Regional groundwater recharge commonly is estimated using a threshold-type water-budget approach in which groundwater recharge is assumed to occur when water in the plant-root zone exceeds the soil’s moisture storage capacity. A water budget of the plant-soil system accounts for water inputs (rainfall, fog interception, irrigation, septic-system leachate, and other inputs), water outputs (runoff, evaporation, transpiration, and recharge), and changes in stored water during a specified time interval. Water budgets can be computed on any desired interval, including annual, monthly, daily, and subdaily intervals. In general, uncertainty in recharge estimates is expected to be lower using daily or subdaily intervals relative to monthly and annual intervals. Average recharge rates computed over a period of a year or multiple years are commonly determined from water budgets computed using a daily computation interval capable of capturing rainfall and land-cover changes during the period.
This report documents the Water-budget Accounting for Tropical Regions Model, or WATRMod, code that can be used to estimate spatially variable, daily water-budget components in tropical-island and other appropriate settings. The purpose of this report is to provide descriptions of WATRMod’s (1) approach to computing a daily water budget, (2) represented processes, (3) limitations, and (4) execution procedure, input requirements, output files, and example files. The model computes a daily water budget for each hydrologically independent subarea within the overall study area. A subarea is defined by its climatic, soil, land-cover, and human-related (for example, adding irrigation or other water) characteristics. The water-budget model can represent processes including rainfall, fog interception, irrigation, septic-system leachate, direct recharge that bypasses the plant-soil system, runoff, canopy evaporation in forested areas, evapotranspiration, and groundwater recharge. The water-budget model can represent either one of the following different accounting orders: (1) accounting for loss of water by evapotranspiration before accounting for recharge, and (2) accounting for recharge before accounting for evapotranspiration. WATRMod’s limitations include: (1) uncharacterized, subdaily transient changes in water inputs and outputs from the plant-soil system, (2) unrepresented precipitation in the form of snow and sublimation, and (3) routing runoff from one subarea to an adjacent subarea that is not directly represented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221013","usgsCitation":"Oki, D.S., 2022, Water-budget accounting for tropical regions model (WATRMod) documentation: U.S. Geological Survey Open-File Report 2022-1013, 77 p., https://doi.org/10.3133/ofr20221013.","productDescription":"Report: viii, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","ipdsId":"IP-126805","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501758,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113077.htm","linkFileType":{"id":5,"text":"html"}},{"id":401381,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPAY41","text":"WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files","description":"Oki, D.S., 2022, WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files: U.S. Geological Survey data release, https://doi.org/10.5066/P9VPAY41."},{"id":401379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1013/covrthb.jpg"},{"id":401380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1013/ofr20221013.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1013"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.3848876953125,\n 20.555652403773365\n ],\n [\n -156.0003662109375,\n 20.6379249854131\n ],\n [\n -155.9454345703125,\n 20.776659051878816\n ],\n [\n -156.26678466796875,\n 20.964004409178308\n ],\n [\n -156.47003173828125,\n 20.925527866647226\n ],\n [\n -156.610107421875,\n 21.056307701901847\n ],\n [\n -156.72271728515625,\n 20.94604992010052\n ],\n [\n -156.67327880859375,\n 20.822875478868443\n ],\n [\n -156.55792236328122,\n 20.761250430919652\n ],\n [\n -156.48651123046875,\n 20.771523019513364\n ],\n [\n -156.4617919921875,\n 20.622502259344817\n ],\n [\n -156.3848876953125,\n 20.555652403773365\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Soil carbon (C) in permafrost peatlands is vulnerable to decomposition with thaw under a warming climate. The amount and form of C loss likely depends on the site hydrology following permafrost thaw, but antecedent conditions during peat accumulation are also likely important. We test the role of differing hydrologic conditions on rates of peat accumulation, permafrost formation, and response to warming at an Arctic tundra fen using a process-based model of peatland dynamics in wet and dry landscape settings that persist from peat initiation in the mid-Holocene through future simulations to 2100 CE and 2300 CE. Climate conditions for both the wet and dry landscape settings are driven by the same downscaled TraCE-21ka transient paleoclimate simulations and CCSM4 RCP8.5 climate drivers. The landscape setting controlled the rates of peat accumulation, permafrost formation and the response to climatic warming and permafrost thaw. The dry landscape scenario had high rates of initial peat accumulation (11.7 ± 3.4 mm decade−1) and rapid permafrost aggradation but similar total C stocks as the wet landscape scenario. The wet landscape scenario was more resilient to 21st century warming temperatures than the dry landscape scenario and showed 60% smaller C losses and 70% more new net peat C additions by 2100 CE. Differences in the modeled responses indicate the largest effect is related to the landscape setting and basin hydrology due to permafrost controls on decomposition, suggesting an important sensitivity to changing runoff patterns. These subtle hydrological effects will be difficult to capture at circumpolar scales but are important for the carbon balance of permafrost peatlands under future climate warming.
Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted tidal exchange in vast areas of coastal wetlands. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls and scaling of carbon exchange in these understudied ecosystems is critical for informing climate consequences of blue carbon restoration and/or management interventions. Here, we (1) examine how carbon fluxes vary across a salinity gradient (4–25 psu) in impounded and natural, tidally unrestricted Phragmites wetlands using static chambers and (2) probe drivers of carbon fluxes within an impounded coastal wetland using eddy covariance at the Herring River in Wellfleet, MA, United States. Freshening across the salinity gradient led to a 50-fold increase in CH4 emissions, but effects on carbon dioxide (CO2) were less pronounced with uptake generally enhanced in the fresher, impounded sites. The impounded wetland experienced little variation in water-table depth or salinity during the growing season and was a strong CO2 sink of −352 g CO2-C m−2 year−1 offset by CH4 emission of 11.4 g CH4-C m−2 year−1. Growing season CH4 flux was driven primarily by temperature. Methane flux exhibited a diurnal cycle with a night-time minimum that was not reflected in opaque chamber measurements. Therefore, we suggest accounting for the diurnal cycle of CH4 in Phragmites, for example by applying a scaling factor developed here of ~0.6 to mid-day chamber measurements. Taken together, these results suggest that although freshened, impounded wetlands can be strong carbon sinks, enhanced CH4 emission with freshening reduces net radiative balance. Restoration of tidal flow to impounded ecosystems could limit CH4 production and enhance their climate regulating benefits.
This report describes the findings of a U.S. Geological Survey study, completed in cooperation with the Bureau of Land Management, focused on better understanding the present-day (1975–2019) hydrogeology and groundwater quality of the San Agustin Basin in west-central New Mexico to support sustainable groundwater resource management. The basin hosts a relatively undeveloped basin-fill and alluvium aquifer system and is topographically divided into east and west subbasins by the McClure Hills. Groundwater chemistry and groundwater elevation data were compiled, collected, and interpreted in the context of groundwater flow and quality. The analyses presented in this report consider groundwater chemistry data collected within the last decade (2010–19) and groundwater elevation data collected from 1975 through 2019 to provide insight into present-day conditions. Groundwater elevations show that groundwater typically moves from the highlands to the lowlands, with a prominent east to west regional trend. Groundwater elevations were lowest in the southwestern portion of the west subbasin, where estimated flow directions suggest underflow through the local highlands into the northern East Fork Gila River watershed, which is further supported by historical groundwater elevation data from the northern East Fork Gila River watershed. Gradual groundwater elevation gradients (about 2 feet per mile) near the east and west subbasin divide suggest that groundwater slowly flows from the east subbasin to the west subbasin.
Quantitative analyses of groundwater chemistry data show that groundwater in both subbasins has similar chemical characteristics. A systematic east to west groundwater evolution in water chemistry was not observed despite evidenced subbasin connectivity. The absence of this pattern suggests that groundwater mixing is regionally prevalent, sediment reactivity is low and variable, and (or) recharge conditions are comparable in both subbasins. Groundwater chemistry was generally independent of aquifer type, suggesting that the aquifers are hydrologically well connected. Corrected carbon-14 groundwater age estimates in the basin ranged from 232 to 13,916 years before present with a median of 5,409 years. A wide range of groundwater ages is therefore present in the basin, with waters commonly being thousands of years old, thereby supporting generally slow regional groundwater movement. A component of relatively young groundwater, for which estimated ages could not be accurately computed, is also present in the basin, and it may commonly mix with older waters. The spatial distribution of categorical and quantitative groundwater ages indicates that most recharge likely occurs in the highlands through mountain-block recharge and as focused recharge within arroyos, although evidence of modern (1953 and after) groundwater was minimal at sampled sites.
Median annual gradients (groundwater elevation change over time) indicate that most groundwater elevations in the lowlands changed little (−0.2 to 0.2 foot per year) from 1975 through 2019. Groundwater elevations in the highlands varied more annually, which is likely due to recharge from precipitation events. These more variable groundwater elevations in the highlands compared with the lowlands, along with groundwater ages, provide further evidence that most groundwater recharge takes place in the highlands, with minimal recharge in the lowlands. Median groundwater elevation change for all sites was −0.05 foot per year. Temporal consistency of lowland groundwater elevations suggests that regional groundwater dynamics have been more or less stable through time under current climate and development conditions, although median annual gradients indicate that groundwater elevations may have slightly declined on average between 1975 and 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225029","collaboration":"Prepared in cooperation with Bureau of Land Management and in collaboration with New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Pepin, J.D., Travis, R.E., Blake, J.M., Rinehart, A., and Koning, D., 2022, Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019: U.S. Geological Survey Scientific Investigations Report 2022–5029, 61 p., 4 app., https://doi.org/10.3133/sir20225029.","productDescription":"Report: x, 61 p.; 6 Tables; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120066","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":502386,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113080.htm","linkFileType":{"id":5,"text":"html"}},{"id":401145,"rank":11,"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":401143,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.csv","text":"Table 3.1","size":"29.5 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 3.1"},{"id":401142,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.xlsx","text":"Table 3.1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 3.1"},{"id":401141,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.csv","text":"Table 2.1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 2.1"},{"id":401140,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.xlsx","text":"Table 2.1","size":"27.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 2.1"},{"id":401138,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.xlsx","text":"Table 1.1","size":"116 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 1.1"},{"id":401137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5029/images"},{"id":401134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5029/coverthb.jpg"},{"id":401135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.pdf","text":"Report","size":"8.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5029"},{"id":401139,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.csv","text":"Table 1.1","size":"146 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 1.1"},{"id":401136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.XML"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Agustin Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 34.5\n ],\n [\n -107.333,\n 34.5\n ],\n [\n -107.333,\n 33.333\n ],\n [\n -108.666,\n 33.333\n ],\n [\n -108.666,\n 34.5\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
