The Bushy Park Reservoir is a relatively shallow impoundment in southeastern South Carolina. The reservoir, located under a semi-tropical climate, is the principal water supply for the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there was an adequate supply of freshwater in the reservoir in 2022, water-quality concerns are present over taste-and-odor and saltwater-intrusion issues. From 2013 to 2015, the U.S. Geological Survey (USGS), in cooperation with the Charleston Water System, engaged in a multi-year study of the hydrology and hydrodynamics of Bushy Park Reservoir to better understand factors affecting water-quality conditions in the reservoir. As part of this study, Charleston Water System worked with Tetra Tech, Inc., a consulting and engineering firm, to develop a Bushy Park Reservoir hydrodynamic and water-quality modeling framework, built upon earlier efforts by both Tetra Tech and the U.S. Army Corps of Engineers. At the completion of the new modeling framework, the USGS was requested to evaluate the calibrated hydrodynamic and water-quality model.
The Bushy Park Reservoir Environmental Fluid Dynamics Code (EFDC) model was calibrated for the time period from January 1, 2012, to December 31, 2015. The general modeling approach for the newly revised modeling framework, as briefly detailed in this report, was developed with EFDC. The EFDC is a grid-based modeling package that can simulate three-dimensional flow, transport, and water quality in surface-water systems. This report evaluated the capacity of Tetra Tech’s Bushy Park EFDC model to simulate water discharge, water circulation, surface elevations, temperature, salinity, and other water-quality parameters.
The USGS model review focused specifically on the following criteria: (1) determine if the model, with additional effort, could be developed into an adequate planning tool for Bushy Park Reservoir; (2) assess the capacity of the model to specifically address water-quality issues in the reservoir related to taste-and-odor and saltwater intrusions; and, (3) evaluate three preliminary water-management scenarios related to reduced water withdrawals in the reservoir and the effect on saltwater intrusion.
Overall, the model was able to simulate discharge, flow velocity, and water-surface elevations with generally good agreement between the simulated and measured values. Specifically, the model was able to demonstrate good agreement for discharge at two USGS continuous discharge locations (USGS station 02172002; USGS station 02172040), with Wilmott index of agreements of 0.86 and 0.75, respectively. A total of seven USGS streamgages, located on the West Branch of the Cooper River, Durham Canal, and the Cooper River, were available for water-surface elevations, with index of agreements ranging from 0.74 to 0.99. However, model-simulated water-surface elevation ranges were appreciably high (compared to measured ranges) for two locations near Pinopolis Dam, farthest upstream on the West Branch of the Cooper River. This result may indicate that too much simulated tidal energy propagated through the model domain.
For water temperature, 16 calibration stations were available for at least part of the 4-year simulation. The index of agreement range for temperature comparisons was from 0.95 to 1.00, indicating excellent agreement between the measured and simulated results. One of the primary future applications for the Bushy Park Reservoir EFDC model is to determine the extent of saltwater intrusions. A wide range in the salinity prediction quality was simulated with the model. The prediction quality ranged from an index of agreement of 0.15 at Cooper River approximately 2.75 miles southeast of the Tee, South Carolina, to 0.92 at West Branch Cooper River near Moncks Corner, South Carolina. Although the model did not accurately simulate some of the larger salinity deviations resulting during individual hydrologic events, the seasonal salinity trends were adequately simulated with the model during the study period (2012–15). Therefore, it may be difficult to simulate extreme hydrologic events, such as during large storms, where high salinity water is exchanged with Bushy Park Reservoir. There was agreement in model simulation with the measured data either on the quantitative index of agreement values or qualitative agreement in the seasonal salinity data trends.
For water quality, the index of agreement values were generally low for total nitrogen, ammonia, nitrate, total Kjeldahl nitrogen, total phosphorus, and orthophosphate. Although general trends were adequately simulated at specific stations, particularly for Bushy Park Reservoir, the model-simulated fit was low across all the constituents described above with index of agreements usually below 0.50. A limitation for simulating nutrient concentrations across the model domain was the lack of characterization for the constituents directly entering Bushy Park Reservoir, or the lack of data directly attributed to the boundary condition (for example, the Cooper River). The other two calibrated water-quality constituents (besides the nutrients mentioned above) were dissolved oxygen and chlorophyll a. Dissolved oxygen varied from index of agreement values from 0.58 to 0.94 for 11 stations, generally indicating agreement with the available measured data. Chlorophyll a, calibrated for seven stations, had a wider range from 0.11 to 0.74 for the index of agreement.
With the current modeling framework, taste-and-odor events, related to cyanobacterial blooms, cannot be directly simulated. However, indirect estimates of cyanobacteria concentrations may be obtained by using the chlorophyll a model outputs, which represent total phytoplankton biomass, and the phytoplankton biovolume data by group (diatoms, green algae, cyanobacteria and others) collected from 2012 to 2015. For the Bushy Park Reservoir modeling framework to be used directly for taste-and-odor issues, cyanobacteria must be simulated and calibrated based on observations of cyanobacteria biomass concentrations. In addition to the cyanobacteria sampling conducted within the reservoir between 2012 and 2015, the new model calibration would also require new algae biomass data-collection efforts to characterize the external sources of cyanobacteria entering the Bushy Park Reservoir from tributaries, as well as the internal cycling, production, and decay of cyanobacteria in the hydrologic system.
Further improvements to the EFDC model would include expanding the collection of boundary condition datasets, such as water-quality monitoring to determine improved nutrient loads into the model domain. Along with improved water-quality monitoring for the major boundary conditions, continuous discharge, for both Foster Creek and the Back River, would further constrain the flow balance and the loads into Bushy Park Reservoir. In addition to better boundary-condition characterization, it is important to better characterize possible shortcomings specifically to the model domain, such as the grid resolution, bathymetry, and numerical hydrodynamic errors. Further consideration of the model may involve a sensitivity analysis to determine if errors in the simulation outputs, such as discharge, water-surface elevations, and salinity, were more likely caused by poor boundary condition characterization or, specifically, the model setup.
