The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.
The United States (US) National Park Service (NPS) manages protected public lands to preserve biodiversity. Exposure to and effects of bioactive organic contaminants in NPS streams are challenges for resource managers. Recent assessment of pesticides and pharmaceuticals in protected-streams within the urbanized NPS Southeast Region (SER) indicated the importance of fluvial inflows from external sources as drivers of aquatic contaminant-mixture exposures. Great Smoky Mountains National Park (GRSM), lies within SER, has the highest biodiversity and annual visitation of NPS parks, but, in contrast to the previously studied systems, straddles a high-elevation hydrologic divide; this setting limits fluvial-inflows of contaminants but potentially increases visitation-driven contaminant deliveries. We leveraged the unique characteristics of GRSM to test further the importance of fluvial contaminant inflows as drivers of protected-stream exposures and to inform the relative importance of potential additional contaminant transport mechanisms, by comparing the estimated risks of 328 pesticides and pharmaceuticals in water at 16 GRSM stream locations to those estimated previously in SER streams. Extensive mixtures (31 compounds) were only observed in an atypical reach on the boundary of GRSM downstream of a wastewater discharge, while limited mixtures (2–5 compounds) were observed in one stream with elevated visitation pressure (recreational “tube floating”). The insecticide, imidacloprid, used to eradicate hemlock woolly adelgid, was detected in 8 (50%) streams. Infrequent exceedances of a cumulative ToxCast-based, exposure-activity ratio (ΣEAR) 0.001 screening-level of concern suggested limited risk to non-target, aquatic vertebrates, whereas exceedances of a cumulative benchmark-based, invertebrate toxicity quotient (ΣTQ) 0.1 screening level at 8 locations indicated generally high risk to invertebrates. The results are consistent with the importance of fluvial transport from extra-park sources as a driver of bioactive-contaminant mixture exposures in protected streams and illustrate the potential additional risks from visitation-driven and tactical-use-pesticides.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.146711","usgsCitation":"Bradley, P., Kulp, M.A., Huffman, B.J., Romanok, K., Smalling, K., Breitmeyer, S.E., Clark, J., and Journey, C., 2021, Reconnaissance of cumulative risk of pesticides and pharmaceuticals in Great Smoky Mountains National Park streams: Science of the Total Environment, v. 781, 146711, 9 p., https://doi.org/10.1016/j.scitotenv.2021.146711.","productDescription":"146711, 9 p.","onlineOnly":"N","ipdsId":"IP-117880","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":436436,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GUEIMD","text":"USGS data release","linkHelpText":"Pesticide and Pharmaceutical Exposure Data for Select Streams within Great Smoky Mountains National Park, 2019"},{"id":384693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Great Smokey Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.166259765625,\n 35.03899204678081\n ],\n [\n -82.81494140625,\n 35.03899204678081\n ],\n [\n -82.81494140625,\n 35.782170703266075\n ],\n [\n -84.166259765625,\n 35.782170703266075\n ],\n [\n -84.166259765625,\n 35.03899204678081\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"781","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":813009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huffman, Bradley J. 0000-0003-2827-8074","orcid":"https://orcid.org/0000-0003-2827-8074","contributorId":220344,"corporation":false,"usgs":true,"family":"Huffman","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":813012,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clark, Jimmy 0000-0002-3138-5738","orcid":"https://orcid.org/0000-0002-3138-5738","contributorId":221235,"corporation":false,"usgs":true,"family":"Clark","given":"Jimmy","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813013,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Journey, Celeste A. 0000-0002-2284-5851","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":221232,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813014,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231210,"text":"70231210 - 2021 - Forecasting ecological responses for wetland restoration planning in Florida's Everglades","interactions":[],"lastModifiedDate":"2022-05-04T13:25:44.781534","indexId":"70231210","displayToPublicDate":"2021-03-24T06:46:04","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Forecasting ecological responses for wetland restoration planning in Florida's Everglades","docAbstract":"The Everglades wetland was once a river of grass, with water flowing slowly through the sawgrass, southward across the landscape. As developers took hold of south Florida, water was sent away from the heart of the Everglades through canals and levees to protect the former wetland for residential and agricultural development. In the 1990s, planning began to restore the Everglades in what is the largest hydrologic restoration undertaking in the world. With billions of taxpayer dollars at stake, restoration planners benefit from forecasting tools to inform restoration planning. To meet this need, scientists developed predictive ecological models and other decision support tools tailored to this dynamic ecosystem as well as to the needs of restoration planning teams. Predictive modeling has been able to take advantage of well-understood relationships between species of interest and hydrologic dynamics in the Everglades. Recent modeling advances include multi-species approaches that consider interactions among species as well as explicit consideration of trade-offs among species from potential water management actions. Scientists are also starting to look at ecosystem-wide vulnerabilities with explicit consideration of future change such as sea level rise. Modeling tools and approaches continue to be refined to meet decision making needs for Everglades restoration. However, more work is needed to consider additional complexities of this dynamic wetland as well as to consider the broader socio-environmental system.
In the humid, temperate Delaware River Basin (DRB) where water availability is generally reliable, summer low flows can cause competition between various human and ecological water uses. As temperatures continue to rise, population increases and development expands, it is critical to understand historical low flow variability to anticipate and plan for future flows. Using a sample of 325 U.S. Geological Survey gages, we evaluated spatial patterns in several low flow metrics, the biophysical and climatic drivers of these metrics, and trends in low flows for two periods: 1950-2018 and 1980-2018. We calculated the annual 7-day low flow and date, low flow deficit as the departure below a long-term daily flow threshold and the number of discrete low flow periods below this threshold. We also aggregated several climate metrics to watershed scale and used existing watershed properties quantifying land cover, topography, soils, geology, and human activity. Random forest models were used to assess the hierarchy of variable importance in explaining mean-annual low flow variability for each low flow metric using all gages. We find muted regional patterns in mean-annual low flow and low flow variability, likely due to the myriad of anthropogenic, landscape, and flow modifications that obscure flow regimes from their natural characteristics. In contrast, individual years show markedly different spatial patterns in low flow magnitude and severity. Coincident with increases in precipitation, 7-day low flows have generally increased and low flow deficits decreased for both 1950-2018 and 1980-2018 periods. However, 7-day low flows have decreased in the Coastal Plain physiographic province where water use and impervious area have increased in recent decades, highlighting the effects of land and water management on low flows. With continued change expected in the DRB, additional research needs are highlighted to enable estimation of future low flows and to plan for periods of prolonged low flow.
