorId":202630,"corporation":false,"usgs":true,"family":"Corson-Dosch","given":"Nicholas","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jahn, Kalle 0000-0002-4976-0137","orcid":"https://orcid.org/0000-0002-4976-0137","contributorId":333053,"corporation":false,"usgs":true,"family":"Jahn","given":"Kalle","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":248830,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":50032,"text":"GNS New Zealand","active":true,"usgs":false}],"preferred":false,"id":891804,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252610,"text":"70252610 - 2024 - Mapping riparian vegetation response to climate change on the San Carlos Apache Reservation and Upper Gila River watershed to inform restoration priorities: 1935 to Present","interactions":[],"lastModifiedDate":"2026-03-25T16:10:20.03069","indexId":"70252610","displayToPublicDate":"2024-01-09T11:07:51","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":20198,"text":"Final Project Report","active":true,"publicationSubtype":{"id":1}},"title":"Mapping riparian vegetation response to climate change on the San Carlos Apache Reservation and Upper Gila River watershed to inform restoration priorities: 1935 to Present","docAbstract":"The riparian vegetation within the San Carlos Apache Reservation (hereafter Reservation), within the Upper Gila River watershed extending from southwestern New Mexico into southeastern Arizona, provides immense ecological and cultural value to the people of the San Carlos Apache Tribe (hereafter referred to as the Tribe/Tribal) but has experienced substantial changes and stresses over the past century because of fluctuations in climate and a series of human-induced and natural disturbances. This research addresses these challenges by analyzing the riparian vegetation within the Upper Gila River watershed using aerial and satellite imagery, and by documenting the direct relationship to fluctuations in climate conditions. Results from this study would be provided to the Tribe to help the Tribe develop a restoration plan for their riparian forests.
We show that the riparian vegetation has largely increased overall in greenness throughout the study period (i.e., 1985 through 2021), despite periods of drought conditions. This extends to the end of our study period particularly with native vegetation in the upper watershed. However, non-native and invasive tamarisk vegetation within much of the lower watershed has shown declining trends and increasing vegetative stress. Furthermore, these areas have experienced a large increase in wildfire presence and other disturbances. Nevertheless, Tribal restoration applications have been shown to increase native vegetation plant cover, suggesting that restoration activities have been successful overall.
In the two decades, since the advent of the term “flash drought,” considerable research has been directed toward the topic. Within the scientific community, we have actively forged a new paradigm that has avoided a chaotic evolution of conventional drought but instead recognizes that flash droughts have distinct dynamics and, particularly, impacts. We have moved beyond the initial debate over the definition of flash drought to a centralized focus on the triad of rapid onset, drought development, and associated impacts. The refinement toward this general set of principles has led to significant progress in determining key variables for monitoring flash drought development, identifying notable case studies, and compiling fundamental physical characteristics of flash drought. However, critical focus areas still remain, including advancing our knowledge on the atmospheric and oceanic drivers of flash drought; developing flash drought-specific detection indices and monitoring systems tailored to practitioners; improving subseasonal-to-seasonal prediction of these events; constraining uncertainty in flash drought and impact projections; and using social science to further our understanding of impacts, particularly with regard to sectors that lie outside of our traditional hydroclimatological focus, such as wildfire management and food-security monitoring. Researchers and stakeholders working together on these critical topics will assure society is resilient to flash drought in a changing climate.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1714","usgsCitation":"Christian, J., Hobbins, M., Hoell, A., Otkin, J., Ford, T.W., Cravens, A.E., Powlen, K., Wang, H., and Mishra, V., 2024, Flash drought: A state of the science review: WIREs Water, v. 11, no. 3, e1714, 28 p., https://doi.org/10.1002/wat2.1714.","productDescription":"e1714, 28 p.","ipdsId":"IP-156901","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":425361,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South 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aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":893951,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powlen, Kathryn 0000-0002-9685-0063","orcid":"https://orcid.org/0000-0002-9685-0063","contributorId":328833,"corporation":false,"usgs":true,"family":"Powlen","given":"Kathryn","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893952,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Hailan","contributorId":298623,"corporation":false,"usgs":false,"family":"Wang","given":"Hailan","email":"","affiliations":[{"id":64628,"text":"NOAA