Three model scenarios were run with the revised Bushy Park Reservoir model: (1) reduced withdrawals from one of the large intake-discharge locations for Bushy Park Reservoir, the Williams Station; (2) elevated (above background levels) ocean water level causing saltwater intrusion from the ocean through Durham Canal into Bushy Park Reservoir; and (3) overtopping of the Back River Dam at the southernmost end of Bushy Park Reservoir. For the reduced withdrawals scenarios, the largest shift in flow resulted near the Williams Station intake, with the next largest flow change at the southern end of Bushy Park Reservoir, and a net increase in flow out of the Bushy Park Reservoir to the Cooper River by way of the Durham Canal. The effect resulting from scenario 3 on water quality and salinity was small, with larger increases for dissolved oxygen than other constituents at several monitoring stations. For the two scenarios related to saltwater intrusion (including dam overtopping), the changes in salinity generally were found to dissipate in the following 2 weeks and generally back to baseline salinity conditions within 3 months. This result did vary depending on the severity of the storm or length of the dam overtopping event.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221079","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Smith, E.A., Akasapu-Smith, M., Petkewich, M.D., and Conrads, P.A., 2022, Evaluation of the Bushy Park Reservoir three-dimensional hydrodynamic and water-quality model, South Carolina, 2012–15: U.S. Geological Survey Open-File Report 2022–1079, 35 p., https://doi.org/10.3133/ofr20221079.","productDescription":"Report: ix, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087955","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501828,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113612.htm","linkFileType":{"id":5,"text":"html"}},{"id":407629,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1079/coverthb.jpg"},{"id":407630,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.pdf","text":"Report","size":"5.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1079"},{"id":407632,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1079/ofr20221079.XML"},{"id":407633,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1079/images/"},{"id":407634,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NG4NVX","text":"USGS data release","linkHelpText":"Water quality data for Bushy Park Reservoir, South Carolina 2013–2015"},{"id":407631,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221079/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1079"}],"country":"United States","state":"South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.39794921875,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 32.676372772089806\n ],\n [\n -79.661865234375,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 33.458942753687616\n ],\n [\n -80.39794921875,\n 32.676372772089806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road, Suite 129
Columbia, SC 29210
The homogenization of freshwater ecosystems and their biological communities has emerged as a prevalent and concerning phenomenon because of the loss of ecosystem multifunctionality. The millions of prairie-pothole wetlands scattered across the Prairie Pothole Region (hereafter PPR) provide critical ecosystem functions at local, regional, and continental scales. However, an estimated loss of 50% of historical wetlands and the widespread conversion of grasslands to cropland make the PPR a heavily modified landscape. Therefore, it is essential to understand the current and potential future stressors affecting prairie-pothole wetland ecosystems in order to conserve and restore their functions. Here, we describe a conceptual model that illustrates how (a) historical wetland losses, (b) anthropogenic landscape modifications, and (c) climate change interact and have altered the variability among remaining depressional wetland ecosystems (i.e., ecosystem homogenization) in the PPR. We reviewed the existing literature to provide examples of wetland ecosystem homogenization, provide implications for wetland management, and identify informational gaps that require further study. We found evidence for spatial, hydrological, chemical, and biological homogenization of prairie-pothole wetlands. Our findings indicate that the maintenance of wetland ecosystem multifunctionality is dependent on the preservation and restoration of heterogenous wetland complexes, especially the restoration of small wetland basins.
","language":"English","publisher":"MPDI","doi":"10.3390/w14193106","usgsCitation":"McLean, K., Mushet, D., and Sweetman, J., 2022, Climate and land use driven ecosystem homogenization in the Prairie Pothole Region: Water, v. 14, no. 19, 3106, 19 p., https://doi.org/10.3390/w14193106.","productDescription":"3106, 19 p.","ipdsId":"IP-142441","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446248,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14193106","text":"Publisher Index Page"},{"id":408480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.712890625,\n 43.58039085560784\n ],\n [\n -94.74609375,\n 41.50857729743935\n ],\n [\n -92.548828125,\n 41.77131167976407\n ],\n [\n -92.900390625,\n 43.32517767999296\n ],\n [\n -94.04296874999999,\n 45.460130637921004\n ],\n [\n -95.537109375,\n 48.45835188280866\n ],\n [\n -96.85546875,\n 49.61070993807422\n ],\n [\n -96.767578125,\n 50.401515322782366\n ],\n [\n -97.55859375,\n 51.069016659603896\n ],\n [\n -97.998046875,\n 50.51342652633956\n ],\n [\n -99.140625,\n 51.12421275782688\n ],\n [\n -99.931640625,\n 51.45400691005982\n ],\n [\n -102.216796875,\n 52.696361078274485\n ],\n [\n -104.501953125,\n 54.213861000644926\n ],\n [\n -107.22656249999999,\n 54.41892996865827\n ],\n [\n -111.533203125,\n 55.57834467218206\n ],\n [\n -114.78515624999999,\n 54.97761367069628\n ],\n [\n -115.13671875,\n 52.74959372674114\n ],\n [\n -115.13671875,\n 50.90303283111257\n ],\n [\n -113.90625,\n 49.095452162534826\n ],\n [\n -111.796875,\n 48.22467264956519\n ],\n [\n -110.56640625,\n 48.69096039092549\n ],\n [\n -109.16015624999999,\n 48.45835188280866\n ],\n [\n -107.40234375,\n 48.80686346108517\n ],\n [\n -105.380859375,\n 48.980216985374994\n ],\n [\n -101.77734374999999,\n 47.989921667414194\n ],\n [\n -100.986328125,\n 47.754097979680026\n ],\n [\n -100.37109375,\n 46.800059446787316\n ],\n [\n -100.546875,\n 45.460130637921004\n ],\n [\n -99.66796875,\n 43.96119063892024\n ],\n [\n -98.96484375,\n 43.45291889355465\n ],\n [\n -96.85546875,\n 42.8115217450979\n ],\n [\n -95.712890625,\n 43.58039085560784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":854916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon","contributorId":298028,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":854917,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237649,"text":"70237649 - 2022 - Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US","interactions":[],"lastModifiedDate":"2022-10-18T15:27:57.063434","indexId":"70237649","displayToPublicDate":"2022-10-01T10:24:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US","docAbstract":"Peatlands play a disproportionate role in the global carbon cycle. However, many peatlands have been ditched to lower the water table and converted into agriculture, which contributes to anthropogenic greenhouse gas emissions. Hydrologic restoration of drained peatlands could offset greenhouse gas emissions from these actions, but field examples that consider various greenhouse gases are still rare. Here, we examined emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from soils in drained shrub bogs in North Carolina, USA, before and after hydrologic restoration. We used static chamber methods and a before-and-after, control-impact (BACI) experimental design. We found that hydrologic manipulation (akin to restoration) increased water table levels by 65%, even with the impact of two hurricanes before and one after hydrologic manipulation. Increased water table levels led to a 58% decrease in CO2 fluxes, and an increase in CH4 (251%) and N2O fluxes (85%). Water table depth and soil temperature explained 43% of variation in CO2, while water table depth explained 25% and 18% of variation in CH4 and N2O fluxes, respectively. Despite the increases in CH4 and N2O, the higher magnitude of fluxes and large decline in CO2 lead to an overall lowering of greenhouse gas emissions after hydrologic restoration. Our results suggest that raising the water table in this shrub bog peatland decreased overall greenhouse gas emissions, illustrating that hydrologic restoration of peatlands can be a valuable climate mitigation practice.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-022-01605-y","usgsCitation":"Armstrong, L., Peralta, A., Krauss, K., Cormier, N., Moss, R., Soderholm, E., McCall, A., Pickens, C., and Ardon, M., 2022, Hydrologic restoration decreases greenhouse gas emissions from shrub bog peatlands in southeastern US: Wetlands, v. 42, 81, 10 p., https://doi.org/10.1007/s13157-022-01605-y.","productDescription":"81, 10 p.","ipdsId":"IP-135745","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Pocosin Lakes National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.61453247070312,\n 35.60930140634475\n ],\n [\n -76.16958618164062,\n 35.60930140634475\n ],\n [\n -76.16958618164062,\n 35.862343734896484\n ],\n [\n -76.61453247070312,\n 35.862343734896484\n ],\n [\n -76.61453247070312,\n 35.60930140634475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2022-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Armstrong, Luise","contributorId":298009,"corporation":false,"usgs":false,"family":"Armstrong","given":"Luise","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":854835,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peralta, Ariane","contributorId":298010,"corporation":false,"usgs":false,"family":"Peralta","given":"Ariane","email":"","affiliations":[{"id":36317,"text":"East Carolina University","active":true,"usgs":false}],"preferred":false,"id":854836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854837,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cormier, N. 