A multi-component geochemical dataset was collected from groundwater and surface-water bodies associated with the urban Fountain Creek alluvial aquifer, Colorado, USA, to facilitate analysis of recharge sources, geochemical interactions, and groundwater-residence times. Results indicate that groundwater can be separated into three distinct geochemical zones based on location within the flow system and proximity to surface water, and these zones can be used to infer sources of recharge and groundwater movement through the aquifer. Rare-earth-element concentrations and detections of wastewater-indicator compounds indicate the presence of effluent from wastewater-treatment plants in both groundwater and surface water. Effluent presence in groundwater indicates that streams in the area lose to groundwater in some seasons and are a source of focused groundwater recharge. Distributions of pharmaceuticals and wastewater-indicator compounds also inform an understanding of groundwater–surface-water interactions. Noble-gas isotopes corroborate rare-earth-element data in indicating geochemical evolution within the aquifer from recharge area to discharge area and qualitatively indicate variable groundwater-residence times and mixing with pre-modern groundwater. Quantitative groundwater-residence times calculated from 3H/3He, SF6, and lumped-parameter modeling generally are less than 20 years, but the presence of mixing with older groundwater of an unknown age is also indicated at selected locations. Future investigations would benefit by including groundwater-age tracers suited to quantification of mixing for both young (years to decades) and old (centuries and millennia) groundwater. This multi-faceted analysis facilitated development of a conceptual model for the investigated groundwater-flow system and illustrates the application of an encompassing suite of analytes in exploring hydrologic and geochemical interactions in complex systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w13060871","usgsCitation":"Newman, C.P., Paschke, S.S., and Keith, G.L., 2021, Natural and anthropogenic geochemical tracers to investigate residence times and groundwater–surface-water interactions in an urban alluvial aquifer: Water, v. 13, no. 6, 30 p., https://doi.org/10.3390/w13060871.","productDescription":"30 p.","ipdsId":"IP-118155","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":452974,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13060871","text":"Publisher Index Page"},{"id":436443,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99SPQM2","text":"USGS data release","linkHelpText":"Environmental-tracer modeling to support hydrogeochemical evaluation of the Fountain Creek Alluvial Aquifer, El Paso County, Colorado, 2018-2019"},{"id":384712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Colorado Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.2490234375,\n 38.61687046392973\n ],\n [\n -104.1888427734375,\n 38.61687046392973\n ],\n [\n -104.1888427734375,\n 39.16839998800286\n ],\n [\n -105.2490234375,\n 39.16839998800286\n ],\n [\n -105.2490234375,\n 38.61687046392973\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paschke, Suzanne S. 0000-0002-3471-4242 spaschke@usgs.gov","orcid":"https://orcid.org/0000-0002-3471-4242","contributorId":1347,"corporation":false,"usgs":true,"family":"Paschke","given":"Suzanne","email":"spaschke@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keith, Gabrielle L. 0000-0002-2304-8504 gkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-2304-8504","contributorId":256699,"corporation":false,"usgs":true,"family":"Keith","given":"Gabrielle","email":"gkeith@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813077,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219088,"text":"70219088 - 2021 - Mapping climate change vulnerability of aquatic-riparian ecosystems using decision-relevant indicators","interactions":[],"lastModifiedDate":"2021-03-23T13:13:09.442898","indexId":"70219088","displayToPublicDate":"2021-03-22T08:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Mapping climate change vulnerability of aquatic-riparian ecosystems using decision-relevant indicators","docAbstract":"Climate change has and is projected to continue to alter historical regimes of temperature, precipitation, and hydrology. To assess the vulnerability of climate change from a land management perspective and spatially identify where the most extreme changes are anticipated to occur, we worked in collaboration with land managers to develop a climate change vulnerability map for the midwestern United States with a focus on riparian systems. The map is intended for use by regional administrators to help them work across various program areas (e.g. fisheries, endangered species) to prioritize locations needing support for adaptation planning. The tool can also be utilized locally by managers to better understand the effects that projected climate scenarios have on the hydrology of management units as they develop adaptation strategies. The vulnerability map is watershed-based (360 watershed units within the region) and combines 15 climate change indicators that were selected by U.S. Fish and Wildlife Service natural resource managers based upon known and anticipated effects to species and habitats. The projected change in each of these indicators from the historical period (1986–2005) to the future period (2040–2059) was aggregated into a composite score for each watershed. Landscape-scale metrics reflective of a watershed’s adaptive capacity were combined with the climate change indicators to produce a vulnerability score. We found sub-regional variation in vulnerability to climate change with the greatest vulnerability in Iowa, central Illinois, and northwest Ohio. Greater vulnerability was seen in the higher greenhouse gas concentration scenario, Representative Concentration Pathway (RCP) 8.5 compared to the lower greenhouse gas concentration scenario RCP 4.5, when looking at the mean of the five downscaled climate models used in this study. By quantifying and mapping climate change vulnerability, natural resource managers can better understand the degree of vulnerability for individual watersheds and identify areas of prioritization in regional and local planning efforts.