Groundwater discharge is vitally important for maintaining or restoring valuable ecosystems in surface water and at the underlying groundwater-surface-water ecotone. Detecting and quantifying groundwater discharge is challenging because rates of flow can be very small and difficult to measure, exchange is commonly highly heterogeneous both in space and time, and surface-water hydrodynamics can influence the exchange and hinder measurements. Fortunately, a growing number of methods developed during the last several decades has led to advancements in our capabilities to identify and quantify groundwater discharge to surface water, including better use of seepage meters, application of tracers such as heat or isotopes, and improved groundwater-modeling capabilities. This progress has led to coalescence in characterizing the complex mix of hydrological, biological, and chemical processes that occur at the groundwater-surface water interface, along with relevant societal effects. Still, many uncertainties and assumptions show an incomplete knowledge of these processes, including the lack of studies in many regions of the world, insufficient sharing of practical methodologies between scientific disciplines, incomplete understanding of processes and parameters specific to the sediment-water interface, and challenges associated with measuring exchange at multiple scales of time and space.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/w14111698","usgsCitation":"Duque, C., and Rosenberry, D., 2022, Advances in the study and understanding of groundwater discharge to surface water: Water, v. 14, no. 11, 1698, 5 p., https://doi.org/10.3390/w14111698.","productDescription":"1698, 5 p.","ipdsId":"IP-141343","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14111698","text":"Publisher Index Page"},{"id":401295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Duque, Carlos 0000-0001-5833-8483","orcid":"https://orcid.org/0000-0001-5833-8483","contributorId":245349,"corporation":false,"usgs":false,"family":"Duque","given":"Carlos","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":843722,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":257638,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":843723,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231799,"text":"sir20225021 - 2022 - Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2026-04-09T16:53:55.498502","indexId":"sir20225021","displayToPublicDate":"2022-05-26T12:05:53","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5021","displayTitle":"Status and Understanding of Groundwater Quality in the Sacramento Metropolitan Domestic-Supply Aquifer Study Unit, 2017: California GAMA Priority Basin Project","title":"Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit (SacMetro-DSA) was studied from August to November 2017 as part of the second phase of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in parts of Amador, Placer, Sacramento, and Sutter Counties, and the extent of the study unit was defined by the location of three California Department of Water Resources groundwater subbasins: the North American, the South American, and the Cosumnes. The SacMetro-DSA focused on groundwater resources used for domestic drinking-water supply, which generally correspond to shallower parts of aquifer systems than those of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.
This study included two components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources. The first component of this study—the status assessment—was based on water-quality data collected from 49 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2017. The samples were analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that had concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, groundwater age, and geochemical and hydrologic conditions of the domestic-supply aquifer and related these data to constituents identified in the status assessment for further evaluation.
In the SacMetro-DSA study unit, arsenic was the only inorganic constituent detected above health-based benchmarks and was detected in 10 percent of the domestic-supply aquifer system. Inorganic constituents were detected above the non-health-based California State Water Resources Control Board—Division of Drinking Water secondary maximum contaminant levels (SMCL-CA) in 16 percent of the system. The inorganic constituents detected above the SMCL-CA were chloride, iron, manganese, and total dissolved solids (TDS). Organic constituents (volatile organic compounds and pesticides) with health-based benchmarks were not detected above health-based benchmarks; however, chloroform was detected at concentrations higher than 10 percent of the health-based benchmark (80 micrograms per liter) in 2 percent of the domestic-supply aquifer system. Of the 310 organic constituents analyzed, 16 constituents were detected; however, only bentazon and chloroform had detection frequencies greater than 10 percent.
Inorganic constituents with health-based benchmarks that were evaluated in the understanding assessment included arsenic and hexavalent chromium. Arsenic and hexavalent chromium are natural constituents of aquifer sediments in the study unit and did not appear to be influenced by anthropogenic processes; rather, the presence of arsenic and hexavalent chromium appeared to be related to geochemical conditions controlled by oxidation–reduction reactions in the aquifer system. Naturally occurring inorganic constituents with SMCL-CAs evaluated in the understanding assessment were the trace elements iron and manganese, the major ion chloride, and TDS. Like arsenic and hexavalent chromium, the presence of iron and manganese was most strongly related to geochemical conditions in the aquifer system, specifically reducing conditions, which were most common near the western edge of the study unit close to the Sacramento River. Concentrations of chloride and TDS are indicators of salinity and were correlated with variables related to well location and included redox, agricultural land use, and elevation. Chloride and TDS were positively correlated to reducing conditions, and agricultural land use was negatively correlated to elevation and well depth. Observed correlations among variables were likely driven by the characteristics of the western part of the study unit, such as its higher proportion of agricultural land use and its relatively low elevation. A large portion of the western edge of the study unit is located in the center of the Sacramento Valley, defined by the location of the Sacramento River. The special-interest constituent perchlorate, also included in the understanding assessment, has natural and anthropogenic sources. Perchlorate was detected frequently and at moderate relative concentrations. In some areas of the study unit, concentrations of perchlorate were higher than what might be expected in nature; therefore, anthropogenic introduction of perchlorate or anthropogenically induced migration of native perchlorate could be occurring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225021","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Bennett, G.L., V, 2022, Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2022–5021, 52 p., https://doi.org/10.3133/sir20225021.","productDescription":"Report: xi, 52 p.; Data Release","numberOfPages":"52","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-125530","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401167,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H4P0XF","text":"Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","description":"Bennett, G.L., V, 2022, Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey data release, available at https://doi.org/10.5066/P9H4P0XF."},{"id":401166,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5021/images"},{"id":401165,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.xml"},{"id":401164,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2022–5021"},{"id":401163,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5021/covrthb.jpg"},{"id":502376,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113075.htm","linkFileType":{"id":5,"text":"html"}},{"id":401191,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2022–5021"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Metropolitan Domestic-Supply Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 37.87485339352928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
The Everglades in south Florida supply fresh drinking water for more than 7 million people, host a National Park, and are classified as a Ramsar wetland of international distinction. Predicting trajectories of water flow and water storage changes in the future is important to managing the Congressionally authorized restoration of the Everglades. Here we describe the needed data sources and analysis approaches to build the inputs for biophysically based modeling that can protect water and ecological resources in the face of changing water management and climate conditions. A biophysical approach to modeling overland flow in the Everglades can help predict future outcomes for ecological habitat, water storage during droughts, and water conveyance during floods. The needed data include measurements of vegetation stem architecture, microtopography, and landscape pattern metrics. Stem architecture measurements present the opportunity to estimate flow roughness of distinct vegetation communities based on hydraulic principles. At a larger scale, the microtopography and the connectivity of the sloughs between ridges offer a way to quantify the effects of flow blockage and tortuous flow paths on overland flow. Combined with theory these data provide the capacity to simulate overland flow in both the historical, pre-drainage Everglades as well as in the present-day managed Everglades. Also provided are the hydrologic data, e.g., water slopes, water depths and overland flow velocities, that can be used to verify a biophysical model. Ultimately, the purpose is to anticipate how changing flow and water depth will interact with evolving vegetation and landscape conditions to influence future water availability for society and for the ecosystem, both in the Everglades and in other low-gradient floodplains.