0000-0003-2453-9900","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":221147,"corporation":false,"usgs":false,"family":"Cormier","given":"N.","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":854838,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moss, Rebecca 0000-0002-7599-9758 mossr@usgs.gov","orcid":"https://orcid.org/0000-0002-7599-9758","contributorId":169722,"corporation":false,"usgs":true,"family":"Moss","given":"Rebecca","email":"mossr@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":854839,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":854840,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCall, Aaron","contributorId":298012,"corporation":false,"usgs":false,"family":"McCall","given":"Aaron","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854841,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pickens, Christine","contributorId":298013,"corporation":false,"usgs":false,"family":"Pickens","given":"Christine","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":854842,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":854843,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238656,"text":"70238656 - 2022 - Estuarine Geomorphology, Circulation, and Mixing","interactions":[],"lastModifiedDate":"2022-12-02T13:28:35.628529","indexId":"70238656","displayToPublicDate":"2022-10-01T07:24:39","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"2","title":"Estuarine Geomorphology, Circulation, and Mixing","docAbstract":"To understand the processes affecting the distribution and cycles of particulates, pollutants, nutrients, and organisms in estuaries, it is insufficient to focus solely on the biological and chemical aspects of the processes. Water sources and movements (e.g. evaporation, precipitation, riverine discharge, submarine ground water discharge, wetland hydrology, and tidal exchange) as well as other hydrodynamic aspects of coastal systems, including circulation patterns, stratification, mixing and flushing, must also be considered. When hydrodynamic changes occur quickly relative to biological, geological, and chemical transformations, they become the dominant controlling factors of many ecological processes in estuaries (Officer 1980), and it is now widely recognized that a thorough understanding of the marine estuarine ecology requires comprehensive knowledge and integration of physical processes affecting the system. Using the terminology of a shallow-water oceanographer, this chapter aims to organize, classify, and describe some of these important physical characteristics and processes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Estuarine Ecology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","usgsCitation":"Snedden, G., Cable, J., and Kjerfve, B., 2022, Estuarine Geomorphology, Circulation, and Mixing, chap. 2 of Estuarine Ecology, p. 16-35.","productDescription":"20 p.","startPage":"16","endPage":"35","ipdsId":"IP-128034","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":409988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"3rd edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Snedden, Gregg 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":213411,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cable, Jaye E.","contributorId":299602,"corporation":false,"usgs":false,"family":"Cable","given":"Jaye E.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":858213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kjerfve, Bjorn","contributorId":299603,"corporation":false,"usgs":false,"family":"Kjerfve","given":"Bjorn","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":858214,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239275,"text":"70239275 - 2022 - HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool","interactions":[],"lastModifiedDate":"2023-01-06T13:05:21.53046","indexId":"70239275","displayToPublicDate":"2022-09-30T07:01:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9110,"text":"Frontiers in Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool","docAbstract":"Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.
In an age of both big data and increasing strain on water resources, sound management decisions often rely on numerical models. Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connections between human activities and water resource impacts. However, despite many recent advances in model data assimilation and uncertainty quantification, the process of constructing numerical models remains laborious, expensive, and opaque, often precluding their use in decision making. Modflow-setup aims to provide rapid and consistent construction of MODFLOW groundwater models through robust and repeatable automation. Common model construction tasks are distilled in an open-source, online code base that is tested and extensible through collaborative version control. Input to Modflow-setup consists of a single configuration file that summarizes the workflow for building a model, including source data, construction options, and output packages. Source data providing model structure and parameter information including shapefiles, rasters, NetCDF files, tables, and other (geolocated) sources to MODFLOW models are read in and mapped to the model discretization, using Flopy and other general open-source scientific Python libraries. In a few minutes, an external array-based MODFLOW model amenable to parameter estimation and uncertainty quantification is produced. This paper describes the core functionality of Modflow-setup, including a worked example of a MODFLOW 6 model for evaluating pumping impacts to a lake in central Wisconsin, United States.
The major streams in Oklahoma, and the alluvial aquifers associated with those major streams, are important resources for the 39 federally recognized Tribes in Oklahoma. Many Tribal Governments are interested in developing water-management plans (hereinafter referred to as “water plans”) to preserve water resources for the future. This report provides a general overview of the types of information and data needed to prepare comprehensive water plans. To assist Tribes in the development of water plans, the U.S. Geological Survey, in cooperation with the Bureau of Indian Affairs, has outlined the steps necessary for creation of such plans.
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\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
A multiscale, multiprocess modeling approach was applied to the Wisconsin Central Sands region in central Wisconsin to quantify the connections between the groundwater system, land use, and lake levels in three seepage lakes in Waushara County, Wisconsin: Long and Plainfield (The Plainfield Tunnel Channel Lakes), and Pleasant Lakes. A regional groundwater-flow model, the Newton Raphson formulation of the U.S. Geological Survey modular finite-difference flow model groundwater-modeling package (MODFLOW-NWT), centered on the lakes, was used to extend regional surface-water boundaries to provide boundary conditions for two focused inset models, in the hydrologic simulation modeling package (MODFLOW 6), at higher resolution around the lakes. Land use and groundwater use were simulated at a regional scale using the Soil Water Balance model, which provided recharge and water-use boundary conditions for the MODFLOW models. Agricultural irrigation is the primary groundwater use in the area. Land and groundwater-use scenarios representing no irrigation, current (2018) irrigation, and potential future irrigation were simulated with the groundwater-flow model and the lake levels over a 38-year representative climate period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225046","collaboration":"Prepared in cooperation with Wisconsin Department of Natural Resources","usgsCitation":"Fienen, M.N., Haserodt, M.J., Leaf, A.T., and Westenbroek, S.M., 2022, Simulation of regional groundwater flow and groundwater/lake interactions in the Central Sands, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2022–5046, 111 p., https://doi.org/10.3133/sir20225046.","productDescription":"Report: xii, 111 p.; Data Release; Dataset","numberOfPages":"128","onlineOnly":"Y","ipdsId":"IP-130070","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407620,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225046/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":407530,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BVFSGJ","text":"USGS data release","linkHelpText":"MODFLOW models used to simulate groundwater flow in the Wisconsin Central Sands Study Area, 2012-2018"},{"id":407529,"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"},{"id":407528,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5046/images"},{"id":407524,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5046/coverthb.jpg"},{"id":407525,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5046/sir20225046.pdf","text":"Report","size":"92.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5046"},{"id":407527,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5046/sir20225046.XML"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Sands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.12908935546874,\n 43.630111446719226\n ],\n [\n -88.912353515625,\n 43.630111446719226\n ],\n [\n -88.912353515625,\n 44.584599008752015\n ],\n [\n -90.12908935546874,\n 44.584599008752015\n ],\n [\n -90.12908935546874,\n 43.630111446719226\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, Wisconsin 53726
Southeast Florida is highly susceptible to flooding because of its low topography and porous, highly permeable Biscayne aquifer. Rising seas will likely result in increased groundwater levels in parts of Broward County, Florida, that will reduce available soil storage and therefore increase the likelihood of inundation and flooding from precipitation events. Moreover, rising seas may also reduce the capacity of the coastal water-control structures to discharge inland waters to tidal areas, thereby increasing surface-water stage and nearby groundwater levels. Increased rainfall intensity will likely further increase peak surface-water stages and groundwater levels, more quickly fill the reduced soil storage capacity, and increase the likelihood for inundation. Managers and planners in Broward County, Florida, face the challenge of understanding and preparing for the consequent risk to residents, businesses, and critical infrastructure posed by increased sea level and precipitation.
