The McMurdo Dry Valleys, Antarctica, are a polar desert populated with numerous closed-watershed, perennially ice-covered lakes primarily fed by glacial melt. Lake levels have varied by as much as 8 m since 1972 and are currently rising after a decade of decreasing. Precipitation falls as snow, so lake hydrology is dominated by energy available to melt glacier ice and to sublimate lake ice. To understand the energy and hydrologic controls on lake level changes and to explain the variability between neighboring lakes, only a few kilometers apart, we model the hydrology for the three largest lakes in Taylor Valley. We apply a physically based hydrological model that includes a surface energy balance model to estimate glacial melt and lake sublimation to constrain mass fluxes to and from the lakes. Results show that lake levels are very sensitive to small changes in glacier albedo, air temperature, and wind speed. We were able to balance the hydrologic budget in two watersheds using meltwater inflow and sublimation loss from the ice-covered lake alone. A third watershed, closest to the coast, required additional inflow beyond model uncertainties. We hypothesize a shallow groundwater system within the active layer, fed by dispersed snow patches, contributes 23% of the inflow to this watershed. The lakes are out of equilibrium with the current climate. If the climate of our study period (1996-2013) persists into the future, the lakes will reach equilibrium starting in 2300, with levels 2-17 m higher, depending on the lake, relative to the 2020 level.
• Cropland expansion from 2008 to 2016 was mostly from losses of grassland (88%), with 3% losses from wetlands (a total of nearly 275,000 acres of wetlands, concentrated in the Prairie Pothole Region). Given the lack of national or regional datasets to track changes in RFS acreage, the extent of wetland losses directly attributable to the RFS cannot be more accurately estimated in the RtC3.
• Wetlands gains and losses are not distributed evenly across wetland types or sizes. Since 2007, the nation has lost 120.3 thousand acres of palustrine (marsh-like) wetlands and gained 205.9 thousand acres of lacustrine (lake-like) habitats in the conterminous United States. The diverse wetlands within these classes support different species and perform different ecosystem functions, including loss of functions that impact watershed hydrology, water quality, and water quantity.
• Small, seasonal wetlands are being lost at the fastest rate. The loss and consolidation of small wetlands to promote crop production has negatively impacted amphibians, invertebrates, and other aquatic species that depend on shallow water depths for reproduction. Shifts to longer hydroperiods in large or consolidated wetlands have more uniform (less diverse) invertebrate communities and can support fish that prey on insects and amphibians.
• Small wetlands and ponds are primary sources of water for aquifer recharge in the Northern Prairies. Recent studies in the Canadian portion of the Prairie Pothole Region found that while permanent ponds and wetlands are sources for recharge to aquifers, wetlands with surface water ponds that dry out every year play the dominant role in groundwater replenishment.
• While some Endangered Species Act-listed and other waterbirds have declined, waterfowl (ducks, geese, swans) as a group have not experienced declines over the past decade, possibly due to availability of food (grains), increased precipitation, and the interspersion of ponded waters and agricultural fields along migration routes.
• Shifts to corn and soybean production have resulted in more frequent application of chemicals, including pesticides and fertilizers. Increased usage of neonicotinoid insecticides is of particular concern because of their high toxicity to invertebrates, which are important food sources for wetland-dependent taxa.
• Evidence from the Prairie Pothole Region suggests that trends in larger wetland size, shifts to lakes and ponds (vs. vegetated wetlands), and prolonged and more frequent flooding are due to the combined effects of climate change and increased wetland ditching and consolidation. These trends are highly correlated with increased annual precipitation, which is projected to continue.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Third Triennial Report to Congress on Biofuels","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Environmental Protectipn Agency","usgsCitation":"Alexander, L., Beck, W.S., Carleton, J.N., Clark, C.M., Jager, H.I., James, A., Kriese, K., Moorhead, L.C., and Mushet, D., 2022, Wetland ecosystem health and biodiversity, 50 p.","productDescription":"50 p.","startPage":"14-1","endPage":"14-50","ipdsId":"IP-126555","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":427178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427177,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://assessments.epa.gov/biofuels/document/&deid=353055","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alexander, Laurie C.","contributorId":138989,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":849817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Whitney S.","contributorId":268335,"corporation":false,"usgs":false,"family":"Beck","given":"Whitney","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":849820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carleton, James N.","contributorId":295729,"corporation":false,"usgs":false,"family":"Carleton","given":"James","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":849821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Christopher M.","contributorId":215744,"corporation":false,"usgs":false,"family":"Clark","given":"Christopher","email":"","middleInitial":"M.","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":849825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jager, Henriette I.","contributorId":206774,"corporation":false,"usgs":false,"family":"Jager","given":"Henriette","email":"","middleInitial":"I.","affiliations":[{"id":37400,"text":"Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee","active":true,"usgs":false}],"preferred":false,"id":849819,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"James, Andrew","contributorId":295731,"corporation":false,"usgs":false,"family":"James","given":"Andrew","affiliations":[{"id":17659,"text":"Natural Resources Conservation Service","active":true,"usgs":false}],"preferred":false,"id":849823,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kriese, Ken","contributorId":295730,"corporation":false,"usgs":false,"family":"Kriese","given":"Ken","email":"","affiliations":[{"id":17659,"text":"Natural Resources Conservation Service","active":true,"usgs":false}],"preferred":false,"id":849822,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Moorhead, Leigh C.","contributorId":295732,"corporation":false,"usgs":false,"family":"Moorhead","given":"Leigh","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":849824,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849818,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238432,"text":"sir20225101 - 2022 - Stormwater reduction and water budget for a rain garden on sandy soil, Gary, Indiana, 2016–18","interactions":[],"lastModifiedDate":"2022-12-01T16:34:22.21762","indexId":"sir20225101","displayToPublicDate":"2022-12-01T10:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5101","displayTitle":"Stormwater Reduction and Water Budget for a Rain Garden on Sandy Soil, Gary, Indiana, 2016–18","title":"Stormwater reduction and water budget for a rain garden on sandy soil, Gary, Indiana, 2016–18","docAbstract":"Stormwater reduction measures, or green infrastructure, were implemented in the parking area at Gary City Hall, Gary, Indiana, with the intention of reducing stormwater discharge to the sewers. A study area, including a centrally located rain garden and the surrounding paved surfaces and green space, was instrumented during both a preconstruction and a postconstruction period to (1) develop water budgets to improve understanding of the rain garden hydrology and (2) determine the quantity of stormwater runoff that was diverted and retained by the green infrastructure instead of reaching the combined storm and sanitary sewer. The study was focused on warm-season precipitation and was monitored during spring, summer, and fall of 2016, 2017 and 2018.