","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511451.1","usgsCitation":"Harvey, J., and Choi, J., 2022, Biophysical methods and data analysis for simulating overland flow in the Everglades: ESSOAr, 51 p., https://doi.org/10.1002/essoar.10511451.1.","productDescription":"51 p.","ipdsId":"IP-140509","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447677,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/essoar.10511451.1","text":"External Repository"},{"id":435841,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DQYB1O","text":"USGS data release","linkHelpText":"Biophysical Data for Simulating Overland Flow in the Everglades"},{"id":408968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.07811851388726,\n 26.46536235501027\n ],\n [\n -82.07811851388726,\n 24.821342005916392\n ],\n [\n -79.90282554513692,\n 24.821342005916392\n ],\n [\n -79.90282554513692,\n 26.46536235501027\n ],\n [\n -82.07811851388726,\n 26.46536235501027\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856292,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237245,"text":"70237245 - 2022 - Hydrological cycle and water budgets","interactions":[],"lastModifiedDate":"2022-10-05T14:33:45.429244","indexId":"70237245","displayToPublicDate":"2022-05-23T09:28:40","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hydrological cycle and water budgets","docAbstract":"In this chapter, we describe the hydrological cycle and each of its components (pools). The hydrological cycle is important to the transport and cycling of nutrients and energy. Quantifying the various components of the hydrological cycle, referred to as constructing water budget for a defined area, is an important framework for wise and equitable water management. The hydrological cycle has changed as the result of human activity affecting specific components of the water budget and the movement of water between the components. Water budgets are provided for two defined areas: the earth as a whole and the watershed of a small inland lake.
Given a specific area with well-defined boundaries, constructing a water budget consists of quantifying the amount and relationships among inflow, outflow, and change in storage within a defined area of the hydrological cycle, water budgets relevant to inland waters and aquatic ecosystems, and how the hydrological cycle and water budgets have been affected by anthropogenic modifications.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of inland waters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00008-6","usgsCitation":"Robertson, D., Perlman, H.A., and Narisimhan, T.N., 2022, Hydrological cycle and water budgets, chap. of Encyclopedia of inland waters, p. 19-27, https://doi.org/10.1016/B978-0-12-819166-8.00008-6.","productDescription":"9 p.","startPage":"19","endPage":"27","ipdsId":"IP-121572","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Second Edition","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853822,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perlman, Howard A. 0000-0002-2392-0737","orcid":"https://orcid.org/0000-0002-2392-0737","contributorId":297327,"corporation":false,"usgs":true,"family":"Perlman","given":"Howard","email":"","middleInitial":"A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Narisimhan, T. N.","contributorId":297329,"corporation":false,"usgs":false,"family":"Narisimhan","given":"T.","email":"","middleInitial":"N.","affiliations":[{"id":33770,"text":"University of California at Berkeley","active":true,"usgs":false}],"preferred":false,"id":853824,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238776,"text":"70238776 - 2022 - Worldwide wetland loss and conservation of biodiversity and ecosystem services","interactions":[],"lastModifiedDate":"2022-12-12T15:15:10.893711","indexId":"70238776","displayToPublicDate":"2022-05-23T09:12:32","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Worldwide wetland loss and conservation of biodiversity and ecosystem services","docAbstract":"Aim: Best strategies for future conservation and management to address global and regional trends in wetland loss and degradation are assessed in this article.
Main concepts covered: Direct drivers of wetland loss and change include land drainage and filling, hydrologic alteration, degradation from pollutants and sediments, and conversion to agriculture, urban and industrial usage. Estimates of global wetland loss are as high as 87% since 1700 CE. All regions of the world have lost wetland area. The designation of wetland protected area reduces disturbance by humans and supports the conservation of biodiversity and habitat. Protected areas have been designated by local, state, or federal entities, NGOs (e.g., Nature Conservancy), and the Ramsar Convention on Wetlands. Protected wetlands have great value for human society. For example, wetlands such as peatland and swamp store carbon that would otherwise be released as greenhouse gases to the atmosphere. A case study of the Keoladeo National Park, Rajasthan, India underscores the importance of maintaining water supply to maintain aquatic vegetation in protected wetlands.