Climate Prediction Center","active":true,"usgs":false}],"preferred":false,"id":893953,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mishra, Vimal","contributorId":333802,"corporation":false,"usgs":false,"family":"Mishra","given":"Vimal","email":"","affiliations":[{"id":79976,"text":"Indian Institute of Technology (IIT) Gandhinagar","active":true,"usgs":false}],"preferred":false,"id":893954,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70250904,"text":"70250904 - 2024 - Machine learning approaches to identify lithium concentration in petroleum produced waters","interactions":[],"lastModifiedDate":"2024-10-07T16:06:46.920365","indexId":"70250904","displayToPublicDate":"2024-01-09T08:18:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5502,"text":"Mineral Economics","onlineIssn":"2191-2211","printIssn":"2191-2203","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning approaches to identify lithium concentration in petroleum produced waters","docAbstract":"Prices for battery-grade lithium have increased substantially since 2020, which is propelling the search for additional sources of this important element. Battery-grade lithium is predominately recovered from continental brines. Most crude oil and natural gas wells recover briny formation water, which may represent an additional source. Chemical analysis of these waters has been shown to indicate the presence of varying concentrations of lithium and related elements. This paper briefly reviews developments and literature supporting the presence of lithium in petroleum reservoir brines. It also describes the coverage and distribution of lithium data analyses in the United States Geological Survey National Produced Waters Geochemical Database (PWGD). It then addresses the question as to whether a lithium concentration can be accurately predicted using constituents of ion chemistry in produced brines from specific geologic formations. Four machine learning algorithms are employed to classify the commercial potential of lithium in oil field brines using data from oil wells recovering formation water from the Smackover Formation. The calibrated classification models are further applied to new (out-of-sample) data from the Marcellus Formation in the Appalachian Basin. Among the approaches considered, the predictive performance and wider applicability of the gradient boosted tree and the deep neural network models are determined to be the most promising. Finally, we discuss how the calibrated models could be applied to assure the quality of the data reported from chemical laboratory analysis and for imputation when lithium values are missing.
","language":"English","publisher":"Springer","doi":"10.1007/s13563-023-00409-8","usgsCitation":"Attanasi, E., Coburn, T., and Freeman, P., 2024, Machine learning approaches to identify lithium concentration in petroleum produced waters: Mineral Economics, v. 37, p. 477-497, https://doi.org/10.1007/s13563-023-00409-8.","productDescription":"21 p.","startPage":"477","endPage":"497","ipdsId":"IP-144611","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":424326,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","noUsgsAuthors":false,"publicationDate":"2024-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":1809,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coburn, Timothy","contributorId":333122,"corporation":false,"usgs":false,"family":"Coburn","given":"Timothy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":891988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891989,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250803,"text":"sir20235063 - 2024 - Streamflow characterization and hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018","interactions":[],"lastModifiedDate":"2026-01-29T23:09:22.64218","indexId":"sir20235063","displayToPublicDate":"2024-01-08T15:21:19","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5063","displayTitle":"Streamflow Characterization and Hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018","title":"Streamflow characterization and hydromodification, Indian and Kill Creek Basins, Johnson County, Kansas, 1985–2018","docAbstract":"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.
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1217 Biltmore Drive
Lawrence, KS 66049
The free-living thermophilic amoeba Naegleria fowleri (N. fowleri) causes the highly fatal disease primary amoebic meningoencephalitis. The environmental conditions that are favorable to the growth and proliferation of N. fowleri are not well-defined, especially in northern regions of the United States. In this study, we used culture-based methods and multiple molecular approaches to detect and analyzeN. fowleri and other Naegleria spp. in water, sediment, and biofilm samples from five hot spring sites in Grand Teton National Park, Wyoming, U.S.A. These results provide the first detections of N. fowleri in Grand Teton National Park and provide new insights into the distribution of pathogenic N. fowleri and other nonpathogenic Naegleria spp. in natural thermal water systems in northern latitudes.