Before construction of the rain garden in the parking lot of Gary City Hall in 2017, nearly all precipitation was conveyed away from the parking lot by underground drains, discharged to the sewer, and treated as sanitary waste at the Gary Sanitary District’s treatment plant or discharged directly to local waterways if stormflow exceeded capabilities of the sewage treatment plant. A goal of the Great Lakes Restoration Initiative is the reduction of sewer overflows to local waterways to improve the quality of water entering the Great Lakes. Cities such as Gary benefit financially and environmentally by reducing discharges of stormwater runoff to the sewer system, eliminating the need for treatment. Before implementation of green infrastructure at Gary City Hall, approximately 25 percent of precipitation (approximately 10,200 cubic feet) discharged as stormwater to the sewers through the parking lot drain. After implementation, 2 percent of precipitation discharged to the sewers. For the spring, summer, and fall seasons of 2017 and 2018, 21–24 percent (about 10,700–19,700 cubic feet) of precipitation was captured by the newly installed rain garden. Stormwater discharged to the rain garden infiltrated the sandy soil and was later evaporated from the soil surface, was transpired by plants, or recharged the underlying groundwater aquifer. The percent reduction in stormwater discharged to the storm sewer after the construction of the rain garden was 80.3 percent, equating to approximately 21,400 and 39,300 gallons of stormwater in 2017 and 2018, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225101","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Lampe, D.C., Bayless, E.R., and Follette, D.D., 2022, Stormwater reduction and water budget for a rain garden on sandy soil, Gary, Indiana, 2016–18: U.S. Geological Survey Scientific Investigations Report 2022–5101, 39 p., https://doi.org/10.3133/sir20225101.","productDescription":"Report: viii, 39 p.; Data Release","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126798","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":409550,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H5RNNE","text":"USGS data release","linkHelpText":"Groundwater recharge estimates for a green infrastructure installation at Gary City Hall, Gary, Indiana 2016–18"},{"id":409545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5101/coverthb.jpg"},{"id":409546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5101/sir20225101.pdf","text":"Report","size":"7.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5101"},{"id":409548,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5101/sir20225101.XML"},{"id":409549,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5101/images/"}],"country":"United States","state":"Indiana","city":"Gary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.33719284301965,\n 41.60392762249708\n ],\n [\n -87.33719284301965,\n 41.602552401500475\n ],\n [\n -87.33581350339763,\n 41.602552401500475\n ],\n [\n -87.33581350339763,\n 41.60392762249708\n ],\n [\n -87.33719284301965,\n 41.60392762249708\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Laura K. Lautz is a premier mentor, collaborator, and researcher at the intersection of natural hydrologic systems and humans. Her research has shifted the paradigm around measuring and understanding the impacts of surface water and groundwater interactions across spatial and temporal scales. She has done this by testing and refining new methods and by collaborating with, training, supporting, and mentoring diverse scientists. Here, we review her research across five themes, summarizing the prior status of the field, what Lautz contributed, as well as new directions in the field inspired by her work. Lautz’s research expanded our understanding of the impacts of stream restoration on surface water-groundwater interactions, where she tested new field methods and showed that restoration structures increase hyporheic exchange, locally altering biogeochemical function of the streambed. She refined novel methods for measuring surface water-groundwater exchanges and worked to make these methods easily accessible through freely available software. Her research group greatly expanded the use of heat as a quantitative tracer of hydrologic processes via the well-used VFLUX and HFLUX programs. Her research evaluated the impacts of surface water-groundwater interactions in urban streams, showing the substantial fluxes of nutrients and chloride that can move through those exchanges and the potential for groundwater to help buffer contamination. To assess groundwater impacts on streamflow below tropical glaciers, she used a wide range of field methods to reveal the sensitivity of these systems to climate change. Finally, she built tools to quantify natural brine contamination of drinking water wells in areas that may later be subject to high-volume hydraulic fracturing, creating a needed ‘pre-fracking’ dataset. Through this process, she identified multiple sources of salinity that are already reaching wells in these systems. Overall, this research has been done with a focus on mentoring and training the next generation of hydrologists, including work to specifically train for careers beyond academia, and facilitating early career scientists to realize their innate potentials. With former trainees in careers across industry, government, and academia, Dr. Laura K. Lautz is now working to build cross-disciplinary research at even larger scales, across federal research units, guaranteeing that an even larger impact on hydrology is still to come.
Levittown Lake is a 30-hectare, brackish waterbody located in the municipality of Toa Baja, on the northern coast of Puerto Rico. The lake is a small, man-made feature formed by draining the marshland over which the Levittown community was built. Levittown Lake has an average depth of about 5 meters and a water level at/near mean sea level. Tidal oscillations within the lake were minimal during the study, about 10 centimeters regardless of ocean tides, and the daily flushing rate of the lake was about 2 percent of its entire water volume.
Hydrologic, water-quality, and biological data were collected in Levittown Lake and adjacent areas (specifically, the inlet/outlet channel and Caño El Hato drainage canal) between April 2010 and June 2011 (1) to establish baseline conditions and determine the water quality of the lake on the basis of preestablished standards and (2) for contrast with other, more healthy coastal lagoons. The study provides a baseline for an assessment of the potential of Levittown Lake to function as a coastal lagoon.
Water-quality properties measured onsite (temperature, pH, dissolved oxygen concentration, specific conductance, salinity, and water transparency) varied diurnally and seasonally. In general, water-quality properties were in compliance with current regulatory Class SB standards established by the Puerto Rico Environmental Quality Board, except for some dissolved oxygen concentration and pH measurements. Some dissolved oxygen concentration measurements at the water surface and all dissolved oxygen concentration measurements at the lake bottom were lower than the values recommended by the Puerto Rico Environmental Quality Board. The pH of the water at the lake surface ranged from 7.3 to 9.1, with the upper value exceeding the recommended pH values. Nutrient concentrations were below the current regulatory standards of less than 5 milligrams per liter (mg/L) for total nitrogen and 1 mg/L for total phosphorus. The measured concentrations of chlorophyll a varied throughout the year of sampling and indicate that eutrophic conditions predominate in Levittown Lake.
The phytoplankton yielded an average net productivity of 0.5 milligram of oxygen per liter per hour, as determined by light and dark bottle primary productivity studies conducted on a monthly basis and measured in the early morning hours. Because these measurements were restricted to the morning hours, a qualification of the representativeness of the results to the full diurnal cycle is necessary. The measured hourly respiration rate averaged 0.39 milligram of oxygen per liter. Diel studies were planned in the lake to assess dissolved oxygen concentration diurnal curves and ultimately to compute the community net primary productivity, respiration, and gross productivity. Conditions during the diel studies were later determined to be unsuitable, limiting the assessment of community metabolism. Another biological indicator evaluated during the study was the phytoplankton biomass, and results indicated that phytoplankton biomass measured at the Levittown Lake ranged from 6.0 to 112.5 mg/L.
Fecal indicator bacteria concentrations ranged from 10 to 1,540,000 colonies per 100 milliliters of water. Concentrations generally were greatest in and near the Caño El Hato drainage canal and, during the study, exceeded current regulatory standards established for Puerto Rico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225096","issn":"2328-0328","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Soler-López, L.R., Gómez-Fragoso, J.M., and Val-Merníz, N.A., 2022, Hydrology, water quality, and biological characteristics of Levittown Lake, Toa Baja, Puerto Rico, April 2010–June 2011: U.S. Geological Survey Scientific Investigations Report 2022–5096, 32 p., https://doi.org/10.3133/sir20225096.","productDescription":"Report: vii, 32 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-064860","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":409442,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MC6JZ6","text":"USGS data release","linkHelpText":"Data for the hydrologic and water-quality characterization of Levittown Lake, Toa Baja, Puerto Rico, April 2010–June 2011"},{"id":409802,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225096/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":409439,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5096/sir20225096.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5096"},{"id":409438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5096/coverthb.jpg"},{"id":409440,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5096/sir20225096.XML"},{"id":409441,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5096/images"},{"id":409443,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Levittown Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.20418646107952,\n 18.468480510318614\n ],\n [\n -66.20418646107952,\n 18.43267147514682\n ],\n [\n -66.16780969765956,\n 18.43267147514682\n ],\n [\n -66.16780969765956,\n 18.468480510318614\n ],\n [\n -66.20418646107952,\n 18.468480510318614\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
It is important to routinely estimate loads from an entire watershed to describe current conditions and evaluate how watershed-wide management efforts have affected the nutrient and sediment export that affect downstream water quality. However, monitoring in most areas, including the Great Lakes watershed, consists of sampling at a limited number of sites that are only periodically used to estimate total watershed loading. Here, we describe a technique to extrapolate loads measured at a limited number of reference sites to the total load from a large watershed using load ratios between monitored sites and unmonitored areas obtained from a watershed model (i.e., model load ratio, MLR, approach). In this study, modeled nonpoint-source load ratios between monitored tributaries (reference sites) and nearby unmonitored areas and point-source delivery factors for all areas were obtained from a Spatially Referenced Regression On Watershed attributes (SPARROW) model and used to extrapolate the measured loads from an ongoing monitoring program (Great Lakes Restoration Initiative Tributary monitoring program) to the entire Great Lakes watershed. The MLR approach incorporates spatial variability in nonpoint- and point-source delivery, watershed characteristics, and hydrology that are often not considered when estimating loads from unmonitored areas, such as using the unit area load (UAL) extrapolation approach. The MLR approach provided smaller watershed loads than the UAL approach because yields from monitored sites, in general, were larger than from unmonitored areas. When both approaches were used to estimate loads at adjacent monitored sites, the MLR approach provided more accurate estimates than the UAL approach.