Conclusion/outlook: Given the combined stresses of land-use and climate change to wetland protected areas, management of altered wetlands may improve their function. Beneficial management actions can include freshwater remediation of hydrologically-altered floodplains, improved wetland reserve design, assisted migration, and the softening of burning/cutting during drought. A better knowledge of potential of management actions to remediate land-use change will be helpful in addressing protected area management to promote conservation in the future.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of inland waters","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-819166-8.00058-X","usgsCitation":"Middleton, B., 2022, Worldwide wetland loss and conservation of biodiversity and ecosystem services, chap. of Encyclopedia of inland waters, v. 3, p. 288-294, https://doi.org/10.1016/B978-0-12-819166-8.00058-X.","productDescription":"7 p.","startPage":"288","endPage":"294","ipdsId":"IP-118283","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":410283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Middleton, Beth 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":222689,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858560,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238744,"text":"70238744 - 2022 - Wetlands under global change","interactions":[],"lastModifiedDate":"2022-12-07T13:13:47.960346","indexId":"70238744","displayToPublicDate":"2022-05-23T07:12:50","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Wetlands under global change","docAbstract":"Wetlands are among the ecosystem types most threatened by global change, including both climate change and other anthropogenic factors such as sea level rise, urban development, deforestation, agricultural land use, drainage, levees, tidal flow restrictions, pollution, eutrophication, and fires. Wetlands not only store disproportionate amounts of carbon compared to other terrestrial ecosystems, but they lie at the terrestrial-aquatic interface crucial to understanding landscape and global scale biogeochemical cycles. In this chapter, we focus on the major global change factors affecting wetlands and the responses of different wetland types to those global change factors. Special attention is given to direct responses to increasing atmospheric carbon dioxide levels. Because of their hydrological connections and placement at the terrestrial-aquatic interface, the conservation of wetlands involves accounting for uncertainties related to interacting stressors. While the past decades have seen many important experimental and observational studies of wetland responses to global change factors, large uncertainties remain, especially within tropical regions where even the basic extent of wetland ecosystems is not well documented.
Coastal wetlands are defined herein as inundated, vegetated ecosystems with hydrology, and biogeochemistry influenced by sea levels, at timescales of tides to millennia. Coastal wetlands are necessary components of global greenhouse gas estimation and scenario modeling, both for continental and oceanic mass balances. The carbon pools and fluxes on coastal lands, especially those influenced by tidal drivers and sea level rise, are distinct in their magnitude, rates, and uncertainties. We describe herein the pathways taken for a US scale estimation of blue carbon based on annual timesteps and bottom-up modeling, as appropriate for the first effort to include coastal wetlands in the Intergovernmental Panel on Climate Change (IPCC) guidelines for a National Greenhouse Gas Inventory (NGGI). As such, we summarize multiple efforts to reconcile mapping, modeling, and measurement issues and we report the assumptions we made based on data availability. Provided as requested feedback to the IPCC.
Subsidiary Body for Scientific and Technological Advice (SBSTA) evaluation of guidance criteria, these analyses synergistically point scientists, practitioners, and policy makers toward the greatest uncertainties to address in future assessments: coastal wetland methane emissions and carbon dioxide emissions associated with the fate of eroded soil. This is a story of what was learned in the 2014–2018 NASA Carbon Monitoring System project (https://carbon.nasa.gov/cgi-bin/cms_projects.pl), how it informs “good practice” (IPCC 2006) in reporting coastal wetland emissions and removals, and where it points scientifically toward data needs at different temporal and spatial scales.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","publisherLocation":"Balancing greenhouse gas budgets: Accounting for natural and anthropogenic flows of CO2 and other trace gases","doi":"10.1016/B978-0-12-814952-2.00001-0","usgsCitation":"Windham-Myers, L., Holmquist, J., Kroeger, K.D., and Troxler, T., 2022, Greenhouse gas balances in coastal ecosystems: Current challenges in “blue carbon” estimation and significance to national greenhouse gas inventories, p. 403-425, https://doi.org/10.1016/B978-0-12-814952-2.00001-0.","productDescription":"23 p.","startPage":"403","endPage":"425","ipdsId":"IP-123602","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":406543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holmquist, James R.","contributorId":272628,"corporation":false,"usgs":false,"family":"Holmquist","given":"James R.","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":832253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":832254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Troxler, Tiffany G.","contributorId":272629,"corporation":false,"usgs":false,"family":"Troxler","given":"Tiffany G.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":832255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234249,"text":"70234249 - 2022 - Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models","interactions":[],"lastModifiedDate":"2022-08-05T13:52:04.318024","indexId":"70234249","displayToPublicDate":"2022-05-17T08:46:59","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models","docAbstract":"Biogeochemical processes are often spatially discrete (hot spots) and temporally isolated (hot moments) due to variability in controlling factors like hydrologic fluxes, lithological characteristics, bio-geomorphic features, and external forcing. Although these hot spots and hot moments (HSHMs) account for a high percentage of carbon, nitrogen and nutrient cycling within the Critical Zone, the ability to identify and incorporate them into reactive transport models remains a significant challenge. This chapter provides an overview of the hot spots hot moments (HSHMs) concepts, where past work has largely focused on carbon and nitrogen dynamics within riverine systems. This work is summarized in the context of process-based and data-driven modeling approaches, including a brief description of recent research that casts a wider net to incorporate Hg, Fe and other Critical Zone elements, and focuses on interdisciplinary approaches and concepts. The broader goal of this chapter is to provide an overview of the gaps in our current understanding of HSHMs, and the opportunities therein, while specifically focusing on the underlying parameters and processes leading to their prognostic and diagnostic representation in reactive transport models.