The University of Kentucky (U KY) has owned Robinson Forest (37.460723° N, 83.158598° W) since 1923, conducting experiments crucial to understanding the environmental effects of land management in the region. Part of the management of Robinson Forest has been collection of environmental data, including precipitation quantity, bulk‐deposition chemistry, streamflow, stream‐water chemistry, and air and stream temperature. Over the years, these data have been collected and archived using various technologies and have been mostly inaccessible for research use – unedited and uncompiled, scattered across several spreadsheets and paper records. Through a partnership between the U.S. Geological Survey (USGS) and U KY, daily precipitation data for six stations and stream data from four watersheds in Robinson Forest have been compiled for 1971–2018, checked for transcription errors, and annotated for changes in methodologies. These data are available as a USGS data release at https://doi.org/10.5066/P9FPLG1O. Improved accessibility of this data set provides an important research resource for understanding water quality in minimally effected forests in the region. Preliminary results indicate that these data present a valuable opportunity to evaluate linkages among atmospheric deposition and stream chemistry, the effects of environmental policy, such as the Clean Air Act, and effects from nearby land disturbance in the form of surface mining. Furthermore, these data fill a geographic and physiographic gap in what is available to examine deposition and streamflow patterns over the last 45 years, supplementing those long‐term records of research sites in northern (e.g., Hubbard Brook Experimental Forest), central (e.g., Fernow Experimental Forest) and southern Appalachia (e.g., Coweeta Hydrologic Laboratory). As an oasis in the midst of significant surface mining activity, Robinson Forest presents a unique opportunity to understand environmental conditions characteristic of minimally disturbed forests similar to pre‐mining conditions in the Central Appalachian region.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14133","usgsCitation":"Sena, K., Barton, C.D., and Williamson, T.N., 2021, The Robinson Forest environmental monitoring network: Long‐term evaluation of streamflow and precipitation quantity and stream‐water and bulk deposition chemistry in eastern Kentucky watersheds: Hydrological Processes, v. 35, no. 4, e14133, 6 p., https://doi.org/10.1002/hyp.14133.","productDescription":"e14133, 6 p.","ipdsId":"IP-122607","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":385638,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"southeast Kentucky","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.27636718749999,\n 36.756490329505176\n ],\n [\n -81.36474609375,\n 36.756490329505176\n ],\n [\n -81.36474609375,\n 37.82280243352756\n ],\n [\n -83.27636718749999,\n 37.82280243352756\n ],\n [\n -83.27636718749999,\n 36.756490329505176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Sena, Kenton 0000-0003-1822-9375","orcid":"https://orcid.org/0000-0003-1822-9375","contributorId":258046,"corporation":false,"usgs":false,"family":"Sena","given":"Kenton","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":815604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barton, Chris D. 0000-0003-0692-3079","orcid":"https://orcid.org/0000-0003-0692-3079","contributorId":236883,"corporation":false,"usgs":false,"family":"Barton","given":"Chris","email":"","middleInitial":"D.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":815605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Tanja N. 0000-0002-7639-8495 tnwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-7639-8495","contributorId":198329,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja","email":"tnwillia@usgs.gov","middleInitial":"N.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815606,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241892,"text":"70241892 - 2021 - A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions","interactions":[],"lastModifiedDate":"2023-03-30T12:08:50.610487","indexId":"70241892","displayToPublicDate":"2021-03-18T07:06:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1927,"text":"Hydrological Sciences Journal","active":true,"publicationSubtype":{"id":10}},"title":"A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions","docAbstract":"Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with cross-correlation in two fundamentally different ways and therefore might be associated with different accuracy and uncertainty of predicted flood quantiles. We perform a Monte Carlo experiment based on a dataset of annual maximum flood series for 20 catchments in a hydrologically homogeneous region. Based on a log-Pearson type III parent distribution, we generate 3000 realizations of the region with different degrees of cross-correlation. For each realization, GLS and TK are applied in leave-one-out cross-validation to predict at-site flood quantiles. Our study shows that (a) TK outperforms GLS when catchment area is the only catchment descriptor used for predicting “true” population (theoretical) flood quantiles, regardless of the level of cross-correlation, and (b) GLS and TK perform similarly when multiple catchment descriptors are used.
Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.
Hydroacoustics have revolutionized how the U.S. Geological Survey (USGS) measures streamflow by increasing the efficiency and quality of the measurement. However, the ability to determine the full range of streamflow at a streamflow-gaging station remains limited because in-person flow measurements still must be made by qualified personnel. As a result, streamflow during flood events typically is measured infrequently in comparison to the duration of the event, usually after the peak flow has occurred. To overcome these difficulties, the USGS has developed the Trolley Operated Automatic Discharge System (TOADS), an automated system for measuring streamflow without the need for onsite personnel. Investment by USGS in TOADS and other innovative technologies and methods provides substantial improvements to flood assessment and watershed management, making the USGS the continued world leader in surface-water hydrology.
Streamflow measurements made with TOADS are analogous to a moving-boat measurement, which measures the flow at a point in a river by moving from bank to bank and measuring water velocities at various depths below the boat. The TOADS uses hydroacoustic technology to profile water velocity across a river while moving vertically through the water column to measure flow at multiple depths. Use of TOADS to measure streamflow can save substantial time and money, provide improved flow ratings by taking numerous targeted automated measurements over a range of conditions, and provide a safe alternative to standard boat measurements when river conditions are hazardous. The TOADS can be programmed to measure flow based on a variety of triggers (including river stage, amount of flow, time of day) and can take repeated measurements at user-specified intervals during floods, droughts, and other events of interest.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 1973 Oklahoma Water Law (82 OK Stat § 82-1020.5) requires that the Oklahoma Water Resources Board (OWRB) conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin (hereinafter referred to as an “aquifer”). The maximum annual yield allocated per acre of land is known as the equal-proportionate-share (EPS) pumping rate. At present (2021), the OWRB has not yet established a maximum annual yield and EPS pumping rate for the Salt Fork Red River aquifer. To provide updated information to the OWRB that could support evaluation and determination of an appropriate maximum annual yield, the U.S. Geological Survey (USGS), in cooperation with the OWRB, conducted a hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater availability in the Salt Fork Red River aquifer.