Urbanization can substantially alter sediment and nutrient loadings to streams. Although a growing body of literature has documented these processes, conditions may vary widely by region and physiographic province (PP). Substantial investments are made by localities to meet federal, state, and local water-quality goals and locally relevant monitoring data are needed to appropriately set standards and track progress. In 2016, a long-term stormwater monitoring program was initiated to characterize water-quality and streamflow conditions and compute average annual nutrient- and sediment-loading rates across the three dominant land-use types—commercial (COM), high-density residential, and single-family residential (SFR)—in the Hampton Roads metropolitan region within the Coastal Plain PP in southeastern Virginia. This report summarizes the first five years of data collection to (1) assess patterns in streamflow and water chemistry across the three major land-use types in the region; (2) compute annual sediment and nutrient loads; and (3) compare annual loading rates to those in other urbanized regions.
Patterns in watershed hydrology characteristics and conditions were similar to those observed in other urban monitoring studies. Base-flow indices were lower and stream flashiness indices were higher in the study watersheds compared to those in less developed reference watersheds. These patterns reflect a decrease in infiltration and consequent increase in storm runoff as a result of urbanization. Stream flashiness was strongly positively related to degree of impervious land cover and negatively to watershed area. Hydrologic metrics varied across the land-use gradient, reflecting greater and more rapid runoff in the COM watersheds than in SFR watersheds. Event-based analyses conducted exclusively on periods of runoff highlight longer duration events, longer time-to-peak streamflow, and a longer lag between peak precipitation and peak streamflow in SFR watersheds, and higher stormflow yields, runoff ratios, and peak flows in COM watersheds. Event-based metrics varied seasonally because of regional meteorological patterns.
Concentrations of total suspended solids (TSS) and total phosphorus (TP) were positively correlated to streamflow, whereas concentrations of total nitrogen (TN) varied little across the hydrologic regime. Phosphorus composition varied spatially and seasonally—the proportion of orthophosphate (PO43-) was highest in samples collected from stations draining residential land-use types and was elevated in summer and fall. Nitrogen composition varied with hydrologic condition: nitrate plus nitrite (NO3-) dominance during base flow shifted to total organic nitrogen (TON) dominance during periods of runoff. For all three major constituents (TSS, TP, and TN), concentrations were highest in SFR watersheds, whereas yields were greatest in COM watersheds. This seeming contradiction in concentration and yield across land-use types occurred because of spatial differences in streamflow yield.
The network average TSS yield in Hampton Roads was lower than that in comparable networks in Fairfax County, Virginia, and Gwinnett County, Georgia, a difference that may reflect dissimilarities in the topographic and soil characteristics of the Coastal Plain versus those in Piedmont PPs, as well as differences in engineered concrete stormwater conveyances versus earthen streams. The average annual TP yield in Hampton Roads was higher than averages reported in comparison studies and was primarily driven by elevated PO43-. Elevated PO43- yields may be related to unique soil and geological features of the Coastal Plain PP that limit phosphorus retention. Total nitrogen yields in the Hampton Roads and Fairfax County networks were similar; however, composition did vary, with greater total organic nitrogen yields in Hampton Roads and greater NO3- yields in Fairfax County.
Cross-correlation analyses and mass-volume curves were used to assess the timing of sediment and nutrient loadings. The majority of TSS and TP was typically transported during the initial phase of a storm-runoff event, a phenomenon commonly termed the “first flush.” Although TN concentrations typically peaked within an hour of peak streamflow, reflecting the particulate dominance of TN during stormflows, and loadings were greater during the early phase of most storm events, the stricter first-flush criterion was rarely met. This suggests that the most abundant sources of TN in these watersheds are not as directly connected to the stormwater-conveyance system as are TSS and TP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225111","isbn":"978-1-4113-4488-4","collaboration":"Prepared in cooperation with the Hampton Roads Planning District Commission","usgsCitation":"Porter, A.J., 2022, Stormwater quantity and quality in selected urban watersheds in Hampton Roads, Virginia, 2016–2020: U.S. Geological Survey Scientific Investigations Report 2022–5111, 77 p., https://doi.org/10.3133/sir20225111.","productDescription":"Report: xi, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140434","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":409552,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225111/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5111"},{"id":409543,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XMPEND","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess stormwater quality and quantity in twelve urban watersheds in Hampton Roads, Virginia, 2016–2020"},{"id":409542,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5111/images/"},{"id":409539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5111/sir20225111.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5111"},{"id":409541,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5111/sir20225111.XML"},{"id":409538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5111/coverthb.jpg"}],"country":"United States","state":"Virginia","city":"Hampton Roads","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.14335620670732,\n 36.90313369880009\n ],\n [\n -76.31812685340054,\n 37.07905097755574\n ],\n [\n -76.51786473533596,\n 37.10821243098252\n ],\n [\n -76.55620727517176,\n 37.13025388344366\n ],\n [\n -76.59365716752067,\n 37.16650105362895\n ],\n [\n -76.62219115065432,\n 37.1295423598058\n ],\n [\n -76.47773786104024,\n 37.03208397181615\n ],\n [\n -76.4179948338545,\n 36.95587973488442\n ],\n [\n -76.23519900440459,\n 36.804670263024434\n ],\n [\n -76.11036282819533,\n 36.821803342395526\n ],\n [\n -76.14335620670732,\n 36.90313369880009\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Extensive drainage of peatlands in the southeastern United States coastal plain for the purposes of agriculture and timber harvesting has led to large releases of soil carbon as carbon dioxide (CO2) due to enhanced peat decomposition. Growth in mechanisms that provide financial incentives for reducing emissions from land use and land-use change could increase funding for hydrological restoration that reduces peat CO2 emissions from these ecosystems. Measuring soil respiration and physical drivers across a range of site characteristics and land use histories is valuable for understanding how CO2 emissions from peat decomposition may respond to raising water table levels. We combined measurements of total soil respiration, depth to water table from soil surface, and soil temperature from drained and restored peatlands at three locations in eastern North Carolina and one location in southeastern Virginia to investigate relationships among total soil respiration and physical drivers, and to develop models relating total soil respiration to parameters that can be easily measured and monitored in the field.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13021-022-00219-5","usgsCitation":"Swails, E.E., Ardon, M., Krauss, K., Peralta, A., Emmanuel, R.E., Helton, A., Morse, J., Gutenberg, L., Cormier, N., Shoch, D., Settlemyer, S., Soderholm, E., Boutin, B.P., Peoples, C., and Ward, S., 2022, Response of soil respiration to changes in soil temperature and water table level in drained and restored peatlands of the southeastern United States: Carbon Balance and Management, v. 17, 18, 10 p., https://doi.org/10.1186/s13021-022-00219-5.","productDescription":"18, 10 p.","ipdsId":"IP-127982","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":445847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13021-022-00219-5","text":"Publisher Index Page"},{"id":409852,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.74816638869669,\n 34.86985768602176\n ],\n [\n -78.47429331497659,\n 33.323999733572165\n ],\n [\n -76.51955704668092,\n 34.07317373436328\n ],\n [\n -75.17979398638913,\n 35.13971099796244\n ],\n [\n -75.44335393267585,\n 36.61670427026024\n ],\n [\n -78.12288005326047,\n 36.54615713022474\n ],\n [\n -79.74816638869669,\n 34.86985768602176\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2022-11-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Swails, Erin E.","contributorId":299540,"corporation":false,"usgs":false,"family":"Swails","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":64873,"text":"TerraCarbon LLC, Illinois","active":true,"usgs":false}],"preferred":false,"id":858000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ardon, Marcelo","contributorId":298014,"corporation":false,"usgs":false,"family":"Ardon","given":"Marcelo","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":858001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":211297,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peralta, A.L.","contributorId":299541,"corporation":false,"usgs":false,"family":"Peralta","given":"A.L.","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":858003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emmanuel, Ryan E.","contributorId":299542,"corporation":false,"usgs":false,"family":"Emmanuel","given":"Ryan","email":"","middleInitial":"E.