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Planetary health is an emerging framework that aims to secure the state of natural systems within environmental limits that ensure humanity can flourish. The planetary health concept is tied to the planetary boundaries framework in which various ecological thresholds are identified with the goal of constraining human activity to within those boundaries (so-called safe operating spaces). Freshwater systems are influenced by some planetary-scale processes like the climate systems and phosphorus and nitrogen cycles. Nonetheless, safe boundaries to guide the conservation and management of freshwater ecosystems need to consider their uneven distribution around the globe, and their ecological and hydrologic limits, which are often site and context dependent. Efforts to down-scale planetary boundaries concepts to the management of freshwater recreational fisheries at the lake scale, suggest that there are opportunities for rethinking planetary health as a nested cross-scale approach from the planet to the watershed.","language":"English","publisher":"Elsevier","doi":"10.1016/S2542-5196(23)00275-9","usgsCitation":"Cooke, S., Lynch, A., Tickner, D., Abell, R., Dalu, T., Fiorella, K.J., Raghavan, R., Harrison, I.J., Jahnig, S.C., Vollmer, D., and Carpenter, S., 2024, Can the planetary health concept save freshwater biodiversity and ecosystems?: Lancet Planetary Health, v. 8, no. 1, p. e2-e3, https://doi.org/10.1016/S2542-5196(23)00275-9.","productDescription":"2 p.","startPage":"e2","endPage":"e3","ipdsId":"IP-156822","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":440775,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/s2542-5196(23)00275-9","text":"Publisher Index Page"},{"id":424737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cooke, Steven J.","contributorId":56132,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven J.","affiliations":[{"id":36574,"text":"Carleton University, Ottawa, Ontario","active":true,"usgs":false}],"preferred":false,"id":893053,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":893054,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tickner, David","contributorId":224152,"corporation":false,"usgs":false,"family":"Tickner","given":"David","email":"","affiliations":[{"id":37767,"text":"World Wildlife Fund","active":true,"usgs":false}],"preferred":false,"id":893055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abell, Robin","contributorId":152400,"corporation":false,"usgs":false,"family":"Abell","given":"Robin","affiliations":[],"preferred":false,"id":893056,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dalu, Tatenda","contributorId":332550,"corporation":false,"usgs":false,"family":"Dalu","given":"Tatenda","email":"","affiliations":[{"id":79488,"text":"University of Mpumalanga","active":true,"usgs":false}],"preferred":false,"id":893057,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fiorella, Kathryn J.","contributorId":268093,"corporation":false,"usgs":false,"family":"Fiorella","given":"Kathryn","email":"","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":893058,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raghavan, Rajeev","contributorId":250656,"corporation":false,"usgs":false,"family":"Raghavan","given":"Rajeev","email":"","affiliations":[{"id":50216,"text":"Kerala University of Fisheries and Ocean Studies","active":true,"usgs":false}],"preferred":false,"id":893059,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harrison, Ian J.","contributorId":200864,"corporation":false,"usgs":false,"family":"Harrison","given":"Ian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":893060,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jahnig, Sonja C.","contributorId":211858,"corporation":false,"usgs":false,"family":"Jahnig","given":"Sonja","email":"","middleInitial":"C.","affiliations":[{"id":38332,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries","active":true,"usgs":false}],"preferred":false,"id":893061,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vollmer, Derek","contributorId":333549,"corporation":false,"usgs":false,"family":"Vollmer","given":"Derek","email":"","affiliations":[{"id":55551,"text":"WWF","active":true,"usgs":false}],"preferred":false,"id":893062,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Carpenter, Steve","contributorId":333550,"corporation":false,"usgs":false,"family":"Carpenter","given":"Steve","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":893063,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70251021,"text":"70251021 - 2024 - Widespread chemical dilution of streams continues as long-term effects of acidic deposition slowly reverse","interactions":[],"lastModifiedDate":"2024-01-18T12:44:48.545435","indexId":"70251021","displayToPublicDate":"2024-01-07T06:41:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Widespread chemical dilution of streams continues as long-term effects of acidic deposition slowly reverse","docAbstract":"Studies of recovery from acidic deposition have focused on reversal of acidification and its associated effects, but as recovery proceeds slowly, chemical dilution of surface waters is emerging as a key factor in the recovery process that has significant chemical and biological implications. This investigation uses long-term chemical records from 130 streams in the Adirondack region of New York, USA, to evaluate the role of ongoing decreases in conductance, an index of dilution, in the recovery of these streams. Stream chemistry data spanning up to 40 years (1980s–2022) showed that acid-neutralizing capacity has increased in 92% of randomly selected streams, but that harmful levels of acidification still occur in 37% of these streams. Conductance and Ca2+ concentrations decreased in 79% of streams, and SO42− concentrations in streams continued to show strong decreases but remained several times higher than concentrations in precipitation. These changes were ongoing through 2022 even though acidic deposition levels were approaching those estimated for pre-industrialization. Further dilution is continuing through ongoing decreases in stream SO42−. Nevertheless, Ca2+ continued to be leached from soils by SO42−, organic acids and NO3−, limiting the replenishment of available soil Ca2+, a prerequisite to stem further dilution of stream water.