The U.S. Geological Survey, in cooperation with the Broward County Environmental Planning and Resilience Division, has developed a groundwater/surface-water model to evaluate the response of the drainage infrastructure and groundwater system in Broward County to projected increases in sea level and potential changes in precipitation. The model was constructed using Modular Finite-Difference Groundwater Flow Model Newton formulation, with the surface-water system represented using the Surface-Water Routing process and the Urban Runoff process. The aquifer layering and flow parameters rely heavily on existing hydrologic flow models developed by the U.S. Geological Survey for the same model area. The surface-water drainage system within this newly developed model actively simulates the extensive canal network using level-pool routing and active structures representing gates, weirs, culverts, and pumps. Steady-state and transient simulation results represented historical conditions (2013–17). The simulated historical groundwater levels and upstream stage and flow at the primary structures generally captured the behavior of the actual hydrologic system. Simulation results incorporating increased sea level and precipitation were used to evaluate the effects of these projected changes on the surface-water drainage system and wet season groundwater levels.
Four future sea-level scenarios were simulated by modifying the historical inputs for both steady-state and the transient versions of the model to represent mean sea levels of 0.5, 2.0, 2.5, and 3.0 feet (ft) above the North American Vertical Datum of 1988. These mean sea levels correspond to sea-level rises of 1.05, 2.55, 3.05, and 3.55 ft, respectively, above the 2013–17 mean measured tidal stage. Additional simulations represented a 15-percent increase in rainfall rates using the transient model and a 15-percent increase in rainfall recharge using the steady-state model. The simulated results indicated that (1) the effects of increased sea level were more evident in the easternmost, coastal areas of the county where increases in groundwater levels are nearly equivalent to sea-level rise; (2) groundwater levels west of the coastal water-control structures only changed slightly in response to increased sea level for most scenarios; (3) when the control elevations of the gravity-controlled coastal water-control structures were surpassed by sea-level rise, the resulting increases in upstream stage in the connected primary canal resulted in increased groundwater levels that can propagate into the western parts of the county; (4) the historical west-to-east downward gradient in groundwater levels decreased with increased sea level, and groundwater levels were lower in central parts of the county than areas west and east for the higher sea-level scenarios; (5) simulated upstream stage for most of the primary coastal water-control structures increased with increased sea level, with the largest increases occurring at gravity-controlled structures having the lowest control elevations; (6) total flow through the primary structures increased as sea level increased because of additional groundwater leakage into the surface-water network; (7) the 3.0-ft mean sea-level rise scenario resulted in an increase of 37.10 square miles in area having a wet season average depth to groundwater of less than 2 ft, and an increase of 22.84 square miles in newly inundated areas compared to historical simulation results; and (8) a 15-percent increase in rainfall rate for the entire simulation period produced little increase in upstream stages at the primary structures and an increase in total flow through the primary structures proportional to the increase in rainfall.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225074","collaboration":"Prepared in cooperation with the Broward County Environmental Planning and Community Resilience Division","usgsCitation":"Decker, J.D., ed., 2022, Drainage infrastructure and groundwater system response to changes in sea level and precipitation, Broward County, Florida: U.S. Geological Survey Scientific Investigations Report 2022–5074, 99 p., https://doi.org/10.3133/sir20225074.","productDescription":"Report: xi, 99 p.; Data Release","numberOfPages":"116","onlineOnly":"Y","ipdsId":"IP-132378","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":407509,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ITQBFZ","text":"USGS data release","linkHelpText":"MODFLOW-NWT datasets for the simulation of drainage infrastructure and groundwater system response to 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Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The characteristic pattern of variation in flow magnitude, frequency, duration, timing, and rate of change defines the flow regime of rivers and streams and is a key driver of ecosystem processes in fluvial ecosystems. Understanding how freshwater biotic assemblages change across gradients of hydrology and anthropogenic-source disturbance in different streamflow regimes is crucial to managing for sustainable environmental flows and watershed conservation. We compiled long-term (1916–2016) occurrence records for fishes collected in the Ouachita-Ozark Interior Highlands and West Gulf Coastal Plain streams, together with hydrologic metrics calculated from daily streamflow data measured at USGS stream gauging stations (n = 111), to examine important drivers and thresholds for fish assemblage turnover in groundwater (GW), runoff (RO), and intermittent (INT) flow regimes. We also examined the importance of spatial gradients (latitude, longitude, elevation, drainage area) and anthropogenic-source stressors (Hydrologic Disturbance Index; HDI) for fish assemblage turnover using a gradient forest modeling approach. Watershed fragmentation was of high importance for fish assemblage turnover in RO and INT streams, while changes in dam storage were more important for fishes in GW streams. Hydrologic metrics describing seasonal and stochastic properties of daily streamflow (Mag6) were most important for fish assemblage turnover in INT streams. Timing of high flow events had significantly higher importance compared to flow magnitude, duration, and frequency metrics, especially for fish assemblages in GW and INT streams. The frequency and timing of low flow events had high importance for fish assemblage turnover across all stream flow classes, while the magnitude of low flows and the magnitude and rate of change of average flows was most important for INT stream fish assemblages. In addition to benefiting multi-species conservation and management actions through identification of local and regional flow-ecology relationships generalized across different flow regimes, the results of this study provide a better understanding of complex nonlinear threshold effects, which is critical to anticipating changes in aquatic ecosystems and communities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109500","usgsCitation":"Fox, J.T., and Magoulick, D.D., 2022, Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes: Ecological Indicators, v. 144, 109500, 12 p., https://doi.org/10.1016/j.ecolind.2022.109500.","productDescription":"109500, 12 p.","ipdsId":"IP-141446","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109500","text":"Publisher Index Page"},{"id":433199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.0072909145776,\n 32.996331346931214\n ],\n [\n -91.86367305364405,\n 32.95089330614236\n ],\n [\n -91.50751795508893,\n 33.152929696557536\n ],\n [\n -91.43542283894192,\n 33.983279606945345\n ],\n [\n -91.88620071539745,\n 34.66359118761602\n ],\n [\n -90.24911433667984,\n 36.41777106986588\n ],\n [\n -89.39173105840047,\n 37.127658750904004\n ],\n [\n -90.27870353794154,\n 38.050842204957206\n ],\n [\n -92.57312445398881,\n 38.27074078230794\n ],\n [\n -94.07764032833012,\n 38.04668122401986\n ],\n [\n -94.69043619691729,\n 36.83802911087706\n ],\n [\n -96.4642878616655,\n 35.22823697686589\n ],\n [\n -96.5088412530875,\n 33.78407715154462\n ],\n [\n -95.25898895253292,\n 33.910484415403275\n ],\n [\n -94.09522136802627,\n 33.61028174407495\n ],\n [\n -94.0072909145776,\n 32.996331346931214\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"144","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fox, John Tyler","contributorId":341398,"corporation":false,"usgs":false,"family":"Fox","given":"John","email":"","middleInitial":"Tyler","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908352,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238019,"text":"70238019 - 2022 - Understanding the role of initial soil moisture and precipitation magnitude in flood forecast using a hydrometeorological modelling system","interactions":[],"lastModifiedDate":"2022-11-04T12:10:15.277815","indexId":"70238019","displayToPublicDate":"2022-09-27T07:07:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the role of initial soil moisture and precipitation magnitude in flood forecast using a hydrometeorological modelling system","docAbstract":"We adapted the WRF-Hydro modelling system to Hurricane Florence (2018) and performed a series of diagnostic experiments to assess the influence of initial soil moisture and precipitation magnitude on flood simulation over the Cape Fear River basin in the United States. Model results suggest that: (1) The modulation effect of initial soil moisture on the flood peak is non-linear and weakens as precipitation magnitude increases. There is a threshold value of the soil saturation, below and above which the sensitivity of flood peak to the soil moisture differentiates substantially; (2) For model spin-up, streamflow needs longer time to reach the ‘practical’ equilibrium (10%) than the soil moisture and latent heat flux. The model uncertainty from spin-up can propagate through the hydrometeorological modelling chain and get amplified into the flood peak; (3) For ensemble flood modelling with a hydrometeorological system, modelling uncertainty is dominated by the precipitation forecast. Spin-up induced uncertainty can be minimized once the model reaches the ‘practical’ equilibrium.