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":858004,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helton, A.M.","contributorId":299543,"corporation":false,"usgs":false,"family":"Helton","given":"A.M.","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":858005,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morse, J.L.","contributorId":299544,"corporation":false,"usgs":false,"family":"Morse","given":"J.L.","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":858006,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gutenberg, Laurel","contributorId":217284,"corporation":false,"usgs":false,"family":"Gutenberg","given":"Laurel","email":"","affiliations":[],"preferred":false,"id":858007,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cormier, Nicole 0000-0003-2453-9900","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":214726,"corporation":false,"usgs":false,"family":"Cormier","given":"Nicole","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":858008,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shoch, D.","contributorId":299545,"corporation":false,"usgs":false,"family":"Shoch","given":"D.","email":"","affiliations":[{"id":64873,"text":"TerraCarbon LLC, Illinois","active":true,"usgs":false}],"preferred":false,"id":858009,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Settlemyer, Scott","contributorId":299546,"corporation":false,"usgs":false,"family":"Settlemyer","given":"Scott","email":"","affiliations":[{"id":64873,"text":"TerraCarbon LLC, Illinois","active":true,"usgs":false}],"preferred":false,"id":858010,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Soderholm, Eric","contributorId":298011,"corporation":false,"usgs":false,"family":"Soderholm","given":"Eric","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":858011,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Boutin, Brian P.","contributorId":299547,"corporation":false,"usgs":false,"family":"Boutin","given":"Brian","email":"","middleInitial":"P.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":858012,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Peoples, Chuck","contributorId":299548,"corporation":false,"usgs":false,"family":"Peoples","given":"Chuck","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":858013,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ward, Sara","contributorId":299549,"corporation":false,"usgs":false,"family":"Ward","given":"Sara","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":858014,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70238406,"text":"70238406 - 2022 - In situ soil moisture sensors in undisturbed soils","interactions":[],"lastModifiedDate":"2022-12-02T13:42:23.337438","indexId":"70238406","displayToPublicDate":"2022-11-18T07:39:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2498,"text":"Journal of Visualized Experiments","active":true,"publicationSubtype":{"id":10}},"title":"In situ soil moisture sensors in undisturbed soils","docAbstract":"Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture data has been slow due to inconsistent data collection, poor standardization, and typically short record duration. Soil moisture, or quantitatively volumetric soil water content (SWC), is measured using buried, in situ sensors that infer SWC from an electromagnetic response. This signal can vary considerably with local site conditions such as clay content and mineralogy, soil salinity or bulk electrical conductivity, and soil temperature; each of these can have varying impacts depending on the sensor technology.,
Furthermore, poor soil contact and sensor degradation can affect the quality of these readings over time. Unlike more traditional environmental sensors, there are no accepted standards, maintenance practices, or quality controls for SWC data. As such, SWC is a challenging measurement for many environmental monitoring networks to implement. Here, we attempt to establish a community-based standard of practice for in situ SWC sensors so that future research and applications have consistent guidance on site selection, sensor installation, data interpretation, and long-term maintenance of monitoring stations.,
The videography focuses on a multi-agency consensus of best-practices and recommendations for the installation of in situ SWC sensors. This paper presents an overview of this protocol along with the various steps essential for high-quality and long-term SWC data collection. This protocol will be of use to scientists and engineers hoping to deploy a single station or an entire network.
","language":"English","publisher":"MyJoVE Corporation","doi":"10.3791/64498","usgsCitation":"Caldwell, T., Cosh, M.H., Evett, S.R., Edwards, N., Hofman, H., Illston, B., Meyers, T.P., Skumanich, M., and Sutcliffe, K., 2022, In situ soil moisture sensors in undisturbed soils: Journal of Visualized Experiments, no. 189, e64498, 35 p., https://doi.org/10.3791/64498.","productDescription":"e64498, 35 p.","ipdsId":"IP-131823","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":445855,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.library.noaa.gov/view/noaa/62072","text":"Publisher Index Page"},{"id":409993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"189","noUsgsAuthors":false,"publicationDate":"2022-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, Todd 0000-0003-4068-0648","orcid":"https://orcid.org/0000-0003-4068-0648","contributorId":217924,"corporation":false,"usgs":true,"family":"Caldwell","given":"Todd","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":857431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":857432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evett, Steven R. 0000-0003-3418-5771","orcid":"https://orcid.org/0000-0003-3418-5771","contributorId":244949,"corporation":false,"usgs":false,"family":"Evett","given":"Steven","email":"","middleInitial":"R.","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":857433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Nathan","contributorId":260132,"corporation":false,"usgs":false,"family":"Edwards","given":"Nathan","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":857434,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hofman, Heather","contributorId":260134,"corporation":false,"usgs":false,"family":"Hofman","given":"Heather","email":"","affiliations":[{"id":52518,"text":"USDA NRCS National Climate Center","active":true,"usgs":false}],"preferred":false,"id":857435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Illston, Bradley","contributorId":299264,"corporation":false,"usgs":false,"family":"Illston","given":"Bradley","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":857436,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meyers, Tilden P.","contributorId":146138,"corporation":false,"usgs":false,"family":"Meyers","given":"Tilden","email":"","middleInitial":"P.","affiliations":[{"id":16598,"text":"NOAA/ATDD","active":true,"usgs":false}],"preferred":false,"id":857437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Skumanich, Marina","contributorId":260137,"corporation":false,"usgs":false,"family":"Skumanich","given":"Marina","email":"","affiliations":[{"id":52519,"text":"NOAA National Integrated Drought Information System","active":true,"usgs":false}],"preferred":false,"id":857438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sutcliffe, Kent","contributorId":299265,"corporation":false,"usgs":false,"family":"Sutcliffe","given":"Kent","email":"","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":857439,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238404,"text":"70238404 - 2022 - Introduction to the special issue on fire impacts on hydrological processes","interactions":[],"lastModifiedDate":"2022-11-22T12:38:49.604613","indexId":"70238404","displayToPublicDate":"2022-11-15T06:35:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12968,"text":"Journal of Hydrology and Hydromechanics","active":true,"publicationSubtype":{"id":10}},"title":"Introduction to the special issue on fire impacts on hydrological processes","docAbstract":"Fire has been present on the Earth since vegetation began colonizing the continents (Santos et al., 2017). The role of fire on terrestrial sedimentation processes was already highlighted by Schumm (1968) in his pioneering research to understand the detachment, transport, and sedimentation of material on the Planet. The use of fire by humans as a tool that transformed the landscapes of the world has been widely accepted (Wang et al., 1999). Glacial-interglacial changes can affect vegetation with resulting implications for global fire regimes and trace gas emissions (Thonicke et al., 2005). Wildfire effects on vegetation can, in turn, alter soil erosion rates (Lenton, 2001), which is mainly due to the control plants exert on soil erosion processes (López-Vicente et al., 2021).","language":"English","publisher":"Sciendo","doi":"10.2478/johh-2022-0036","usgsCitation":"Cerdà, A., Ebel, B., Serpa, D., and Lichner, L., 2022, Introduction to the special issue on fire impacts on hydrological processes: Journal of Hydrology and Hydromechanics, v. 70, no. 4, p. 385-387, https://doi.org/10.2478/johh-2022-0036.","productDescription":"3 p.","startPage":"385","endPage":"387","ipdsId":"IP-146188","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":445879,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2478/johh-2022-0036","text":"Publisher Index Page"},{"id":409526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"70","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Cerdà, Artemi","contributorId":299255,"corporation":false,"usgs":false,"family":"Cerdà","given":"Artemi","affiliations":[{"id":64797,"text":"Valencia University, Spain","active":true,"usgs":false}],"preferred":false,"id":857418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":857419,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Serpa, Dalila","contributorId":299256,"corporation":false,"usgs":false,"family":"Serpa","given":"Dalila","email":"","affiliations":[{"id":36309,"text":"University of Aveiro, Portugal","active":true,"usgs":false}],"preferred":false,"id":857420,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lichner, Lubomir","contributorId":299257,"corporation":false,"usgs":false,"family":"Lichner","given":"Lubomir","email":"","affiliations":[{"id":64798,"text":"Slovak Academy of Sciences, Slovakia","active":true,"usgs":false}],"preferred":false,"id":857421,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238049,"text":"sir20225102 - 2022 - Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana","interactions":[],"lastModifiedDate":"2022-11-11T17:47:08.905958","indexId":"sir20225102","displayToPublicDate":"2022-11-10T07:15:06","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5102","displayTitle":"Effect of Uncertainty of Discharge Data on Uncertainty of Discharge Simulation for the Lake Michigan Diversion, Northeastern Illinois and Northwestern Indiana","title":"Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana","docAbstract":"Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and uncertainties of the input data and of the estimated model parameters, which, although optimized for the given calibration data, remain uncertain. The input data errors are caused by uncertainties in the forcing data, such as precipitation and other climatological data, and in the published discharges used for calibration. In the numerical algorithms used for calibration, the published discharges are often assumed to be without error, but they are themselves uncertain, typically having been computed using ratings, which are models fitted to uncertain discharge measurements.