The Salt Fork Red River aquifer in Greer, Harmon, and Jackson Counties of southwestern Oklahoma is composed of about 274.5 square miles of alluvium and terrace deposits associated with the Salt Fork Red River. The mean annual recharge rate to the Salt Fork Red River aquifer for the period 1980–2015 was estimated to be about 2.94 inches per year, or 10.0 percent of the mean annual precipitation for the same period (29.4 inches per year). This 1980–2015 mean annual recharge rate is equivalent to a mean annual recharge rate of about 38,000 acre-feet per year (acre-ft/yr) for the Salt Fork Red River aquifer excluding about 19,764 acres comprising the Mulberry Creek and Horse Creek terraces. The mean annual recharge rates upgradient and downgradient from USGS streamgage 07300500 Salt Fork Red River at Mangum, Okla. (hereinafter referred to as the “Mangum gage”), apportioned by aquifer area (41.5 and 58.5 percent, respectively), were about 16,000 and 22,000 acre-ft/yr, respectively. Mean annual groundwater use for the study period (1980–2015) was 3,532.7 acre-ft/yr; about 77 percent of that groundwater use was for irrigation, and about 23 percent was for public supply. Most groundwater use for irrigation was associated with wells in the Martha terrace.
A hydrogeologic framework was developed for the Salt Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of the numerical groundwater-flow model of the Salt Fork Red River aquifer described in this report. A conceptual model for the Salt Fork Red River aquifer that reasonably represents the groundwater-flow system was developed to constrain the construction and calibration of the numerical model. The conceptual-model water budget estimated mean annual inflows to, and outflows from, the Salt Fork Red River aquifer for the period 1980–2015 and included a subaccounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from the Mangum gage.
The numerical groundwater-flow model of the Salt Fork Red River aquifer was constructed by using MODFLOW-2005 with the Newton formulation solver. The model of the Salt Fork Red River aquifer was spatially discretized into 1,050 rows, 1,125 columns, about 170,000 active cells measuring 200 by 200 feet (ft), and a single convertible layer. The model was temporally discretized into 432 monthly transient stress periods (each with two time steps to improve model stability). An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer and produced a solution that was used as the initial condition for subsequent transient stress periods as well as some groundwater-availability scenarios. The model was calibrated to water-table-altitude observations at selected wells and base-flow observations at selected streamgages.
The simulated saturated thickness of the Salt Fork Red River aquifer was determined by subtracting the altitude of the aquifer base from the simulated water-table altitude at the end of the numerical-model period (2015). The simulated saturated thickness was more than 75 ft in a paleochannel in the Dodson terrace near the Texas border. The mean aquifer thickness (sum of saturated and unsaturated) was 49.62 ft, and the mean saturated thickness was 28.55 ft. A simulated mean transmissivity of 1,024 feet squared per day was computed from the calibrated hydraulic conductivity and saturated thickness of each cell. The simulated available water in storage at the end of the numerical-model period (2015) was 526,117 acre-feet (acre-ft); about 42 percent of that total was available upgradient from the Mangum gage, and about 58 percent of that total was available downgradient from the Mangum gage (including the Mangum terrace).
Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate the EPS pumping rate that ensures a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought on base flow and groundwater storage. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were about 0.51, 0.48, and 0.48 acre-foot per acre per year, respectively. Given the 155,929-acre modeled aquifer area, these rates correspond to annual yields of about 78,800, 74,900, and 74,700 acre-ft/yr, respectively. For the 20-year EPS scenario, decreasing and increasing recharge by 10 percent resulted in a 6-percent change in the EPS pumping rate in both cases; for the 40- and 50-year EPS scenarios, decreasing and increasing recharge by 10 percent resulted in a 7-percent change in the EPS pumping rate in both cases.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the Salt Fork Red River aquifer and base flows in the Salt Fork Red River. The effects of well withdrawals were evaluated by quantifying differences in groundwater storage and base flow in four 50-year scenarios, which applied (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2015), (3) 2015 pumping rates, and (4) increasing demand pumping rates at simulated wells. The increasing demand pumping rates assumed a cumulative 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwestern Oklahoma. Groundwater storage after 50 years with no pumping was 535,000 acre-ft, or 8,900 acre-ft (1.7 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-table-altitude increase of 0.48 ft. Groundwater storage after 50 years of pumping at the mean rate for the study period (1980–2015) was 519,900 acre-ft, or 6,200 acre-ft (1.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.34 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 513,100 acre-ft, or 13,000 acre-ft (2.5 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.71 ft. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was 509,700 acre-ft, or 16,500 acre-ft (3.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.89 ft.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage. The period January 1983–December 1992 was chosen as the simulated drought period. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought) at the end of the simulated drought period (1992). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1983–92). Upstream inflows from the Salt Fork Red River, Turkey Creek, and Bitter Creek were reduced by 75 percent. Groundwater storage at the end of the drought period (1992) was 479,200 acre-ft, or 53,200 acre-ft (10.0 percent) less than the groundwater storage of the calibrated numerical model at the end of the drought period. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 2.9 ft. At the end of the 10-year hypothetical drought period, simulated base flows at the Mangum gage and USGS streamgage 07301110 Salt Fork Red River near Elmer, Okla., had decreased by about 80 and 70 percent, respectively.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
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Austin, Texas 78754-4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The Panola Mountain Research Watershed (PMRW) is a 41‐hectare forested catchment within the Piedmont Province of the Southeastern United States. Observations, experimentation, and numerical modelling have been conducted at Panola over the past 35 years. But to date, these studies have not been fully incorporated into a more comprehensive synthesis. Here we describe the evolving perceptual understanding of streamflow generation mechanisms at the PMRW. We show how the long‐term study has enabled insights that were initially unforeseen but are also unachievable in short‐term studies. In particular, we discuss how the accumulation of field evidence, detailed site characterization, and modelling enabled a priori hypotheses to be formed, later rejected, and then further refined through repeated field campaigns. The extensive characterization of the soil and bedrock provided robust process insights not otherwise achievable from hydrometric measurements and numerical modelling alone. We focus on two major aspects of streamflow generation: the role of hillslopes (and their connection to the riparian zone) and the role of catchment storage in controlling fluxes and transit times of water in the catchment. Finally, we present location‐independent hypotheses based on our findings at PMRW and suggest ways to assess the representativeness of PMRW in the broader context of headwater watersheds.