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":70252011,"text":"70252011 - 2024 - Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA","interactions":[],"lastModifiedDate":"2024-03-11T12:18:44.85142","indexId":"70252011","displayToPublicDate":"2024-01-06T07:15:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1517,"text":"Engineering Geology","active":true,"publicationSubtype":{"id":10}},"title":"Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA","docAbstract":"Lithology and geologic structure are important controls on landslide susceptibility and are incorporated into many regional landslide hazard models. Typically, metrics for mapped geologic units are used as model input variables and a single set of values for material strength are assumed, regardless of spatial heterogeneities that may exist within a map unit. Here we describe how differences in bedding thickness, grain size, inferred uniaxial compressive strength, and bedding dip control the inherent susceptibility of slopes to deep-seated failure within a single mapped geologic unit - the Tyee Formation of Oregon, USA. The Tyee, which covers over 15,000 km2 and underlies much of the Oregon Coast Range, comprises gently folded alternating beds of sandstone and siltstone deposited as turbidites, forming a 2-km thick Eocene submarine fan which has been uplifted and exhumed through the Cenozoic. Deep-seated landslides are widespread in the Tyee, but form a complex spatial pattern such that landslide density ranges from 0 to 24% of the total landscape area. These slides are often extensive and sufficiently deep to reduce local hillslope gradients, resulting in a strong negative correlation between landslide density and mean local slope. Mean annual precipitation and predicted strong ground motions from Cascadia earthquake scenarios also fail to explain the spatial distribution of deep-seated landslides. Consequently, landslide stability models, which are strongly influenced by landscape slope, pore-water pressure, and seismic acceleration, yield landslide susceptibility maps which are broadly anti-correlated with mapped deep-seated landslide density. Through a multivariable linear regression model, we show that much of the variance in deep-seated landslide density can be explained by variability of intra-unit stratigraphic and structural characteristics, which we measure at 128 sites across two study areas totaling ∼3000 km2. Our results suggest bedding dip is only weakly correlated to landslide density, but strongly influences landslide failure style. Subtle increases in bedding dip, even in the gently folded Tyee Formation, result in a substantially higher likelihood of a landslide being cataclinal, or parallel to bedding. Overall, we find a slight majority of landslides fail within these cataclinal slopes, and that these landslides tend to be larger than non-cataclinal landslides. We also show that the lithological and structural properties that influence landslide susceptibility are distinct for these two populations of landslides. Our results demonstrate how localized, intra-unit, geologic variability can exert strong control on landslide susceptibility and failure style. This suggests that in some locations, landslide hazard models could be significantly improved by incorporating detailed, spatially variable, geologic properties rather than relying solely on generalized geologic map units.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.enggeo.2023.107387","usgsCitation":"LaHusen, S.R., and Grant, A.R., 2024, Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA: Engineering Geology, v. 329, 107387, 14 p., https://doi.org/10.1016/j.enggeo.2023.107387.","productDescription":"107387, 14 p.","ipdsId":"IP-146959","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":440788,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.enggeo.2023.107387","text":"Publisher Index Page"},{"id":426489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tyee Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.10474453625554,\n 42.47204971687748\n ],\n [\n -121.72095547375605,\n 42.47204971687748\n ],\n [\n -121.72095547375605,\n 45.35210028381104\n ],\n [\n -125.10474453625554,\n 45.35210028381104\n ],\n [\n -125.10474453625554,\n 