In this study, uncertainty of published daily discharge data and how the discharge uncertainty is transmitted to the parameter values of the Hydrological Simulation Program–FORTRAN (HSPF) rainfall-runoff model and to the simulated discharge at both calibration and prediction locations were investigated for the Lake Michigan diversion in northeastern Illinois and northwestern Indiana. The HSPF model used in this study is used by the U.S. Army Corps of Engineers as part of quantifying the diversion of water from Lake Michigan by the State of Illinois. In this study, the model is calibrated jointly at two watersheds in the study area; the resulting model is considered the base model in this study. Seven other gaged watersheds in the study area are used for testing predictive simulations. A Bayesian rating curve estimation (BaRatin) approach, the BaRatin stage-period-discharge (SPD) method, was used to estimate the uncertainty of the published discharge from the calibration watersheds. To characterize the effect of the discharge uncertainty on parameter values, the HSPF model parameters were recalibrated to 17 nonrandomly selected pairs of discharge series from the BaRatin SPD analysis. To provide an indicator of the effect of parameter uncertainty to compare to the effect of discharge uncertainty, 1,000 parameter sets also were randomly generated from the estimated parameter covariance matrix of the base model. The recalibrated and random parameter sets were then used in HSPF simulations of discharge at the two calibration watersheds and at the seven prediction watersheds. Selected discharge summary statistics—the period-of-study (POS, water years 1997 to 2015) mean discharge, selected flow-duration curve (FDC) quantiles, and water year mean discharges—are used to characterize the variability between simulated and published discharge.
A normalized variability index (VN) is used as a measure of the uncertainty of flow statistics arising from the uncertainty of the sources considered in this study. When this index is at least 1, the variability of the simulations is large enough to explain the median error between simulated and published values, although offsetting errors from other sources are also likely. When the index is appreciably less than 1, the variability of the simulations is clearly insufficient to explain the median error between simulated and published values. At the two calibration watersheds and for results of the two simulation sets considered together, the VN values ranged from 0.2 to 0.8 for POS mean discharge, from 0.3 to 0.6 in the median for a set of FDC quantiles, and from 0.1 to 0.2 in the median for water year mean discharges. These values indicate that substantial uncertainty remains unexplained. Even though two watersheds were used in calibration, that calibration was highly constrained because it was applied to the watersheds simultaneously and was subject to parameter regularization that constrained the adjustment of the parameters from their initial values. These constraints were applied to avoid overfitting to the calibration watersheds and thus to increase the likelihood that the resulting parameters would give accurate results at watersheds not used in the calibration, but they created a parameter transfer error in the calibration watershed results shown by the balancing of errors between the two watersheds. Additional remaining error sources include model structural error and meteorological forcing error to the degree that the calibration was unable to adjust the parameters to account for these errors. At the prediction watersheds, the corresponding VN values were almost always substantially lower than those values at the calibration watersheds. This result is expected because the prediction watersheds have additional uncertainty, including parameter transfer error.
The work described in this report provides preliminary estimates of a limited range of sources of error in predicted discharge uncertainty. Future work would be beneficial to obtain a better statistical characterization of the effect of the uncertainty of calibration discharge series and to address additional sources of uncertainty, such as from precipitation input data used in calibration and prediction and from structural (model) errors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225102","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Soong, D.T., and Over, T.M., 2022, Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2022–5102, 54 p., https://doi.org/10.3133/sir20225102.","productDescription":"Report: ix, 54 p.; 2 Data releases; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-120412","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":409202,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S2IID","text":"USGS data release","linkHelpText":"National Land Cover Database (NLCD) 2011 Land Cover Conterminous United States"},{"id":409201,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UC21B0","text":"USGS data release","linkHelpText":"Models, inputs, and outputs for estimating the uncertainty of discharge simulations for the Lake Michigan Diversion using the Hydrological Simulation Program – FORTRAN model"},{"id":409196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5102/coverthb.jpg"},{"id":409197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.pdf","text":"Report","size":"8.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5102"},{"id":409198,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.XML"},{"id":409199,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5102/images"},{"id":409200,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Illinois, Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.34881814497747,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 42.492126793048925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Big Lost River Basin, located in parts of Butte and Custer Counties in south-central Idaho, supports the communities surrounding the cities of Arco, Leslie, Mackay, and Moore and provides for agricultural resources that depend on a sustainable supply of surface water from the Big Lost River and its tributaries and groundwater from an unconfined aquifer. The aquifer, situated in a structurally controlled intermontane valley, is composed of unconsolidated alluvium, consolidated sedimentary and volcanic rocks, and younger interbedded volcanic rocks.
This report presents two separate groundwater budgets for the aquifer, one above and one below Mackay Dam, as well as a combined groundwater budget for the aquifer within the entire Big Lost River Basin. The budgets span a 20-year period (2000–19), characterizing average conditions, a dry year (2014), and a wet year (2017). The groundwater budgets will help address questions regarding the availability of groundwater supply in the Big Lost River Basin and inform future groundwater modeling. The Idaho Geological Survey has prepared the groundwater budgets as part of a larger hydrogeologic investigation completed by the U.S. Geological Survey and the Idaho Geological Survey in cooperation with the Idaho Department of Water Resources during 2018–21. Other reports describe the hydrogeologic framework and several streamflow-measurement events to evaluate gains and losses on the Big Lost River. Collectively, these reports provide an updated characterization of groundwater resources in the Big Lost River Basin which will help address water resources challenges.
A groundwater budget is a conceptual and numerical accounting of inflow (recharge) to groundwater and outflow (discharge) from groundwater. The predominant sources of recharge to the aquifer include losing river reaches (33 percent), areal recharge (as precipitation less evapotranspiration and surface runoff, comprising about 23 percent of the total inflow), tributary canyon underflow from higher altitudes (20 percent), canal seepage (13 percent), recharge through applied irrigation on fields below the root zone and other minor sources (11 percent), and Mackay Reservoir seepage (less than 1 percent). The primary sources of discharge from the aquifer are groundwater pumpage to meet irrigation demand, domestic supply, and municipal supply (76 percent) and gaining river reaches (24 percent).
The positive or negative difference between the sum of all inflows and outflows is regarded as the residual, representing the change in groundwater storage, groundwater outflow from the basin or subbasins, and uncertainty and errors in the budget. In the Big Lost River Basin, groundwater outflow is at the mouth of the basin below Arco into the eastern Snake River Plain aquifer.