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Biogeochemistry of the Critical Zone","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Nature","doi":"10.1007/978-3-030-95921-0_2","usgsCitation":"Arora, B., Briggs, M., Zarnetske, J.P., Stegen, J., Gomez-Velez, J., and Dwivedi, D., 2022, Hot spots and hot moments in the Critical Zone: Identification of and incorporation into reactive transport models, chap. of Biogeochemistry of the Critical Zone, p. 9-47, https://doi.org/10.1007/978-3-030-95921-0_2.","productDescription":"39 p.","startPage":"9","endPage":"47","ipdsId":"IP-114081","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":404874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Arora, Bhavna 0000-0001-7841-886X","orcid":"https://orcid.org/0000-0001-7841-886X","contributorId":290532,"corporation":false,"usgs":false,"family":"Arora","given":"Bhavna","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":848330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222756,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":848331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":848332,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stegen, James","contributorId":242792,"corporation":false,"usgs":false,"family":"Stegen","given":"James","affiliations":[{"id":48525,"text":"Earth and Biological Sciences Division, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":848333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomez-Velez, Jesus","contributorId":219087,"corporation":false,"usgs":false,"family":"Gomez-Velez","given":"Jesus","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":848334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dwivedi, D.","contributorId":294554,"corporation":false,"usgs":false,"family":"Dwivedi","given":"D.","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":848335,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70231682,"text":"70231682 - 2022 - Decadal trends of mercury cycling and bioaccumulation within Everglades National Park","interactions":[],"lastModifiedDate":"2022-06-01T15:37:49.966824","indexId":"70231682","displayToPublicDate":"2022-05-17T06:45:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Decadal trends of mercury cycling and bioaccumulation within Everglades National Park","docAbstract":"Mercury (Hg) contamination has been a persistent concern in the Florida Everglades for over three decades due to elevated atmospheric deposition and the system's propensity for methylation and rapid bioaccumulation. Given declines in atmospheric Hg concentrations in the conterminous United States and efforts to mitigate nutrient release to the greater Everglades ecosystem, it was vital to assess how Hg dynamics responded on temporal and spatial scales. This study used a multimedia approach (water and biota) to examine Hg and methylmercury (MeHg) dynamics across a 76-site network within the southernmost portion of the region, Everglades National Park (ENP), from 2008 to 2018. Atmospheric Hg deposition was evaluated over time using a long-term monitoring station. Hg concentrations across matrices showed that air, water, and biota from the system were inextricably linked. Temporal patterns across matrices were driven primarily by hydrologic and climatic changes in the park and no evidence of a decline in atmospheric Hg deposition from 2008 to 2018 was observed, unlike other regions of the United States. In the Shark River Slough (SRS), excess dissolved organic carbon and sulfate were also consistently delivered from upgradient canals and showed no evidence of decline over the study period. Within the SRS a strong positive correlation was observed between MeHg concentrations in surface water and resident fish. Within distinct geographic regions of ENP (SRS, Marsh, Coastal), the geochemical controls on MeHg dynamics differed and highlighted regions susceptible to higher MeHg bioaccumulation, particularly in the SRS and Coastal regions. This study demonstrates the strong influence that dissolved organic carbon and sulfate loads have on spatial and temporal distributions of MeHg across the ENP. Importantly, improved water quality and flow rates are two key restoration targets of the nearly 30-year Everglades restoration program, which if achieved, this study suggests would lead to reduced MeHg production and exposure.
Frequent and prolonged military training maneuvers are an intensive type of land use that may disturb land cover, compact soils, and have lasting effects on adjacent stream hydrology and ecosystems. To better understand the potential effect of military training on hydrologic and environmental processes, the U.S. Geological Survey in cooperation with the U.S. Army established hydrologic and water-quality data-collection networks at the U.S. Army Garrison Fort Carson (AGFC) in 1978 and at the Piñon Canyon Maneuver Site (PCMS) in 1982. The purpose of this report is to present precipitation, streamflow, suspended-sediment, and water-quality data collected by the U.S. Geological Survey at the AGFC and PCMS for water years (WYs) 2016–18 and to evaluate those data in relation to long-term data from the AGFC and PCMS. In WYs 2016–18, the U.S. Geological Survey monitored 26 sites on the AGFC and 17 sites on the PCMS for precipitation amount, streamflow, suspended sediment, and (or) water quality.
On the AGFC, total annual precipitation in WYs 2016–18 was larger than the long-term mean for all 3 years at Rod and Gun Meteorologic Station at Fort Carson, CO (Rod and Gun). There were statistically significant upward trends in annual precipitation at Rod and Gun and Young Hollow Meteorologic Station at Fort Carson, CO (Young Hollow) with slopes of 1.25 and 0.66 inches per year (in/yr), respectively. The precipitation totals for WY 2017 were either the largest on record or in the top three for both sites and at Sullivan Park Meteorologic Station at Fort Carson, CO. On the PCMS, total annual precipitation was larger than the long-term mean in WYs 2016–18 at Brown Sheep Camp Meteorologic Station near Tyrone, CO; CIG Pipeline South Meteorologic Station near Simpson, CO; Bear Springs Hills Meteorologic Station near Houghton, CO (Bear Springs); and Upper Red Rock Canyon Meteorologic Station near Houghton, CO (Upper Red Rock). There were statistically significant upward trends in precipitation at Bear Springs and Upper Red Rock with slopes of 0.16 and 0.19 in/yr, respectively. The precipitation totals for WY 2017 were the largest on record for all sites except for Upper Bent Canyon Meteorological Station near Delhi, CO.