Seasonal precipitation affects carbon turnover and methane accumulation in karst subterranean estuaries, the region of coastal carbonate aquifers where hydrologic and biogeochemical processes regulate material exchange between the land and ocean. However, the impact that tropical cyclones exert on subsurface carbon cycling within karst landscapes is poorly understood. Here, we present 5-month-long hydrologic and chemical records from 1 and 2 km inland from the coastline within the Ox Bel Ha Cave System in the northeastern Yucatan Peninsula. The record encompasses wet and dry seasons and includes the impact of rainfall during the development of Tropical Storm Hanna in October 2014. Methane accumulated in highest concentrations at the inland site, especially during the wet season preceding the storm. Intense rainfall led to episodic increases in water level and salinity shifts at both sites, indicating a spatially widespread hydrologic response. The most profound storm effect was a ~ 0.8 mg L−1 pulse of dissolved oxygen that declined to zero within 2 weeks and corresponded with a reduction of methane. A positive shift in methane's stable carbon isotope content from −62.6‰ ± 0.6‰ before the storm to −44.0‰ ± 2.4‰ after the storm indicates microbial methane oxidation was a mechanism for the loss of groundwater methane. Post-storm methane concentrations did not recover to pre-storm levels during the observation period, suggesting tropical cyclones have long-lasting (months) effects on the carbon cycle. Compared to seasonal effects, mixing and oxygen inputs during storm-induced hydrologic forcing have an outsized biogeochemical influence within stratified coastal aquifers.
Urban forests have largely been overlooked for the role they play in reducing stormwater runoff volume by using hydrologic processes such as interception (rainfall intercepted by tree canopy), evapotranspiration (the transfer of water from vegetation into the atmosphere) and infiltration (percolation of rainwater into the Earth’s soil). Early research into the effects of trees on urban stormwater runoff used simple estimates based on assumptions of canopy coverage and design storm criteria. In a review of available literature on how capable urban trees are at reducing runoff, the Center for Watershed Protection (2017) found only six studies; three of them used measured data from a single plot, and the other three used models. When identifying gaps in research on the role of trees in stormwater management, Kuehler and others (2017) highlighted the need for studies that scale the local effects of urban trees to the larger sewershed catchment area, allowing a more holistic understanding of the urban tree canopy effects on hydrology.
For these reasons, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, U.S. Forest Service, and the University of Wisconsin, quantified the effect of removing urban street trees and their canopy on stormwater generation in a medium-density residential area. Using a paired-catchment experimental design, rainfall-runoff relations were characterized in two medium-density residential catchments in Fond du Lac, Wisconsin, during May through September in 2018–20. Results of the study are detailed in Selbig and others (2022).
During the calibration phase, hydrograph metrics from paired runoff events were used to develop the relation between the control and test catchments with street trees in place. The ability to measure changes to the rainfall-runoff response after removal of tree canopy was made possible by an aggressive tree removal program by the city as a response to rapid infestation from the Agrilus planipennis (emerald ash borer). In March 2020, a total of 31 street trees were removed at the onset of the treatment period, resulting in a loss of 2,990 square meters of canopy over streets, driveways, sidewalks, and grassed areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223074","usgsCitation":"Selbig, W.R., Loheide, S.P., II, Shuster, W., Scharenbroch, B.C., Coville, R.C., Kruegler, J., Avery, W., Haefner, R., and Nowak, D., 2022, Loss of street tree canopy increases stormwater runoff: U.S. Geological Survey Fact Sheet 2022–3074, 4 p., https://doi.org/10.3133/fs20223074.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141242","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407081,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.XML"},{"id":407082,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3074/images"},{"id":407157,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":407079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3074/coverthb.jpg"},{"id":407080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.pdf","text":"Report","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3074"},{"id":501510,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113526.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.494873046875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.72148995228582\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained primarily from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of water year 2021, the network consisted of 120 wells and piezometers at 54 locations. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels at the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2021 (October 1, 2020, through September 30, 2021). Water-level data that were collected in previous water years from wells that were later discontinued were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1162","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Bell, M.T., and Montero, N.Y., 2022, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021: U.S. Geological Survey Data Report 1162, 43 p., https://doi.org/10.3133/dr1162.","productDescription":"Report: iv, 43 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-138357","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":406961,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1162/coverthb.jpg"},{"id":501270,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113524.htm","linkFileType":{"id":5,"text":"html"}},{"id":407061,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1162/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406967,"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"},{"id":406966,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1162/images"},{"id":406965,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1162/dr1162.XML"},{"id":406963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1162/dr1162.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1162"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin and adjacent areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.2540283203125,\n 33.95247360616282\n ],\n [\n -106.248779296875,\n 33.95247360616282\n ],\n [\n -106.248779296875,\n 35.51434313431818\n ],\n [\n -107.2540283203125,\n 35.51434313431818\n ],\n [\n -107.2540283203125,\n 33.95247360616282\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Managers charged with recovering endangered species in regulated river segments often have limited flexibility to alter flow regimes and want estimates of the expected population benefits associated with both flow and nonflow management actions. Disentangling impacts on different life stages from concurrently applied actions is essential for determining the effectiveness of each action, but difficult without models that integrate multiple information sources. Here, we develop and fit an integrated population model for endangered Rio Grande Silvery Minnow (Hybognathus amarus) in the Middle Rio Grande, New Mexico. We integrate catch per unit effort monitoring data collected during 2002–2018 with population estimates, data collected during rescue of minnow from drying pools, habitat availability estimates, laboratory results, releases of hatchery reared minnow, and expert opinion. We use expert elicitation to develop a larval carrying capacity index as an informed proxy for the complex interactions among flow, habitat, and life history in this species. We evaluate the model using out-of-sample forecasts of 2019 and 2020, develop an algorithm to identify supplemental water releases that maximize benefits to the minnow, and quantify the effectiveness of various actions. Experts generally agreed on the duration and timing of flow requirements and disagreed regarding the importance of different magnitudes. The integrated model with the larval carrying capacity index outperformed two alternative models in forecasting catch in 2019 and 2020. The model estimates that minnow abundance varied by more than three orders of magnitude between 2002 and 2018 and that in a few years recruitment was limited by spawner abundance. Evaluation of the expected benefits of flow and nonflow management actions to fall population abundance across different years suggests that efficient addition of water to the base hydrograph is the most effective action in most, but not all years. Many actions are effective only under certain hydrologic and population conditions and the effectiveness of different actions varies in different sections of the study area. Widespread water extraction and river regulation combined with periodic drought and ongoing climate change may necessitate creative management of federally listed fish species in arid systems informed by thorough analyses of management effectiveness.