The total mean annual estimated recharge to the Big Lost River Basin was 439,100 acre-feet per year (acre-ft/yr; 607 cubic feet per second [ft3/s]) for 2000–19, 373,900 acre-ft/yr (516 ft3/s) in 2014, and 762,100 acre-ft/yr (1,053 ft3/s) in 2017. The mean annual estimated groundwater discharge from the aquifer was about 112,300 acre-ft/yr (155 ft3/s) for 2000–19, 153,500 acre-ft/yr (212 ft3/s) in 2014, and 53,400 acre-ft/yr (74 ft3/s) in 2017. The estimated mean annual groundwater residual was 326,800 acre-ft/yr (451 ft3/s) for 2000–19, 220,400 acre-ft/yr (304 ft3/s) in 2014, and 708,700 acre-ft/yr (979 ft3/s) in 2017. The mean annual residual above Mackay Dam was 100,400 acre-ft/yr (2000-19), 96,700 acre-ft (2014), and 248,300 acre-ft (2017). The mean annual residual contribution below Mackay Dam, minus any groundwater-flow above Mackay Dam, was 226,400 acre-ft/yr (2000-19), 123,700 acre-ft (2014), and 460,400 acre-ft (2017).
These results are highly sensitive to assumptions about certain budget inflow parameters. In particular, the magnitude of the budget residuals during especially dry and wet periods is amplified by the groundwater-budget terms tributary streamflow and tributary underflow that contribute appreciable recharge but also have high uncertainty.
The results of the groundwater-budget evaluation describe an interconnected and complex hydrologic response throughout the basin to various climatic and water-use trends. The part of the basin above Mackay Dam typically has a positive groundwater residual derived from snowmelt recharge to tributary canyons and areal recharge in excess of groundwater pumpage for irrigation demand. This supply is used to meet irrigation demand above Mackay Dam and to provide for water supply below Mackay Dam. On average, groundwater inflow from above Mackay Dam to below Mackay Dam, assuming negligible reservoir storage effects, accounts for about 25 percent of the total groundwater recharge below Mackay Dam. Considerable recharge to groundwater below Mackay Dam occurs through seepage from the Big Lost River and canals and ditches. Most groundwater discharge from the aquifer is through irrigation pumping. The water supply below Mackay Dam is highly dependent on available upstream surface-water flows, the magnitude of the groundwater residual from above Mackay Dam, and annual variability in local groundwater conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078C","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Clark, A., 2022, Groundwater budgets for the Big Lost River Basin, south-central Idaho, 2000–19, chap. C of Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078–C, 111 p., https://doi.org/10.3133/sir20215078C.","productDescription":"xi, 111 p.","ipdsId":"IP-125226","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":409232,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/coverthb.jpg"},{"id":409233,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/sir20215078C.pdf","text":"Reports","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5078-C"},{"id":409235,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/images"},{"id":409236,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/sir20215078C.XML"},{"id":502105,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113824.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.1863738631967,\n 43.10571945845362\n ],\n [\n -113.42308735779262,\n 43.54649028685452\n ],\n [\n -112.13258233834704,\n 44.22138739870667\n ],\n [\n -112.23487846793722,\n 44.737914300373745\n ],\n [\n -114.26506595862107,\n 46.10751185031063\n ],\n [\n -115.75229430420214,\n 46.493497990156555\n ],\n [\n -117.884775159506,\n 45.476547804668826\n ],\n [\n -117.57788677073549,\n 45.01671717637413\n ],\n [\n -116.38967788087962,\n 44.5307302025393\n ],\n [\n -115.2014689910242,\n 43.60919623765622\n ],\n [\n -114.1863738631967,\n 43.10571945845362\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director , Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPERPNW) to the upper Missouri River basin (PROSPERUM), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPERUM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPERUM are generally consistent with the PROSPERPNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPERUM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2022.100138","usgsCitation":"Sando, R., Jaeger, K.L., Farmer, W., Barnhart, T., McShane, R., Welborn, T.L., Kaiser, K.E., Hafen, K., Blasch, K.W., York, B.C., and Shallcross, A., 2022, Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018: Journal of Hydrology X, v. 17, 100138, 22 p., https://doi.org/10.1016/j.hydroa.2022.100138.","productDescription":"100138, 22 p.","ipdsId":"IP-137870","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":446045,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2022.100138","text":"Publisher Index Page"},{"id":413911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"upper Missouri River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.06578880812711,\n 48.98607018582902\n ],\n [\n -114.41405308204301,\n 46.6259860103564\n ],\n [\n -114.45505714000817,\n 45.56604735512229\n ],\n [\n -114.1280129319525,\n 45.6810748968652\n ],\n [\n -113.43692249781836,\n 44.85861341197983\n ],\n [\n -112.98504047311934,\n 44.442264035596594\n ],\n [\n -111.79851411576917,\n 44.50526246095063\n ],\n [\n -111.2396387557593,\n 44.90171571207168\n ],\n [\n -110.61806405817302,\n 42.14074973473086\n ],\n [\n -105.77175988800175,\n 41.952647712608155\n ],\n [\n -104.56426824820389,\n 42.95942508508247\n ],\n [\n -103.25615692243574,\n 43.83191953044022\n ],\n [\n -101.00144324324455,\n 44.44211500891038\n ],\n [\n -100.09948505313812,\n 44.838575527202494\n ],\n [\n -99.6320641494822,\n 46.96241959544966\n ],\n [\n -99.99744313272754,\n 48.133167378584716\n ],\n [\n -102.2714095903581,\n 48.758327670163794\n ],\n [\n -107.84693201879426,\n 48.8300878096519\n ],\n [\n -115.06578880812711,\n 48.98607018582902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":865992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - 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2022 - Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions","interactions":[],"lastModifiedDate":"2022-10-25T14:01:48.041611","indexId":"70237820","displayToPublicDate":"2022-10-22T08:52:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions","docAbstract":"Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113307","usgsCitation":"Enderlin, E., Elkin, C., Gendreau, M., Marshall, H., O'Neel, S., McNeil, C., Florentine, C., and Sass, L., 2022, Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions: Remote Sensing of Environment, v. 283, 113307, 17 p., https://doi.org/10.1016/j.rse.2022.113307.","productDescription":"113307, 17 p.","ipdsId":"IP-141547","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113307","text":"Publisher Index Page"},{"id":486323,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1WHK","text":"USGS data release","linkHelpText":"Point Raw Glaciological Data: Ablation Stake, Snow Pit, and Probed Snow Depth Data on USGS Benchmark Glaciers"},{"id":408693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed, Wolverine Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.9169563546656,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.36296558422342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.84207609134586,\n 60.42699332401864\n ],\n [\n -148.8772978314237,\n 60.46763185035496\n ],\n [\n -148.92384084509808,\n 60.44126913255184\n ],\n [\n -148.95214402908923,\n 60.43009729404224\n ],\n [\n -148.9219539661653,\n 60.37666770702921\n ],\n [\n -148.9112616522131,\n 60.37542411458642\n ],\n [\n -148.84207609134586,\n 60.42699332401864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":855759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elkin, Colten","contributorId":298508,"corporation":false,"usgs":false,"family":"Elkin","given":"Colten","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gendreau, Madeline","contributorId":298509,"corporation":false,"usgs":false,"family":"Gendreau","given":"Madeline","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, H. P.","contributorId":298510,"corporation":false,"usgs":false,"family":"Marshall","given":"H. P.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":855763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":855766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237674,"text":"sir20225059 - 2022 - Virginia Bridge Scour Pilot Study—Hydrological Tools","interactions":[],"lastModifiedDate":"2023-03-03T15:46:19.895694","indexId":"sir20225059","displayToPublicDate":"2022-10-18T13:50:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5059","displayTitle":"Virginia Bridge Scour Pilot Study—Hydrological Tools","title":"Virginia Bridge Scour Pilot Study—Hydrological Tools","docAbstract":"Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408487,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"},{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Warming temperatures and increasing disturbance by wildfire and extreme weather events is driving permafrost change across northern latitudes. The state of permafrost varies widely in space and time, depending on landscape, climate, hydrologic, and ecological factors. Despite its importance, few approaches commonly measure and monitor the changes in deep (>1 m) permafrost conditions with high spatial resolution. Here, we use electrical resistivity tomography surveys along two transects in interior Alaska previously disturbed by wildfire and more recently by warming temperatures and extreme precipitation. Long-term point observations of permafrost depth, temperature, and water content inform geophysical measurements which, in turn, are used to extrapolate interpretations over larger areas and with high spatial fidelity. We contrast gradual loss of recently formed permafrost driven by warmer temperatures and increased snowfall, with rapid permafrost loss driven by changes in air temperature, snow depth, and extreme summer precipitation in 2014.