Streamflow was calculated at 18 sites on the AGFC and 7 sites on the PCMS in at least 1 of WYs 2016–18. At AGFC, mean annual (or seasonal) streamflow in WYs 2016–18 was less than the long-term mean at 7 sites and greater than the long-term mean at 3 sites. There were statistically significant downward trends in mean annual or seasonal streamflow at Womack Ditch from Little Fountain Creek near Fort Carson, CO, and Ripley Ditch from Little Fountain Creek at Fort Carson, CO, with slopes of −0.036 and −0.028 cubic feet per second per year (ft3/s/y), respectively; and a significant upward trend in streamflow at Turkey Creek West Seepage below Teller Reservoir near Stone City, CO, with a slope of less than 0.001 ft3/s/y. Unlike for precipitation, the mean annual or seasonal streamflow for WY 2017 was not in the top 3 for any of the 12 sites with measured flow.
At the PCMS, mean annual (or seasonal) streamflow was less than the long-term mean streamflow in WYs 2016–18 at the Taylor Arroyo below Rock Crossing near Thatcher, CO, and Bent Canyon Creek at Mouth near Timpas, CO, sites; and in WYs 2016 and 2018 at the Purgatoire River near Thatcher, CO (Purgatoire Thatcher), and Purgatoire River at Rock Crossing near Timpas, CO (Purgatoire Rock Crossing). There were no statistically significant trends in mean annual (or seasonal) streamflow at sites on the PCMS, and unlike for precipitation, the mean streamflow for WY 2017 was not in the top three for any sites except Purgatoire Rock Crossing. In WYs 2016–18, streamflow from sites on the AGFC and PCMS represented only a small fraction of streamflow in Fountain Creek or the Purgatoire River, and changes in streamflow that resulted from military maneuvers on the AGFC and PCMS were not likely to be detected in the downstream receiving waters.
Suspended-sediment concentrations, loads, and yields for WYs 2016–18, were analyzed at two sites on the AGFC and five sites on the PCMS. On the AGFC, mean seasonal suspended-sediment concentrations ranged from 3.10 to 155 milligrams per liter (mg/L), mean seasonal suspended-sediment loads ranged from 0.04 to 27.1 tons per day (t/d), and seasonal suspended-sediment yields ranged from 0.28 to 216 tons per season per square mile (t/s/mi2). Suspended-sediment yields at the two AGFC sites in WYs 2016–18 were all less than the long-term means. On the PCMS, mean seasonal suspended-sediment concentrations (at sites with some streamflow during a WY) ranged from 1.12 to 41.8 mg/L, mean suspended-sediment loads ranged from 0.01 to 13.1 t/d, and seasonal suspended-sediment yields ranged from 0.06 to 57.4 t/s/mi2. Suspended-sediment yields at the five PCMS sites in WYs 2016–18 were all less than the long-term means. In WYs 2016–18, mean daily suspended-sediment loads at Little Fountain were 1.3, 2.5, and 7.6 percent, respectively, of the mean daily suspended-sediment load at Fountain Creek at Security, Colorado. Likewise, the total of mean daily suspended-sediment loads from the five tributary sites to the Purgatoire River in WYs 2016–18 were about 0.25, 0.17, and 3.2 percent, respectively, of the historical mean daily suspended-sediment load at Purgatoire Thatcher.
Spearman’s rank correlation coefficient was used to evaluate the strength and form of the relations between daily total precipitation and daily mean streamflow and between daily mean streamflow and suspended-sediment concentration and load for WYs 2016–18. For the sites on the AGFC and PCMS, there were weak or statistically insignificant positive correlations between precipitation and streamflow at nearby streamgauges, but strong statistically significant positive correlations between streamflow and suspended-sediment concentration and load. The ephemeral nature of the streams, quantity and timing of precipitation, air temperature, seasonal soil-moisture deficits, and effective runoff detention in erosion-control ponds could all contribute to inconsistent conversion of precipitation to streamflow.
Water-quality data were analyzed for as many as 43 parameters from 9 samples collected from 3 sites on the AGFC and from 37 samples collected from 4 sites on PCMS during WYs 2016–18. The concentrations of selected water-quality parameters were compared to regulatory standards for aquatic life from the Colorado Department of Public Health and Environment (CDPHE) or aquatic-life criteria from the U.S. Environmental Protection Agency (EPA). There is at least 1 CDPHE standard or EPA criterion for 30 of the 43 water-quality parameters.
For all samples from both the AGFC and the PCMS in WYs 2016–18, the concentrations of most water-quality parameters were compliant with the associated standards or criteria. However, there were some exceedances of standards or criteria: 11 samples exceeded the CDPHE recreational class standard for Escherichia coli concentration, 9 samples exceeded the CDPHE chronic unfiltered phosphorus aquatic-life standard, 36 samples exceeded the CDPHE chronic sulfate aquatic-life standard, 5 samples exceeded the EPA criterion for selenium, 7 samples exceeded the EPA criterion for aluminum, 2 samples exceeded the CDPHE chronic standard for iron, and 15 samples exceeded the CDPHE chronic standard for manganese.