42.47204971687748\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"329","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":896262,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":896263,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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":70252027,"text":"70252027 - 2024 - Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador","interactions":[],"lastModifiedDate":"2024-03-11T12:02:54.212891","indexId":"70252027","displayToPublicDate":"2024-01-04T06:54:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador","docAbstract":"The failure of a 144-m-high lava-dam waterfall on the Río Coca, Ecuador, in February 2020 initiated a catastrophic watershed reset—regressive erosion upstream and a massive sediment pulse downstream—as the river evolves towards a new equilibrium grade. The evolution of this river corridor after a sudden base-level fall embodies the “complex response” concepts long understood through laboratory experiments, numerical modelling and smaller-scale field studies, but that have not been observed in the field before on this scale. This paper presents geomorphic and geotechnical data to characterize the evolution of the Río Coca since 2020. In the three years after the lava-dam failure, the erosion front migrated almost 13 km upstream along the mainstem river and triggered secondary headcuts that began migrating up tributaries. Erosion of the mainstem and tributary valleys generated a sediment pulse estimated to be 277 million m3 and ~500 million tonnes (Mt) over three years, depositing sediment tens of meters thick over tens of kilometres downstream from the former waterfall. This sediment pulse is one of the largest in modern times, comparable to the annual sediment load of a major continent-draining river but with orders-of-magnitude greater sediment yield. Geomorphic adjustment of the Río Coca represents a highly unusual natural disaster threatening life, property, water quality, the regional economy, major infrastructure and energy security. However, this event also provides a rare opportunity to learn how a large autogenic watershed disturbance and recovery evolve, with important lessons for interpreting the sedimentary record of volcanic landscapes.
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":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","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":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891902,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249904,"text":"70249904 - 2024 - Drought prediction and water availability: A report on the 2022 USGS-NIDIS National Listening Session Series","interactions":[],"lastModifiedDate":"2024-04-01T17:30:03.617149","indexId":"70249904","displayToPublicDate":"2024-01-01T12:27:08","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Drought prediction and water availability: A report on the 2022 USGS-NIDIS National Listening Session Series","docAbstract":"The U.S. Geological Survey (USGS) and NOAA’s National Integrated Drought Information System (NIDIS) conducted a series of four Listening Sessions in 2022 – each with a different application or topical focus – to seek input on priorities and needs related to predicting water availability changes under drought conditions at national and regional scales. This input was gathered to help inform the USGS Drought Program, regional and national drought efforts at NIDIS, and other national drought efforts. The series started with a February 2022 kick-off that introduced the series of Listening Sessions being held from March through September 2022. This kickoff also provided an overview of the USGS Drought Program’s work to characterize hydrological (e.g., streamflow and groundwater) drought, drought variability, drivers, and trends over the past century. Participants in these Listening Sessions included diverse stakeholder representation and perspectives.