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14127","usgsCitation":"Aulenbach, B.T., Hooper, R.P., van Meerveld, H.J., Burns, D., Freer, J.E., Shanley, J.B., Huntington, T., McDonnell, J.J., and Norman E. Peters, 2021, The evolving perceptual model of streamflow generation at the Panola Mountain Research Watershed: Hydrological Processes, v. 35, no. 4, e14127, 14 p., https://doi.org/10.1002/hyp.14127.","productDescription":"e14127, 14 p.","ipdsId":"IP-125152","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":385149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.78149414062499,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 33.25706340236547\n ],\n [\n -83.770751953125,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 34.288991865037524\n ],\n [\n -84.78149414062499,\n 33.25706340236547\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooper, Richard P 0000-0002-3329-9622","orcid":"https://orcid.org/0000-0002-3329-9622","contributorId":257488,"corporation":false,"usgs":false,"family":"Hooper","given":"Richard","email":"","middleInitial":"P","affiliations":[{"id":52045,"text":"Tufts University, Department of Civil and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":814372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Meerveld, H. J. 0000-0002-7547-3270","orcid":"https://orcid.org/0000-0002-7547-3270","contributorId":257489,"corporation":false,"usgs":false,"family":"van Meerveld","given":"H.","email":"","middleInitial":"J.","affiliations":[{"id":52048,"text":"University of Zurich, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":814373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freer, James E. 0000-0001-6388-7890","orcid":"https://orcid.org/0000-0001-6388-7890","contributorId":188139,"corporation":false,"usgs":false,"family":"Freer","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":814375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","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":814376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McDonnell, Jeffery J. 0000-0002-3880-3162","orcid":"https://orcid.org/0000-0002-3880-3162","contributorId":62723,"corporation":false,"usgs":false,"family":"McDonnell","given":"Jeffery","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":814378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Norman E. Peters 0000-0002-0637-9424","orcid":"https://orcid.org/0000-0002-0637-9424","contributorId":207130,"corporation":false,"usgs":false,"family":"Norman E. Peters","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":814379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223354,"text":"70223354 - 2021 - Landscape level effects of invasive plants and animals on water infiltration through Hawaiian tropical forests","interactions":[],"lastModifiedDate":"2021-08-24T12:41:21.483952","indexId":"70223354","displayToPublicDate":"2021-03-13T07:39:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Landscape level effects of invasive plants and animals on water infiltration through Hawaiian tropical forests","docAbstract":"Watershed degradation due to invasion threatens downstream water flows and associated ecosystem services. While this topic has been studied across landscapes that have undergone invasive-driven state changes (e.g., native forest to invaded grassland), it is less well understood in ecosystems experiencing within-system invasion (e.g. native forest to invaded forest). To address this subject, we conducted an integrated ecological and ecohydrological study in tropical forests impacted by invasive plants and animals. We measured soil infiltration capacity in multiple fenced (i.e., ungulate-free)/unfenced and native/invaded forest site pairs along moisture and substrate age gradients across Hawaii to explore the effects of invasion on hydrological processes within tropical forests. We also characterized forest composition, structure and soil characteristics at these sites to assess the direct and vegetation-mediated impacts of invasive species on infiltration capacity. Our models show that invasive ungulates negatively affect soil infiltration capacity consistently across the wide moisture and substrate age gradients considered. Additionally, several soil characteristics known to be affected by invasive ungulates were associated with local infiltration rates, indicating that the long-term secondary effects of high ungulate densities in tropical forests may be stronger than effects observed in this study. The effect of invasive plants on infiltration was complex and likely to depend on their physiognomy within existing forest community structure. These results provide clear evidence for managers that invasive ungulate control efforts can improve ecohydrological function of mesic and wet forest systems critical to protecting downstream and nearshore resources and maintaining groundwater recharge.
Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
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dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":843234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buchanan, Claire 0000-0001-5627-448X","orcid":"https://orcid.org/0000-0001-5627-448X","contributorId":291854,"corporation":false,"usgs":false,"family":"Buchanan","given":"Claire","email":"","affiliations":[{"id":39005,"text":"ICPRB","active":true,"usgs":false}],"preferred":false,"id":843235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rapp, Jennifer L. 0000-0003-2253-9886 jrapp@usgs.gov","orcid":"https://orcid.org/0000-0003-2253-9886","contributorId":197342,"corporation":false,"usgs":true,"family":"Rapp","given":"Jennifer","email":"jrapp@usgs.gov","middleInitial":"L.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":false,"id":843236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Austin, Samuel H. 0000-0001-5626-023X saustin@usgs.gov","orcid":"https://orcid.org/0000-0001-5626-023X","contributorId":153,"corporation":false,"usgs":true,"family":"Austin","given":"Samuel","email":"saustin@usgs.gov","middleInitial":"H.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":843237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":843238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":843239,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225526,"text":"70225526 - 2021 - Pore water exchange-driven inorganic carbon export from intertidal salt marshes","interactions":[],"lastModifiedDate":"2021-10-20T13:06:17.578828","indexId":"70225526","displayToPublicDate":"2021-03-11T08:02:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Pore water exchange-driven inorganic carbon export from intertidal salt marshes","docAbstract":"Respiration in intertidal salt marshes generates dissolved inorganic carbon (DIC) that is exported to the coastal ocean by tidal exchange with the marsh platform. Understanding the link between physical drivers of water exchange and chemical flux is a key to constraining coastal wetland contributions to regional carbon budgets. The spatial and temporal (seasonal, annual) variability of marsh pore water exchange and DIC export was assessed from a microtidal salt marsh (Sage Lot Pond, Massachusetts). Spatial variability was constrained from 224Ra : 228Th disequilibria across two hydrologic units within the marsh sediments. Disequilibrium between the more soluble 224Ra and its sediment-bound parent 228Th reveals significant pore water exchange in the upper 5 cm of the marsh surface (0–36 L m−2 d−1) that is most intense in low marsh elevation zones, driven by tidal overtopping. Surficial sediment DIC transport ranges from 0.0 to 0.7 g C m−2 d−1. The sub-surface sediment horizon intersected by mean low tide was disproportionately impacted by tidal pumping (20–80 L m−2 d−1) and supplied a seasonal DIC flux of 1.7–5.4 g C m−2 d−1. Export exceeded 10 g C m−2 d−1 for another marsh unit, demonstrating that fluxes can vary substantially across salt marshes under similar conditions within the same estuary. Seasonal and annual variability in marsh pore water exchange, constrained from tidal time-series of radium isotopes, was driven in part by variability in mean sea level. Rising sea levels will further inundate high marsh elevation zones, which may lead to greater DIC export.