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4240","usgsCitation":"Yackulic, C., Archdeacon, T.P., Valdez, R.A., Hobbs, M., Porter, M., Lusk, J., Tanner, A.M., Gonzales, E., Lee, D.Y., and Haggerty, G.M., 2022, Quantifying flow and nonflow management impacts on an endangered fish by integrating data, research, and expert opinion: Ecosphere, v. 13, no. 9, e4240, 22 p., https://doi.org/10.1002/ecs2.4240.","productDescription":"e4240, 22 p.","ipdsId":"IP-138221","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4240","text":"Publisher Index Page"},{"id":407407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853029,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archdeacon, Thomas P","contributorId":296980,"corporation":false,"usgs":false,"family":"Archdeacon","given":"Thomas","email":"","middleInitial":"P","affiliations":[{"id":64264,"text":"U.S. Fish & Wildlife Service, New Mexico Fish & Wildlife Conservation Office, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853030,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valdez, Richard A.","contributorId":204243,"corporation":false,"usgs":false,"family":"Valdez","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":34515,"text":"SWCA Environmental Consultants","active":true,"usgs":false}],"preferred":false,"id":853031,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hobbs, Monika","contributorId":296981,"corporation":false,"usgs":false,"family":"Hobbs","given":"Monika","email":"","affiliations":[{"id":64265,"text":"Albuquerque Bernalillo County Water Utility Authority, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853032,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Michael D.","contributorId":139912,"corporation":false,"usgs":false,"family":"Porter","given":"Michael D.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":853033,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lusk, Joel","contributorId":296982,"corporation":false,"usgs":false,"family":"Lusk","given":"Joel","email":"","affiliations":[{"id":64266,"text":"US Bureau of Reclamation, Environment and Lands Division, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":853034,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tanner, Ashley M.","contributorId":264589,"corporation":false,"usgs":false,"family":"Tanner","given":"Ashley","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":853035,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzales, Eric J","contributorId":296983,"corporation":false,"usgs":false,"family":"Gonzales","given":"Eric J","affiliations":[{"id":64267,"text":"U.S. Bureau of Reclamation, Albuquerque Area Office, Environment & Lands Division, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853036,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lee, Debbie Y","contributorId":296984,"corporation":false,"usgs":false,"family":"Lee","given":"Debbie","email":"","middleInitial":"Y","affiliations":[{"id":64268,"text":"Western EcoSystems Technology, Inc., Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853037,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haggerty, Grace M","contributorId":296985,"corporation":false,"usgs":false,"family":"Haggerty","given":"Grace","email":"","middleInitial":"M","affiliations":[{"id":64269,"text":"New Mexico Interstate Stream Commission, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853038,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70239387,"text":"70239387 - 2022 - Hydrologic connectivity and residence time affect the sediment trapping efficiency and dissolved oxygen concentrations of the Atchafalaya River Basin","interactions":[],"lastModifiedDate":"2023-01-11T16:09:55.321068","indexId":"70239387","displayToPublicDate":"2022-09-09T10:03:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic connectivity and residence time affect the sediment trapping efficiency and dissolved oxygen concentrations of the Atchafalaya River Basin","docAbstract":"Little is known about water movement, volume, or residence time (RT), and how those characteristics affect sediment trapping efficiency (TE) and dissolved oxygen concentrations (DO) in the United States' largest remaining bottomland hardwood swamp, the Atchafalaya River Basin. To better understand these dynamics, this study used bathymetry, lidar, and stage records to determine volumes in the Basin's hydrologically distinct water management units (WMUs). Discharge measurements determined flow distribution and RT. Residence time was compared with DO to identify conditions that coincided with DO increases or decreases. Suspended sediment concentrations (SSC) were used to determine TE relative to calculated and measured discharge and RT. Discharge through units (85–2,200 m3/s) and RT (0.37–231 d) depended on connectivity and river stage. At high stages, with water temperatures >20°C, DO in the largest WMU declined by −0.21 mg/l/day. DO trends indicated less well-connected areas of the WMU contributed hypoxic waters as the flood wave lengthened and stages fell. In the two WMUs examined for TE, TE (−266% to 99% and up to 38 Gg/day) correlated with hydrologic connectivity, SSC, RT, water volume, and, in one WMU, discharge losses. Long RT and high TE indicated a high potential to process nutrients. These relationships varied among WMUs. Large volumes of sediment-laden water moving over the floodplain combined with long RT, high TE, and hypoxia indicate that this ecosystem has continental-scale importance in reducing nutrient loads to the northern Gulf of Mexico. Reports from other systems suggest similar processes may be operating on other large river floodplains globally.
","language":"English","publisher":"Wiley","doi":"10.1029/2021WR030731","usgsCitation":"Kroes, D., Day, R., Kaller, M.D., Demas, C.R., Kelso, W.E., Pasco, T., Harlan, R., and Roberts, S., 2022, Hydrologic connectivity and residence time affect the sediment trapping efficiency and dissolved oxygen concentrations of the Atchafalaya River Basin: Water Resources Research, v. 58, no. 11, e2021WR030731, 25 p., https://doi.org/10.1029/2021WR030731.","productDescription":"e2021WR030731, 25 p.","ipdsId":"IP-122676","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":446481,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr030731","text":"Publisher Index Page"},{"id":411722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Atchafalaya River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.19842494954662,\n 29.43299698721681\n ],\n [\n -91.0065616177603,\n 29.745824547354005\n ],\n [\n -91.69407188999388,\n 30.994196767826082\n ],\n [\n -91.98986119316385,\n 30.994196767826082\n ],\n [\n -91.91791244374411,\n 30.39269246892897\n ],\n [\n -91.62212314057413,\n 29.849884487088616\n ],\n [\n -91.4622370307522,\n 29.540858164204536\n ],\n [\n -91.19842494954662,\n 29.43299698721681\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroes, Daniel 0000-0001-9104-9077 dkroes@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-9077","contributorId":3830,"corporation":false,"usgs":true,"family":"Kroes","given":"Daniel","email":"dkroes@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861386,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":221895,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861387,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kaller, Michael D. 0000-0002-1239-7725","orcid":"https://orcid.org/0000-0002-1239-7725","contributorId":300764,"corporation":false,"usgs":false,"family":"Kaller","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":861388,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Demas, Charles R.","contributorId":300765,"corporation":false,"usgs":false,"family":"Demas","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":12545,"text":"USGS retired","active":true,"usgs":false}],"preferred":false,"id":861389,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelso, William E.","contributorId":300766,"corporation":false,"usgs":false,"family":"Kelso","given":"William","email":"","middleInitial":"E.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":861390,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pasco, Tiffany","contributorId":300767,"corporation":false,"usgs":false,"family":"Pasco","given":"Tiffany","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":861391,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harlan, Raynie","contributorId":300768,"corporation":false,"usgs":false,"family":"Harlan","given":"Raynie","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":861392,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roberts, Steven","contributorId":300769,"corporation":false,"usgs":false,"family":"Roberts","given":"Steven","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":861393,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238489,"text":"70238489 - 2022 - Landscape genetics of a sub-alpine toad: Climate change predicted to induce upward range shifts via asymmetrical migration corridors","interactions":[],"lastModifiedDate":"2022-11-28T12:58:23.041365","indexId":"70238489","displayToPublicDate":"2022-09-08T06:50:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1890,"text":"Heredity","active":true,"publicationSubtype":{"id":10}},"title":"Landscape genetics of a sub-alpine toad: Climate change predicted to induce upward range shifts via asymmetrical migration corridors","docAbstract":"Climate change is expected to have a major hydrological impact on the core breeding habitat and migration corridors of many amphibians in the twenty-first century. The Yosemite toad (Anaxyrus canorus) is a species of meadow-specializing amphibian endemic to the high-elevation Sierra Nevada Mountains of California. Despite living entirely on federal lands, it has recently faced severe extirpations, yet our understanding of climatic influences on population connectivity is limited. In this study, we used a previously published double-digest RADseq dataset along with numerous remotely sensed habitat features in a landscape genetics framework to answer two primary questions in Yosemite National Park: (1) Which fine-scale climate, topographic, soil, and vegetation features most facilitate meadow connectivity? (2) How is climate change predicted to influence both the magnitude and net asymmetry of genetic migration? We developed an approach for simultaneously modeling multiple toad migration paths, akin to circuit theory, except raw environmental features can be separately considered. Our workflow identified the most likely migration corridors between meadows and used the unique cubist machine learning approach to fit and forecast environmental models of connectivity. We identified the permuted modeling importance of numerous snowpack-related features, such as runoff and groundwater recharge. Our results highlight the importance of considering phylogeographic structure, and asymmetrical migration in landscape genetics. We predict an upward elevational shift for this already high-elevation species, as measured by the net vector of anticipated genetic movement, and a north-eastward shift in species distribution via the network of genetic migration corridors across the park.