Depressional wetlands in the Prairie Pothole Region of North America have a long history of investigation owing to their importance in maintaining migratory-bird populations, especially waterfowl. One area of particularly intensive study is the Cottonwood Lake study area in Stutsman County, North Dakota. Studies at the Cottonwood Lake study area began in 1967 and continue through the present (2022). During this period of scientific discovery, meteorological conditions at the Cottonwood Lake study area varied greatly and included one of the most severe droughts of the 20th century and one of the wettest periods in the past 500 years.
Persistent wet conditions that began in 1993 have contributed to state changes in many of the study area’s larger wetlands to lake-like conditions, whereas the smaller wetlands returned to seasonally ponded conditions during relatively dry years interspersed within the longer-term wet period. Additionally, some nonwetland areas of the study area developed wetland plant, hydrology, and soil characteristics during the 1993-to-present (2022) wet period. The persistently high stages of water in the larger wetlands since 1993 contributed to a buildup of dissolved solids and increases in salinity with time following an initial decrease in salinity caused by the dilution of dissolved solids within a larger volume of water. During 2021, drought conditions similar to the 1988 to 1992 period may develop if conditions persist. However, meteorological changes during the past 30 years have persisted long enough to be considered a change in climate conditions at the study area and, if such wet conditions continue, would represent a change from conditions that occurred in the past two millennia.
During the period of study covered in this report (1967–2021), biotic communities responded in a variety of ways to subtle and marked changes in ponded-water depths, permanence, and salinity among the different wetland types in the study area. This report provides background information on the Cottonwood Lake study area and its context within the Prairie Pothole Region, documents techniques used to quantify environmental conditions and biotic communities, describes major trends that have been observed, presents significant findings as “lessons learned,” discusses recent modeling advances, and highlights key messages to managers. The Wetland Continuum concept was used as a framework to place research findings within an ecological context and to highlight the dynamic nature of prairie-pothole wetland ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1874","usgsCitation":"Mushet, D.M., Euliss, N.H., Jr., Rosenberry, D.O., LaBaugh, J.W., Bansal, S., Levy, Z.F., McKenna, O.P., McLean, K.I., Mills, C.T., Neff, B.P., Nelson, R.D., Solensky, M.J., and Tangen, B.A., 2022, Lessons learned from wetlands research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021: U.S. Geological Survey Professional Paper 1874, 162 p., https://doi.org/10.3133/pp1874.","productDescription":"Report: xi, 162 p.; 19 Data Releases","numberOfPages":"180","onlineOnly":"Y","ipdsId":"IP-125548","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":407725,"rank":23,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTOFCW","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Specific conductance (ver. 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release","linkHelpText":"Diurnal patterns of methane flux from a depressional, seasonal wetland"},{"id":407713,"rank":12,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TX3CJ7","text":"USGS data release","linkHelpText":"Dissolved greenhouse gas concentrations and fluxes from Wetlands P7 and P8 of the Cottonwood Lake study area, Stutsman County, North Dakota, 2015"},{"id":407712,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DZ06GR","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Aerial imagery"},{"id":407711,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75X27TW","text":"USGS data release","linkHelpText":"Hydraulic conductivity data for piezometers near Cottonwood Lake study area, North Dakota (1978–2017)"},{"id":501888,"rank":25,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113769.htm","linkFileType":{"id":5,"text":"html"}},{"id":407720,"rank":18,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YKWWSZ","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Groundwater elevations (ver. 2.0)"},{"id":407718,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MULBED","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Water surface elevations (ver. 2.0)"},{"id":408378,"rank":24,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1874/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":407710,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58JJ2","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Water Chemistry—Wells—In situ measurements"},{"id":407704,"rank":4,"type":{"id":34,"text":"Image 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data release","linkHelpText":"Cottonwood Lake study area—Boundary polygon"},{"id":407705,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J101DQ","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Wetland vegetation zones"},{"id":407708,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69GTD","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Digital elevation model with topobathy"},{"id":407707,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78W3BH2","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Water chemistry—Wells"},{"id":407706,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN437S","text":"USGS data release","linkHelpText":"Cottonwood Lake study area—Water chemistry—Wetlands"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":2033,\"properties\":{\"name\":\"Stutsman\",\"state\":\"ND\"},\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.2669,47.3268],[-98.8466,47.327],[-98.8392,47.327],[-98.8232,47.3272],[-98.8152,47.3271],[-98.4991,47.327],[-98.467,47.3266],[-98.4677,47.2402],[-98.4685,46.9788],[-98.4412,46.9789],[-98.4396,46.6296],[-98.7894,46.6294],[-99.0379,46.6309],[-99.1616,46.6317],[-99.4122,46.6316],[-99.4498,46.6319],[-99.4477,46.8044],[-99.4476,46.9788],[-99.4821,46.9795],[-99.4824,47.0089],[-99.4822,47.0162],[-99.4821,47.0249],[-99.4826,47.0396],[-99.4827,47.1558],[-99.4801,47.3267],[-99.2669,47.3268]]]}}]}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
While nonstationary flood frequency analysis (NSFFA) methods have proliferated, few studies have rigorously compared them for modeling changes in both the central tendency and variability of annual peak-flow series, also known as the annual maximum series (AMS), in hydrologically diverse areas. Through Monte Carlo experiments, we appraise five methods for updating estimates of 10- and 100-year floods at gauged sites using synthetic records based on sample moments and change trajectories of observed AMS in the conterminous United States (CONUS). We compare two methods that consider changes in both central tendency and variability - a Gamma generalized linear model estimated with weighted least squares and the Generalized Additive Model for Location, Scale, Shape (GAMLSS) - with a distribution-free approach (quantile regression), and baseline cases assuming stationarity or only changes in central tendency.