In coarse resolution hydrological modeling we face the problem of subgrid variability, the effects of which are difficult to express and are often hidden in the parameterization and calibration. We present a numerical experiment with the physically based hydrological model ParFlow‐CLM with which we quantify the effect of subgrid heterogeneities in headwater catchments within the cell size typically used for regional hydrological applications. We simulate homogeneous domains and domains with subgrid heterogeneities in topography or soil thickness for two climates and soil types. The presence of side slope is the main error source, leading to large underestimation of runoff, and marginally also of evapotranspiration. The spatial distribution of soil saturation in the presence of subgrid variability in topography also leads to underestimation of landslide risk. Soil thickness is the second influential subgrid property, affecting soil moisture distribution and surface runoff formation. Results are consistent for the climates and the soil types considered. The topographic wetness index approach is tested as a way to downscale soil moisture simulations within the domain. Although this method is successful in reproducing some spatial variability and patterns, it fails when the coarse grid mean soil saturation is inaccurate or subgrid topography does not represent subsurface flow paths accurately. We conclude that ignoring subgrid variability in topography and soil thickness in coarse‐scale hydrological models may lead locally to underestimation of runoff and slope instability. Users of such models should be aware of these biases and consider ways to include subgrid effects in coarse‐scale hydrological predictions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027326","usgsCitation":"Leonarduzzi, E., Maxwell, R.M., Mirus, B.B., and Molnar, P., 2021, Numerical analysis of the effect of subgrid variability in a physically based hydrological model on runoff, soil moisture, and slope stability: Water Resources Research, v. 57, no. 4, e2020WR027326, 16 p., https://doi.org/10.1029/2020WR027326.","productDescription":"e2020WR027326, 16 p.","ipdsId":"IP-124808","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453131,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020wr027326","text":"External Repository"},{"id":384495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonarduzzi, E. 0000-0002-6811-9118","orcid":"https://orcid.org/0000-0002-6811-9118","contributorId":255523,"corporation":false,"usgs":false,"family":"Leonarduzzi","given":"E.","email":"","affiliations":[{"id":51571,"text":"Institute of Environmental Engineering, ETH Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":812489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maxwell, R. M.","contributorId":255524,"corporation":false,"usgs":false,"family":"Maxwell","given":"R.","email":"","middleInitial":"M.","affiliations":[{"id":51573,"text":"Integrated Groundwater Modeling Center and Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":812490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molnar, P. 0000-0001-6437-4931","orcid":"https://orcid.org/0000-0001-6437-4931","contributorId":255525,"corporation":false,"usgs":false,"family":"Molnar","given":"P.","email":"","affiliations":[{"id":51575,"text":"Institute of Environmental Engineering, ETH Zurich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":812492,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219479,"text":"70219479 - 2021 - Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom","interactions":[],"lastModifiedDate":"2021-04-12T11:50:22.717788","indexId":"70219479","displayToPublicDate":"2021-03-07T07:20:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom","docAbstract":"Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.
In this work we present an integrated approach to assess the spatial scaling behavior of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30% to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80%–95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error over the entire country.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028076","usgsCitation":"Formetta, G., Over, T.M., and Stewart, E., 2021, Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom: Water Resources Research, v. 57, no. 4, e2020WR028076, 21 p., https://doi.org/10.1029/2020WR028076.","productDescription":"e2020WR028076, 21 p.","ipdsId":"IP-119682","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/529960/1/N529960PP.pdf","text":"External Repository"},{"id":384966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -5.712890625,\n 49.61070993807422\n ],\n [\n -2.28515625,\n 50.064191736659104\n ],\n [\n 1.669921875,\n 50.84757295365389\n ],\n [\n 2.3291015625,\n 52.32191088594773\n ],\n [\n 0.9228515625,\n 54.826007999094955\n ],\n [\n -0.2197265625,\n 55.85064987433714\n ],\n [\n -0.791015625,\n 57.231502991478926\n ],\n [\n -1.142578125,\n 57.938183012205315\n ],\n [\n -2.548828125,\n 58.63121664342478\n ],\n [\n -4.130859375,\n 59.153403092050375\n ],\n [\n -6.767578125,\n 58.97266715450153\n ],\n [\n -8.1298828125,\n 56.24334992410525\n ],\n [\n -7.9541015625,\n 54.521081495443596\n ],\n [\n -7.0751953125,\n 54.059387886623576\n ],\n [\n -5.888671875,\n 53.409531853086435\n ],\n [\n -5.6689453125,\n 51.56341232867588\n ],\n [\n -6.1083984375,\n 50.233151832472245\n ],\n [\n -6.064453125,\n 49.55372551347579\n ],\n [\n -5.712890625,\n 49.61070993807422\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Formetta, Giuseppe 0000-0002-0252-1462","orcid":"https://orcid.org/0000-0002-0252-1462","contributorId":210296,"corporation":false,"usgs":false,"family":"Formetta","given":"Giuseppe","email":"","affiliations":[{"id":38100,"text":"Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO","active":true,"usgs":false}],"preferred":false,"id":813730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Elizabeth","contributorId":257050,"corporation":false,"usgs":false,"family":"Stewart","given":"Elizabeth","email":"","affiliations":[{"id":51971,"text":"UK Centre for Ecology & Hydrology","active":true,"usgs":false}],"preferred":false,"id":813732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221894,"text":"70221894 - 2021 - Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","interactions":[],"lastModifiedDate":"2021-07-13T18:35:29.258188","indexId":"70221894","displayToPublicDate":"2021-03-05T13:30:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3892,"text":"Ecohydrology & Hydrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","docAbstract":"Dissolved organic carbon (DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000).