\n\nThe first of the four Listening Sessions focused on streamflow (March 3, 2022), and included a short introduction to the USGS national streamflow drought research, the properties of a national drought prediction system, as well as presentations by other agencies on different drought prediction and forecasting efforts. The second session focused on groundwater (May 5, 2022), and included presentations on groundwater drought, sustainable groundwater management, and improving our understanding of soil moisture, groundwater, and surface water drought. The third session focused on water use (July 14, 2022), and included a discussion of the different drought types, as well as an introduction to several key projects, including the USGS Upper Colorado River Basin Study, the Ogallala Data Directory project, and a multi-agency drought prediction partnership in Oklahoma. The fourth and final Listening Session focused on water availability prediction for ecosystems (September 8, 2022), and included presentations on the development of a national capacity for eco-hydrological and drought science, building climate resilience, and actionable ecodrought resources.","language":"English","publisher":"National Integrated Drought Information System","usgsCitation":"Skumanich, M., Smith, E., Lisonbee, J., and Hammond, J., 2024, Drought prediction and water availability: A report on the 2022 USGS-NIDIS National Listening Session Series, 24 p.","productDescription":"24 p.","ipdsId":"IP-153596","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":427276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":422385,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.drought.gov/documents/drought-prediction-and-water-availability-report-2022-usgs-nidis-national-listening","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skumanich, Marina","contributorId":260137,"corporation":false,"usgs":false,"family":"Skumanich","given":"Marina","email":"","affiliations":[{"id":52519,"text":"NOAA National Integrated Drought Information System","active":true,"usgs":false}],"preferred":false,"id":897766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Erik 0000-0001-8434-0798","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":221804,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lisonbee, Joel","contributorId":298624,"corporation":false,"usgs":false,"family":"Lisonbee","given":"Joel","email":"","affiliations":[{"id":64629,"text":"NOAA-NIDIS","active":true,"usgs":false}],"preferred":false,"id":897768,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887629,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259273,"text":"70259273 - 2024 - Broad scale community-level larval fish survey of southern Lake Erie","interactions":[],"lastModifiedDate":"2024-10-03T15:36:14.807593","indexId":"70259273","displayToPublicDate":"2024-01-01T10:26:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18728,"text":"Aquatic Ecosystem Health and Management","active":true,"publicationSubtype":{"id":10}},"title":"Broad scale community-level larval fish survey of southern Lake Erie","docAbstract":"The early-life history stages of fish are sensitive to environmental change and therefore can indicate habitat quality as well as help predict recruitment of resident and transient fishes. In 2019, as part of the Lake Erie Cooperative Science and Monitoring Initiative, we conducted a lake-wide assessment of the ichthyoplankton community in U.S. nearshore waters and international offshore waters. The goal of this work was to characterize the larval fish community across the lake and assess species composition, phenology, and distribution of larvae. Ichthyoplankton were sampled weekly using bongo nets at ports beginning at the Detroit River and along the southern shore of Lake Erie to Dunkirk, NY, and less frequently in the Niagara River and offshore areas. Larval fish were present from March 26 through August 29, 2019. The first taxon to emerge was Lake Whitefish in all basins, followed by Walleye, Yellow Perch, and catostomids, depending on port. Mean total density peaked in mid-June due to high catches of Gizzard Shad, Morone spp., and Freshwater Drum in the western basin. Few fish were collected in the offshore sites. Taxa richness, diversity, and larval density were higher in the western basin and lower in the central and eastern basins, generally following the productivity gradient. This was the first study to provide a comprehensive community assessment of the ichthyoplankton community of Lake Erie and can provide a baseline to assess future change, especially in community composition or phenology, of larvae which are likely to respond to climate and habitat change.