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41437-022-00561-x","usgsCitation":"Maier, P., Vandergast, A.G., Ostoja, S.M., Aguilar, A., and Bohonak, A.J., 2022, Landscape genetics of a sub-alpine toad: Climate change predicted to induce upward range shifts via asymmetrical migration corridors: Heredity, v. 129, p. 257-272, https://doi.org/10.1038/s41437-022-00561-x.","productDescription":"16 p.","startPage":"257","endPage":"272","ipdsId":"IP-144739","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446498,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9613655","text":"External Repository"},{"id":409669,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.4668810085783,\n 38.44363766038242\n ],\n [\n -120.4668810085783,\n 36.93160395898977\n ],\n [\n -118.12778648528116,\n 36.93160395898977\n ],\n [\n -118.12778648528116,\n 38.44363766038242\n ],\n [\n -120.4668810085783,\n 38.44363766038242\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","noUsgsAuthors":false,"publicationDate":"2022-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Maier, Paul A. 0000-0003-0851-8827","orcid":"https://orcid.org/0000-0003-0851-8827","contributorId":221033,"corporation":false,"usgs":false,"family":"Maier","given":"Paul A.","affiliations":[{"id":40313,"text":"Department of Biology, San Diego State","active":true,"usgs":false}],"preferred":false,"id":857617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostoja, Steven M sostoja@usgs.gov","contributorId":192955,"corporation":false,"usgs":false,"family":"Ostoja","given":"Steven","email":"sostoja@usgs.gov","middleInitial":"M","affiliations":[],"preferred":false,"id":857619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aguilar, Andres","contributorId":195155,"corporation":false,"usgs":false,"family":"Aguilar","given":"Andres","email":"","affiliations":[],"preferred":false,"id":857620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bohonak, Andrew J.","contributorId":195156,"corporation":false,"usgs":false,"family":"Bohonak","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":857621,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238578,"text":"70238578 - 2022 - Advancing geophysical techniques to image a stratigraphic hydrothermal resource","interactions":[],"lastModifiedDate":"2022-11-30T17:25:35.358802","indexId":"70238578","displayToPublicDate":"2022-09-01T11:18:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Advancing geophysical techniques to image a stratigraphic hydrothermal resource","docAbstract":"Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi-disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this and analogous resources. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data, in conjunction with new and preceding geoscientific measurements and observations, are being integrated and evaluated in this study for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings (i.e., whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs) will also be evaluated. This paper provides details and current updates on the course of this study in-progress.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Schwering, P., Winn, C., Jaysaval, P., Knox, H., Siler, D.L., Hardwick, C., Ayling, B., Faulds, J., Mlawsky, E., McConville, E., Norbeck, J., Hinz, N., Matson, G., and Queen, J., 2022, Advancing geophysical techniques to image a stratigraphic hydrothermal resource: Geothermal Resources Council Transactions, v. 46, p. 976-991.","productDescription":"16 p.","startPage":"976","endPage":"991","ipdsId":"IP-141659","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":409862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409843,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034650","linkFileType":{"id":5,"text":"html"}}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schwering, Paul","contributorId":299507,"corporation":false,"usgs":false,"family":"Schwering","given":"Paul","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":857964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winn, Carmen","contributorId":299508,"corporation":false,"usgs":false,"family":"Winn","given":"Carmen","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":857965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaysaval, Piyoosh","contributorId":299509,"corporation":false,"usgs":false,"family":"Jaysaval","given":"Piyoosh","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":857966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knox, Hunter","contributorId":299510,"corporation":false,"usgs":false,"family":"Knox","given":"Hunter","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":857967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":857968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hardwick, Christian","contributorId":299511,"corporation":false,"usgs":false,"family":"Hardwick","given":"Christian","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":857969,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayling, Bridget","contributorId":299512,"corporation":false,"usgs":false,"family":"Ayling","given":"Bridget","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857970,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Faulds, James","contributorId":299513,"corporation":false,"usgs":false,"family":"Faulds","given":"James","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857971,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mlawsky, Elijah","contributorId":299515,"corporation":false,"usgs":false,"family":"Mlawsky","given":"Elijah","email":"","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; Nevada Bureau of Mines and Geology; University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":857972,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McConville, Emma","contributorId":299518,"corporation":false,"usgs":false,"family":"McConville","given":"Emma","email":"","affiliations":[{"id":51825,"text":"Fervo Energy","active":true,"usgs":false}],"preferred":false,"id":857973,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Norbeck, Jack","contributorId":299519,"corporation":false,"usgs":false,"family":"Norbeck","given":"Jack","affiliations":[{"id":51825,"text":"Fervo Energy","active":true,"usgs":false}],"preferred":false,"id":857974,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hinz, Nicholas","contributorId":299524,"corporation":false,"usgs":false,"family":"Hinz","given":"Nicholas","affiliations":[{"id":64866,"text":"Geologica Geothermal Group, Inc","active":true,"usgs":false}],"preferred":false,"id":857975,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Matson, Gabe","contributorId":299527,"corporation":false,"usgs":false,"family":"Matson","given":"Gabe","email":"","affiliations":[{"id":64866,"text":"Geologica Geothermal Group, Inc","active":true,"usgs":false}],"preferred":false,"id":857976,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Queen, John","contributorId":299529,"corporation":false,"usgs":false,"family":"Queen","given":"John","affiliations":[{"id":47634,"text":"Hi-Q Geophysical, Inc.","active":true,"usgs":false}],"preferred":false,"id":857977,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70236341,"text":"70236341 - 2022 - Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range","interactions":[],"lastModifiedDate":"2022-09-02T14:17:32.925032","indexId":"70236341","displayToPublicDate":"2022-09-01T09:13:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12565,"text":"GeoHazards","active":true,"publicationSubtype":{"id":10}},"title":"Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range","docAbstract":"Although accurate root cohesion model estimates are essential to quantify the effect of vegetation roots on shallow slope stability, few means exist to independently validate such model outputs. One validation approach for cohesion estimates is back-calculation of apparent root cohesion at a landslide site with well-documented failure conditions. The catchment named CB1, near Coos Bay, Oregon, USA, which experienced a shallow landslide in 1996, is a prime locality for cohesion model validation, as an abundance of data and observations from the site generated broad insights related to hillslope hydrology and slope stability. However, previously published root cohesion values at CB1 used the Wu and Waldron model (WWM), which assumes simultaneous root failure and therefore likely overestimates root cohesion. Reassessing published cohesion estimates from this site is warranted, as more recently developed models include the fiber bundle model (FBM), which simulates progressive failure with load redistribution, and the root bundle model-Weibull (RBMw), which accounts for differential strain loading. We applied the WWM, FBM, and RBMw at CB1 using post-failure root data from five vegetation species. At CB1, the FBM and RBMw predict values that are less than 30% of the WWM-estimated values. All three models show that root cohesion has substantial spatial heterogeneity. Most parts of the landslide scarp have little root cohesion, with areas of high cohesion concentrated near plant roots. These findings underscore the importance of using physically realistic models and considering lateral and vertical spatial heterogeneity of root cohesion in shallow landslide initiation and provide a necessary step towards independently assessing root cohesion model validity.