In this study, we emphasized the simulation of streamflow and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 (Huntington and Aiken, 2013).
Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et al., 2010; Huntington and Aiken, 2013; Tian et al., 2013). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and biogeochemistry within similar large complex watersheds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecohyd.2021.02.005","usgsCitation":"Rouhani, S., Schaaf, C.B., Huntington, T., and Choate, J., 2021, Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine: Ecohydrology & Hydrobiology, v. 21, no. 23-24, p. 256-270, https://doi.org/10.1016/j.ecohyd.2021.02.005.","productDescription":"15 p.","startPage":"256","endPage":"270","ipdsId":"IP-106391","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecohyd.2021.02.005","text":"Publisher Index Page"},{"id":387156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.73046875,\n 44.48866833139464\n ],\n [\n -67.576904296875,\n 45.57560020947802\n ],\n [\n -68.5986328125,\n 46.255846818480315\n ],\n [\n -70.15869140625,\n 46.430285240839964\n ],\n [\n -70.37841796875,\n 45.78284835197676\n ],\n [\n -69.43359375,\n 45.874712248904764\n ],\n [\n -69.60937499999999,\n 45.36758436884978\n ],\n [\n -70.11474609375,\n 45.213003555993964\n ],\n [\n -69.345703125,\n 44.6061127451739\n ],\n [\n -68.73046875,\n 44.48866833139464\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"23-24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rouhani, Shabnam","contributorId":260994,"corporation":false,"usgs":false,"family":"Rouhani","given":"Shabnam","email":"","affiliations":[{"id":52735,"text":"University of Massachusetts, Boston, MA","active":true,"usgs":false}],"preferred":false,"id":819233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaaf, Crystal B.","contributorId":149538,"corporation":false,"usgs":false,"family":"Schaaf","given":"Crystal","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":819234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Janet","contributorId":260995,"corporation":false,"usgs":false,"family":"Choate","given":"Janet","email":"","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":819236,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219036,"text":"70219036 - 2021 - Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments","interactions":[],"lastModifiedDate":"2021-03-19T11:44:31.211077","indexId":"70219036","displayToPublicDate":"2021-03-05T06:32:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments","docAbstract":"Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.
","language":"English","publisher":"MDPI","doi":"10.3390/w13050713","usgsCitation":"Fusco, F., Mirus, B.B., Baum, R.L., Calcaterra, D., and De Vita, P., 2021, Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments: Water, v. 13, no. 5, 27 p., https://doi.org/10.3390/w13050713.","productDescription":"27 p.","ipdsId":"IP-120315","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13050713","text":"Publisher Index Page"},{"id":384490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Vesuvius","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.327545166015625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.75974059207392\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, F. 0000-0002-6271-2228","orcid":"https://orcid.org/0000-0002-6271-2228","contributorId":219005,"corporation":false,"usgs":false,"family":"Fusco","given":"F.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calcaterra, D. 0000-0002-3480-3667","orcid":"https://orcid.org/0000-0002-3480-3667","contributorId":219008,"corporation":false,"usgs":false,"family":"Calcaterra","given":"D.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Vita, P.","contributorId":219006,"corporation":false,"usgs":false,"family":"De Vita","given":"P.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812519,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219458,"text":"70219458 - 2021 - Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","interactions":[],"lastModifiedDate":"2021-04-08T12:47:24.059845","indexId":"70219458","displayToPublicDate":"2021-03-04T07:44:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","docAbstract":"Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0 mm/yr; peak −345 mm/yr) are twice that in 2017 (mean −20.4 mm/yr; peak −177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).
Repeat microgravity surveys carried out using relative- and absolute-gravity meters are useful for identifying changes in subsurface mass, such as the volume of water stored in an aquifer. These surveys require careful field procedures to achieve the part-per-billion accuracy required to measure the small changes in gravity relevant for hydrologic studies. This chapter describes techniques and methods for carrying out gravity surveys, requirements for assuring high-quality survey results, and data processing and archival procedures. The focus is on acquiring and documenting repeat gravity surveys for monitoring changes in groundwater storage. Similar gravity surveys may be completed to evaluate other causes of mass change, such as those caused by magma movement below volcanoes. The methods are also useful for one-time surveys that map spatial gravity variations associated with geologic structures such as faults or sedimentary basins.
Repeat microgravity surveys can be carried out using relative-gravity meters, absolute-gravity meters, or both. Specific locations, known as gravity stations, are visited during each survey. Most commonly, absolute- and relative-gravity are combined using the least-squares method of network adjustment, much like benchmark elevations and relative-height differences in a leveling network. This chapter primarily describes the use of the A-10 absolute-gravity meter manufactured by Micro-g LaCoste, Inc., and relative-gravity meters made by LaCoste & Romberg (no longer in production) and ZLS Corporation, Inc. Field and office procedures are similar for other instruments such as the FG-5 absolute-gravity meter and Scintrex relative-gravity meters, but some adaptation may be required. Quality control for absolute-gravity data focuses primarily on proper field procedures and maintaining the time and distance calibration of the instrument. Quality control for relative-gravity surveys requires careful field procedures, an understanding of how the meter is behaving while in the field, and appropriate postprocessing.