‘Trend-space’ plots identify realistic AMS changes for which modeling trends in both central tendency and variability were warranted based on fractional root mean squared errors (fRMSE). They also reveal statistical properties of AMS under which NSFFA models perform especially well or poorly. For instance, quantile regression performed especially well (poorly) under strong negative (positive) skewness. Although the nonstationary LP3 distribution accommodates most AMS with trends well, the sensitivity of NSFFA model performance to different sample moments and trends suggests the need for more flexibility in prescribing design-flood adjustments in the CONUS. A follow-up comparison of regional NSFFA models pooling at-site AMS would further illuminate NSFFA guidance, especially for AMS with properties less conducive to NSFFA modeling, such as positive skewness and increasing variability.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2021.100115","usgsCitation":"Hecht, J., Barth, N.A., Ryberg, K.R., and Gregory, A., 2022, Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States: Journal of Hydrology X, v. 17, 100115, 24 p., https://doi.org/10.1016/j.hydroa.2021.100115.","productDescription":"100115, 24 p.","ipdsId":"IP-129280","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":446110,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2021.100115","text":"Publisher Index Page"},{"id":435655,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PVRCDS","text":"USGS data release","linkHelpText":"Data for simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States"},{"id":408467,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.32031249999997,\n 24.5271348225978\n ],\n [\n -65.91796875,\n 24.5271348225978\n ],\n [\n -65.91796875,\n 50.958426723359935\n ],\n [\n -128.32031249999997,\n 50.958426723359935\n ],\n [\n -128.32031249999997,\n 24.5271348225978\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hecht, Jory Seth","contributorId":298019,"corporation":false,"usgs":true,"family":"Hecht","given":"Jory Seth","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":854875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barth, Nancy A. 0000-0002-7060-8244 nabarth@usgs.gov","orcid":"https://orcid.org/0000-0002-7060-8244","contributorId":298020,"corporation":false,"usgs":true,"family":"Barth","given":"Nancy","email":"nabarth@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":854876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gregory, Angela 0000-0002-9905-1240","orcid":"https://orcid.org/0000-0002-9905-1240","contributorId":45018,"corporation":false,"usgs":true,"family":"Gregory","given":"Angela","email":"","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854938,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238372,"text":"70238372 - 2022 - Hydrologic recovery after wildfire: A framework of approaches, metrics, criteria, trajectories, and timescales","interactions":[],"lastModifiedDate":"2022-11-18T12:36:54.183072","indexId":"70238372","displayToPublicDate":"2022-10-17T06:32:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12968,"text":"Journal of Hydrology and Hydromechanics","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic recovery after wildfire: A framework of approaches, metrics, criteria, trajectories, and timescales","docAbstract":"Deviations in hydrologic processes due to wildfire can alter streamflows across the hydrograph, spanning peak flows to low flows. Fire-enhanced changes in hydrologic processes, including infiltration, interception, and evapotranspiration, and the resulting streamflow responses can affect water supplies, through effects on the quantity, quality, and timing of water availability. Post-fire shifts in hydrologic processes can also alter the timing and magnitude of floods and debris flows. The duration of hydrologic deviations from a pre-fire condition or function, sometimes termed hydrologic recovery, is a critical concern for land, water, and emergency managers. We reviewed and summarized terminology and approaches for defining and assessing hydrologic recovery after wildfire, focusing on statistical and functional definitions. We critically examined advantages and drawbacks of current recovery assessment methods, outline challenges to determining recovery, and call attention to selected opportunities for advancement of post-fire hydrologic recovery assessment. Selected challenges included hydroclimatic variability, post-fire land management, and spatial and temporal variability. The most promising opportunities for advancing assessment of hydrologic recovery include: (1) combining statistical and functional recovery approaches, (2) using a greater diversity of post-fire observations complemented with hydrologic modeling, and (3) defining optimal assemblages of recovery metrics and criteria for common hydrologic concerns and regions.","language":"English","publisher":"Institute of Hydrology of the Slovak Academy of Sciences","doi":"10.2478/johh-2022-0033","usgsCitation":"Ebel, B., Wagenbrenner, J.W., Kinoshita, A.M., and Bladon, K.D., 2022, Hydrologic recovery after wildfire: A framework of approaches, metrics, criteria, trajectories, and timescales: Journal of Hydrology and Hydromechanics, v. 70, no. 4, p. 388-400, https://doi.org/10.2478/johh-2022-0033.","productDescription":"13 p.","startPage":"388","endPage":"400","ipdsId":"IP-145116","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":446116,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2478/johh-2022-0033","text":"Publisher Index Page"},{"id":409437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"70","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":857271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagenbrenner, Joseph W. 0000-0003-3317-5141","orcid":"https://orcid.org/0000-0003-3317-5141","contributorId":264444,"corporation":false,"usgs":false,"family":"Wagenbrenner","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":857272,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kinoshita, Alicia M.","contributorId":245287,"corporation":false,"usgs":false,"family":"Kinoshita","given":"Alicia","email":"","middleInitial":"M.","affiliations":[{"id":49134,"text":"San Diego State University, California","active":true,"usgs":false}],"preferred":false,"id":857273,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bladon, Kevin D.","contributorId":298225,"corporation":false,"usgs":false,"family":"Bladon","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":857274,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237484,"text":"sir20225095 - 2022 - Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","interactions":[],"lastModifiedDate":"2024-05-07T20:58:03.278223","indexId":"sir20225095","displayToPublicDate":"2022-10-12T10:35:13","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5095","displayTitle":"Updated Annual and Semimonthly Streamflow Statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Southwestern Idaho, 2021","title":"Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021","docAbstract":"The U.S. Geological Survey, in cooperation with the Bureau of Land Management (BLM), continued streamflow data collection in water years 2013–21 to update daily streamflow regressions and annual and semimonthly streamflow statistics initially developed in 2012 for streams designated as “wild,” “scenic,” or “recreational” under the National Wild and Scenic Rivers System in the Owyhee Canyonlands Wilderness in southwestern Idaho. To sustain “outstanding remarkable values” in the Owyhee Canyonlands Wilderness, BLM determined that maintaining specific streamflow conditions in rivers was important for sustaining ecological health, recreational opportunities, and water demands for stock water and irrigation in a region with increased pressure from upstream land development. Streamflow statistics previously developed using regional regressions based on limited number of streamgages and generalized basin characteristics were determined to inaccurately represent hydrologic characteristics in the Owyhee Canyonlands Wilderness.
In this study, updated streamflow regressions and statistics are provided for 11 partial-record sites in the Owyhee Canyonlands Wilderness using 311 additional streamflow measurements. A partial-record Maintenance of Variance Extension, Type 1 (MOVE.1) streamflow regression method was used to relate discrete streamflow measurements collected at partial-record sites with daily mean streamflow at nearby index sites. The updated regressions were used to estimate a synthetic daily mean streamflow record at each partial-record site for the period of record of the selected index site. The computed synthetic streamflow record was then used to determine annual and semimonthly streamflow statistics at each partial-record site. Annual bankfull streamflow statistics were calculated at each partial-record site using the computed bankfull streamflow at the selected index site and the updated streamflow regression.
Additional streamflow measurements representing a larger range of hydrologic conditions since 2012, reevaluation of index site selection, and updated regression techniques improved streamflow statistic estimates in the Owyhee Canyonlands Wilderness. Regression performance was evaluated based on the coefficient of determination (R2) between the partial-record and index sites, percent bias, and similarity of basin characteristics between the selected index site and the partial-record site. Generally, the updated regressions performed well for partial-record sites with an index site located upstream or downstream on the same stream. Regression performance was degraded and less robust for index sites located farther away from the corresponding partial-record site. Additional streamflow measurements at partial-record sites with few measurements over a small range in hydrologic conditions could improve regression performance and reduce prediction intervals. Furthermore, additional index sites in the Owyhee Canyonlands Wilderness could improve the updated streamflow regressions and statistics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225095","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Dudunake, T.J., and Ducar, S.D., 2022, Updated annual and semimonthly streamflow statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, southwestern Idaho, 2021 (ver. 1.1, May 2024): U.S. Geological Survey Scientific Investigations Report 2022–5095, 31 p., https://doi.org/10.3133/sir20225095.","productDescription":"Report: viii, 31 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128129","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":408220,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.XML"},{"id":408218,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJA24","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow regressions and annual and semimonthly exceedance probability statistics for Wild and Scenic Rivers, Owyhee Canyonlands Wilderness, Idaho"},{"id":408217,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5095/sir20225095.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5095"},{"id":408221,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225095/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5095"},{"id":408219,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5095/images"},{"id":428468,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5095/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":408216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5095/coverthb2.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Owyhee Canyonlands Wilderness","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.02636718749999,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 42.00848901572399\n ],\n [\n -114.14794921875,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 43.50872101129684\n ],\n [\n -117.02636718749999,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
A thorough understanding of how urbanization affects stream hydrology is crucial for effective and sustainable water management, particularly in rapidly urbanizing regions. This study presents a comprehensive analysis of changes in streamflow response to rainfall events across a rural to urban gradient in the semi-arid area of Denver, Colorado. We used 8 years of April to October instantaneous streamflow data in 21 watersheds ranging in size from 0.8 to 90 km2 and with impervious areas ranging from 1% to 47%. With these data, we applied a semi-automated method to identify a total of 2877 streamflow responses, which were analysed for event-based metrics of peak flow, runoff depth, runoff to rainfall ratio, time to peak, duration and number of streamflow responses to rainfall events. We also determined whether streamflow responses could be predicted by a precipitation threshold. Watersheds with >10% impervious cover had a precipitation threshold of 1–2 mm/hr needed to produce a streamflow response, compared to thresholds of 4–36 mm/hr for watersheds with less than 10% impervious surface cover. This lower precipitation threshold in more impervious watersheds led to more frequent streamflow responses. On average, streamflow responses had shorter duration and higher peak flows in watersheds with more impervious surface cover. In contrast to other regions, runoff depth, runoff to rainfall ratio and time to peak either gave mixed results or did not vary significantly with imperviousness. These alterations in streamflow response to rainfall events indicate the specific ways that urban development changes how streams respond to rain events in a semi-arid setting. This work points to the need for local adaptation of stormwater management to mitigate the effects of streamflow changes with urbanization.