","language":"English","publisher":"MDPI","doi":"10.3390/geohazards3030022","usgsCitation":"Cronkite-Ratcliff, C., Schmidt, K.M., and Wirion, C., 2022, Comparing root cohesion estimates from three models at a shallow landslide in the Oregon Coast Range: GeoHazards, v. 3, no. 3, p. 428-451, https://doi.org/10.3390/geohazards3030022.","productDescription":"24 p.","startPage":"428","endPage":"451","ipdsId":"IP-133079","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geohazards3030022","text":"Publisher Index Page"},{"id":406136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","city":"Coos Bay","otherGeospatial":"Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.56298828125001,\n 43.14909399920127\n ],\n [\n -123.50830078125,\n 43.14909399920127\n ],\n [\n -123.50830078125,\n 43.75522505306928\n ],\n [\n -124.56298828125001,\n 43.75522505306928\n ],\n [\n -124.56298828125001,\n 43.14909399920127\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Kevin M. 0000-0003-2365-8035 kschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-2365-8035","contributorId":1985,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kevin","email":"kschmidt@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wirion, Charlotte 0000-0003-0721-3036","orcid":"https://orcid.org/0000-0003-0721-3036","contributorId":296101,"corporation":false,"usgs":false,"family":"Wirion","given":"Charlotte","email":"","affiliations":[{"id":63984,"text":"ETH Zurich, Switzerland (now at WEO, Luxembourg)","active":true,"usgs":false}],"preferred":false,"id":850666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236602,"text":"70236602 - 2022 - What did they just say? Building a Rosetta stone for geoscience and machine learning","interactions":[],"lastModifiedDate":"2022-09-14T13:16:23.671752","indexId":"70236602","displayToPublicDate":"2022-08-31T09:17:57","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"What did they just say? Building a Rosetta stone for geoscience and machine learning","docAbstract":"Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(s) to represent very specific, precise, but, perhaps, different concepts. Under the best circumstances, misunderstanding key terms will lead toward a breakdown of efficiency. Under less optimal conditions, miscommunication will sow frustration, lead to errors, and inhibit scientific breakthroughs. Here, our research group of geoscientists and machine learning experts presents a process to help geoscientists understand the fundamentals of supervised learning by describing the general workflow (i.e., a conceptual pipeline) for supervised learning that must be understood by all the parties involved in a geoscience-machine learning endeavor. Terms critical for machine learning are introduced, defined, and used within the context of an overly simplified mock hydrological study to illustrate their appropriate usage, and then used again in the context of a published geothermal-machine learning study. These key terms are divided into two groups, which are 1) essential to the field of machine learning but are predominantly absent in geoscience or 2) homonyms (i.e., words with the same spelling or pronunciation but with different meanings) between the fields. Lastly, we discuss a few other important homonyms that were not introduced in the general workflow but arise regularly in machine learning applications","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the earth to save the earth","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 Geothermal Rising Conference","conferenceDate":"Aug 28-31, 2022","conferenceLocation":"Reno, NV","language":"English","publisher":"Geothermal Rising","usgsCitation":"Mordensky, S.P., Lipor, J., Burns, E., and Lindsey, C.R., 2022, What did they just say? Building a Rosetta stone for geoscience and machine learning, in Using the earth to save the earth, v. 46, Reno, NV, Aug 28-31, 2022, p. 1347-1374.","productDescription":"28 p.","startPage":"1347","endPage":"1374","ipdsId":"IP-140223","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":406592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":406573,"type":{"id":15,"text":"Index Page"},"url":"https://grc2022.mygeoenergynow.org/"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":851489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851491,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236950,"text":"70236950 - 2022 - Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","interactions":[],"lastModifiedDate":"2024-05-16T15:46:58.587708","indexId":"70236950","displayToPublicDate":"2022-08-31T06:58:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12603,"text":"Journal of the American Society of Agricultural and Biological Engineers","active":true,"publicationSubtype":{"id":10}},"title":"Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","docAbstract":"The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org","doi":"10.13031/ja.14964","usgsCitation":"Douglas-Mankin, K., Rumsey, C., Sexstone, G., Ivahnenko, T.I., Houston, N., Chavarria, S., Senay, G.B., Foster, L.K., Thomas, J., Flickinger, A.K., Galanter, A.E., Moeser, C.D., Welborn, T.L., Pedraza, D.E., Lambert, P., and Johnson, M.S., 2022, Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis: Journal of the American Society of Agricultural and Biological Engineers, v. 65, no. 4, p. 881-901, https://doi.org/10.13031/ja.14964.","productDescription":"21 p.","startPage":"881","endPage":"901","ipdsId":"IP-133533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science 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The Great Lakes provide a source of drinking water to tens of millions of people in Canada and the United States and support one of the most diverse ecosystems in the world. Increasing urbanization combined with aging infrastructure and more extreme storm events because of changing weather patterns creates stormwater management challenges for communities across the Great Lakes region. A variety of green infrastructure (GI) practices, designed to decrease runoff and improve water quality, have been implemented throughout the region in response to these challenges; however, implementation often remains limited to local efforts and with little coordination among various levels of government because of, at least in part, a lack of clear standards for stormwater, limited funding, and a general uncertainty in the type and expected performance of these practices. City planners, engineers, and political leaders often see GI investment as riskier than other alternatives despite studies that determined, in most cases, practices can either reduce or not affect costs.
This report summarizes selected published reports and data sources from studies done in Great Lakes states and compares the measured effects of various GI practices and their applicability in different settings around the Great Lakes. By summarizing selected published reports and data sources from studies done in Great Lakes states, this report provides foundational information for U.S. Geological Survey scientists and their local and national partners to assess the ability of GI to reduce stormwater runoff in Great Lakes urban areas. GI includes a variety of stormwater management techniques designed to mimic natural hydrologic processes like infiltration and evapotranspiration, which can decrease the volume of water running into sewers and streams. It can also improve water quality by trapping sediment, nutrients, and other contaminants. A variety of landscape practices can be incorporated into urban areas as GI, but the discussion here is limited to vegetated basins, vegetated channels, permeable pavement, urban tree canopy, and green roofs. Other types of GI, such as downspout disconnection, rainwater harvesting, and wet and dry detention basins were not included because hydrologic function and associated components are not widely monitored or evaluated in literature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1496","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Baker, N.T., Sullivan, D.J., Selbig, W.R., Haefner, R.J., Lampe, D.C., Bayless, R., and McHale, M.R., 2022, Green infrastructure in the Great Lakes—Assessment of performance, barriers, and unintended consequences: U.S. Geological Survey Circular 1496, 70 p., https://doi.org/10.3133/cir1496.","productDescription":"Report: ix, 70 p.; 1 Table","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-128488","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
High-latitude peatlands are changing rapidly in response to climate change, including permafrost thaw. Here, we reconstruct hydrological conditions since the seventeenth century using testate amoeba data from 103 high-latitude peat archives. We show that 54% of the peatlands have been drying and 32% have been wetting over this period, illustrating the complex ecohydrological dynamics of high latitude peatlands and their highly uncertain responses to a warming climate.