The techniques and methods described in this chapter were developed over 30 years at the USGS Arizona Water Science Center and the Southwest Gravity Program and are the basis for many studies on groundwater-storage change and geologic structure. A description of the Program and complete bibliography is available at https://www.usgs.gov/centers/az-water/science/azwsc-capabilities-hydrologic-gravity-monitoring.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The purpose of this data series is to compile information on the occurrence and conservation status of the freshwater mussel fauna of Nebraska, Kansas, and Oklahoma and to map the distribution of a freshwater mussel assemblage for the U.S. Department of the Interior, Bureau of Land Management Rapid Ecoregional Assessment (REA) program. The six focal species in the freshwater mussel assemblage are Amblema plicata (threeridge), Fusconaia flava (Wabash pigtoe), Lampsilis cardium (plain pocketbook), Lampsilis teres (yellow sandshell), Pyganodon grandis (giant floater), and Uniomerus tetralasmus (pondhorn). The focal species were selected using the following criteria: (1) the species are regionally significant, (2) occurrence records are sufficient to map the distribution of the species by hydrologic subbasins, (3) the assemblage includes species representing a range of State-level conservation priorities, and (4) the species are not listed as federally endangered or threatened. In addition, the species represent a broad array of life history strategies and habitat associations.
A total of 61 native species of freshwater mussels have documented occurrences within at least 1 of the 3 States, including 6 species that appear to have been extirpated from all the States and 6 species that may have been extirpated from at least 1 State. Of the 61 species, 8 species (including 3 potentially extirpated species) are listed as federally threatened or endangered and an additional 5 species are ranked as imperiled or vulnerable across their range. Approximately 80 percent of the native species known to have occurred within the three-State area have a secure conservation status, in comparison to only 40 percent of all freshwater mussel species or subspecies occurring within the United States. The compiled records for the contemporary period (1970–2017) documented the occurrence of 24 extant species in Nebraska, 42 in Kansas, and 48 in Oklahoma.
The contemporary distributions of the six focal species were mapped by subbasins and the larger hydrologic subregions. Historical records (prior to 1962) were also mapped but were limited. Amblema plicata, Fusconaia flava, and Lampsilis cardium were present in approximately one-third of all subbasins and slightly more than half of the subregions, primarily along the eastern portion of the three-State area. Lampsilis teres and Uniomerus tetralasmus were more widespread, occurring in close to half of the subbasins and about three-quarters of the subregions. Pyganodon grandis was the most widespread, occurring in about three-quarters of the subbasins and almost all subregions. There were very few subbasins with historical occurrences that lacked contemporary occurrences. The broad-scale distribution maps for the freshwater mussel assemblage presented with this report are intended to contribute baseline information for regional assessments, such as the Southern Great Plains Rapid Ecoregional Assessment. Despite the limitations of the available data, such baseline information can be useful for identifying data gaps, monitoring future trends, identifying conservation priorities, and providing the larger context for more detailed watershed- or catchment-level studies. ScienceBase data release files associated with this data series are available at https://doi.org/10.5066/P9SBFZJU (Fancher and Carr, 2021).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1133","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Carr, N.B., and Fancher, T.S., 2021, Compilation of information on occurrence and conservation status for the freshwater mussel fauna of Nebraska, Kansas, and Oklahoma: U.S. Geological Survey Data Series 1133, 22 p., https://doi.org/10.3133/ds1133.","productDescription":"Report: vi, 22 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119647","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":383657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1133/ds1133.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1133"},{"id":383666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBFZJU","text":"USGS data 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\"}}]}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Despite abundant research documenting that land use/land cover (LULC) have substantial impacts on the hydrology of humid tropical systems, field-based evidence for the physical mechanisms behind these impacts are still lacking. In particular, our understanding of the hydrologic flowpaths that generate runoff in these systems, and how they vary with respect to LULC is insufficient to inform both physically-based hydrologic modeling and land-use decision-making. In this study, we use end-member mixing analysis (EMMA) of stream chemistry, and hydrometric characterizations of hillslope soil moisture to identify hydrologic flowpaths in humid tropical steep-land catchments of varying LULC: mature tropical forest, young secondary tropical forest, cattle pasture. EMMA was applied to data from 14 storm events (six at the mature forest, five at the young secondary forest, and three at the cattle pasture) that were intensively sampled during the 2017 wet season representing a wide range of rainfall magnitudes and intensities. Additionally, volumetric-soil-moisture responses at multiple depths were characterized during and after 74 storm events occurring from 2015 to 2017. EMMA results indicated that lateral preferential flow within the top 30 cm of the soil profile was a dominant source of runoff generation at the two forested catchments, with the contribution of this flow path increasing with rainfall magnitude and intensity. This was corroborated by volumetric-soil-moisture data, that showed that a perched zone of saturation developed at 30 cm at the time of peak storm runoff during the largest events and lasted for the remaining duration of the event. EMMA indicated that runoff was a combination of infiltration-excess overland flow and lateral subsurface flow in the actively grazed pastoral catchment. There, overland flow contributed 62 % of runoff during the highest runoff rate sampled (35.3 mm/hr) and this contribution increased substantially with storm magnitude. This flowpath identification was also supported by volumetric-soil-moisture data at the pasture, with peak saturation at all depths during the largest storm events occurring up to 30 min after peak runoff. These results provide a mechanistic explanation for previously observed hydrologic differences among tropical LULCs. Additionally, the wide range of hydrologic conditions during these storm events provide a basis for understanding how future changes to this, and similar humid tropical regions will impact hydrological processes and water availability.