","language":"English","publisher":"Michigan State University Press","doi":"10.14321/aehm.027.01.98","usgsCitation":"DeBruyne, R.L., Amidon, Z., Angelosanto, M.J., Eberly, E.A., Gorsky, D., Ireland, S., Mayer, C., Provo, S., VanScoyoc, H., Watkins, J.M., and Roseman, E., 2024, Broad scale community-level larval fish survey of southern Lake Erie: Aquatic Ecosystem Health and Management, v. 27, p. 97-114, https://doi.org/10.14321/aehm.027.01.98.","productDescription":"18 p.","startPage":"97","endPage":"114","ipdsId":"IP-151982","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":462544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 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sireland@usgs.gov","orcid":"https://orcid.org/0000-0001-8568-8980","contributorId":215595,"corporation":false,"usgs":true,"family":"Ireland","given":"Stacey","email":"sireland@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":914745,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mayer, Christine","contributorId":237769,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","affiliations":[{"id":47604,"text":"University of Toledo, Lake Erie Center","active":true,"usgs":false}],"preferred":false,"id":914746,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Provo, Stacy","contributorId":139756,"corporation":false,"usgs":false,"family":"Provo","given":"Stacy","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":914747,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"VanScoyoc, Haley","contributorId":344799,"corporation":false,"usgs":false,"family":"VanScoyoc","given":"Haley","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":914748,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":914749,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":914750,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70261588,"text":"70261588 - 2024 - Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)","interactions":[],"lastModifiedDate":"2024-12-16T15:32:25.983624","indexId":"70261588","displayToPublicDate":"2024-01-01T09:26:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)","docAbstract":"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.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-24-0003.1","usgsCitation":"Oakley, N.S., Perkins, J.P., Bartlett, S.M., Collins, B.D., Comstock, K.H., Brien, D.L., Burgess, W., and Corbett, S.C., 2024, Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA): Earth Interactions, v. 28, no. 1, e240003, 19 p., https://doi.org/10.1175/EI-D-24-0003.1.","productDescription":"e240003, 19 p.","ipdsId":"IP-157035","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":363,"text":"Landslide Hazards Program","active":false,"usgs":true}],"links":[{"id":467041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-24-0003.1","text":"Publisher Index Page"},{"id":465146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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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":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892730,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259275,"text":"70259275 - 2024 - Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis","interactions":[],"lastModifiedDate":"2024-10-03T13:31:43.005985","indexId":"70259275","displayToPublicDate":"2024-01-01T08:28:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18728,"text":"Aquatic Ecosystem Health and Management","active":true,"publicationSubtype":{"id":10}},"title":"Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis","docAbstract":"As with other large lake ecosystems worldwide, Lake Erie can be considered a moving target for management, owing to physicochemical and biological changes brought on by anthropogenic change, both planned (e.g. nutrient and fisheries management) and unplanned (e.g. climate change, invasive species, modified land-use activities). These changes have challenged efforts to conserve biodiversity, sustain exploitable resources, and maintain the integrity of services valued by society both within the Lake Erie basin and (Fraker et al., 2022; Fussell et al., 2016; Sinclair et al., 2021; Sinclair et al., 2023) and outside of it (Allan et al., 2013; Jenny et al., 2020; Sterner et al., 2017). Some of these changes and their ramifications for management were documented in the first of four AEHM special issues devoted to the Lake Erie ecosystem (the fourth issue of 2023, volume 26, issue 4; see overview by Ludsin et al., 2023). That special issue focused explicitly on nutrient inputs and availability in Lake Erie and the lower food web, including planktonic and benthic microbial (including cyanobacteria), algal, and invasive dreissenid mussel communities. Similar to the previous Lake Erie special issue, this second one has focused on documenting the state of the lake, providing ecological understanding that could potentially benefit management. While some overlap in topics exists between issues, the studies conducted herein were completely independent of those previous investigations and offer unique insights. Specifically, the contributions to this current issue center on: 1) dynamics of water chemistry in Lake Erie’s central basin (i.e. bottom hypoxia; Ackerman et al., 2024) and western basin (i.e. mercury; Starr et al., 2024); 2) changes in primary producer biomass (Lesht et al., 2024), cyanotoxins (i.e. microcystin; Zastepa et al., 2024), and water quality (e.g. water clarity and dissolved nutrients; Howell et al., 2024); and 3) larval fish foraging (i.e. Lake Whitefish; Coregonus clupeaformis; Amidon et al., 2024) and community structure and phenology (DeBruyne et al., 2024). Below we summarize the major findings of these papers and offer a synthetic perspective on the value of this research for understanding the state of Lake Erie and enhancing management.
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Todd","contributorId":344802,"corporation":false,"usgs":false,"family":"Howell","given":"E.","email":"","middleInitial":"Todd","affiliations":[{"id":82411,"text":"Ontario Ministry of the Environment, Conservation, and Parks","active":true,"usgs":false}],"preferred":false,"id":914754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyson, Jeffrey","contributorId":344803,"corporation":false,"usgs":false,"family":"Tyson","given":"Jeffrey","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":914755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":914756,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"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.
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