Identifying potential effects of military training on water quality in adjacent streams on the AGFC and PCMS is difficult due to the ephemeral nature of streamflow, limited number of sampling locations and samples, and limited access to the study areas. At the PCMS, pairs of water-quality samples were collected in March and May 2017 before and after an April–May 2017 military training event. At the Purgatoire Rock Crossing site, streamflow at the time of the May sample was approximately 35 times larger than streamflow for the March sample. The absolute percent differences of concentrations for 27 parameters ranged from −71.7 to 183 percent, and 7 parameters had increases in concentration whereas 22 parameters had no change or decreases in concentrations. The absolute percent differences of loads for 24 parameters ranged from 141 to 198 percent. The generally lower concentrations and higher loads were expected given the higher streamflows at the time of collection of the May compared to the March samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225018","collaboration":"Prepared in cooperation with the U.S. Department of the Army","usgsCitation":"Battaglin, W.A., and Kisfalusi, Z.D., 2022, Characterization of and relations among precipitation, streamflow, suspended-sediment, and water-quality data at the U.S. Army Garrison Fort Carson and Piñon Canyon Maneuver Site, Colorado, water years 2016–18: U.S. Geological Survey Scientific Investigations Report 2022–5018, 94 p., https://doi.org/10.3133/sir20225018.","productDescription":"Report: ix, 94 p.; Database; Data Release","onlineOnly":"Y","ipdsId":"IP-115539","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":400086,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NGJPGY","text":"USGS data release","linkHelpText":"Precipitation, suspended-sediment, and water-quality data collected 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U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
Climate change can act to facilitate or inhibit invasions of non-native species. Here, we address the influence of climate change on control of non-native common carp (hereafter, carp), a species recognized as one of the “world's worst” invaders across the globe. Control of this species is exceedingly difficult, as it exhibits rapid population growth and compensatory density dependence. In many locations where carp have invaded, however, climate change is altering hydrologic regimes and may influence population demography and efficacy of human control efforts. To further evaluate these processes, we employed a modified version of an age-based population model (CarpMOD), to investigate how hydrologic variability (change in lake area) influences carp population dynamics and control efforts in Malheur Lake, southeastern Oregon, USA. We explored how changes in lake area influence carp populations under three control scenarios: (1) no carp removal, (2) carp removal during low water years, and (3) carp removal during all years. Lake area fluctuations strongly influenced carp populations and the efficacy of carp control. Modeled carp biomass peaked when the lake transitioned from high-to-low levels, and carp biomass declined when lake area transitioned from low-to-high. Removing carp during low water periods—when fish were concentrated into a smaller area—reduced carp populations almost as much as removing carp every year. Furthermore, the effectiveness of control efforts increased with the prevalence and severity of low lake conditions (longer durations of very low lake area). These simulations suggest that a drier climate may naturally decrease carp populations and make them easier to control. However, drier conditions may also negatively affect aquatic ecosystems and potentially have a greater impact than non-native species themselves.
Streams in the Loup River Basin are sensitive to groundwater withdrawals because of the close hydrologic connection between groundwater and surface water. The U.S. Geological Survey, in cooperation with the Upper Loup and Lower Loup Natural Resources Districts, and the Nebraska Environmental Trust, studied the age and water-quality characteristics of groundwater near the South Loup River to assess the possible effects of a multiyear drought on streamflow.
Groundwater sampled in wells screened in Quaternary-age deposits displayed a wide range of mean ages (27 to 2,100 years), fraction modern, and susceptibility index values. Groundwater with higher concentrations of chloride and higher specific conductance was indicative of younger groundwater with a narrower age distribution and is more sensitive to climatic disturbances such as short-term drought conditions, based on the calculated susceptibility index. Groundwater samples from wells and springs in Pliocene-age deposits were categorized into two groups with different geochemical and age characteristics. One sample group of springs and wells, called the Western Pliocene, had higher concentrations of chloride and nitrate with young mean ages (18 to 77 years) and narrow age distributions. Groundwater in the Western Pliocene sample group is susceptible to short-term drought. In contrast, the other sample group from Pliocene-age deposits to the east (called Pliocene) had lower concentrations of nitrate, chloride, and mean groundwater ages ranging from 1,900 to 2,900 years old and is less likely to be affected by short-term drought conditions. Groundwater sampled from three wells screened in the Ogallala Formation was shown to have the oldest mean ages ranging from 8,700 to 23,000 years and the lowest calculated susceptibility index values observed in this study. Strong upward hydraulic gradients measured in wells indicated that groundwater from the Ogallala Formation is likely contributing to streamflow of the South Loup River.
Continuously measured gage height and specific conductance data indicated groundwater discharge from Quaternary-age deposits was highly responsive to precipitation events. In contrast, groundwater discharge from Pliocene-age deposits (Pliocene sample group) was far less responsive, indicating groundwater discharge from Pliocene-age deposits is likely more resilient to short-term drought conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225042","collaboration":"Prepared in cooperation with the Upper Loup and Lower Loup Natural Resources Districts and the Nebraska Environmental Trust","usgsCitation":"Hobza, C.M., and Solder, J.E., 2022, Age and water-quality characteristics of groundwater discharge to the South Loup River, Nebraska, 2019: U.S. Geological Survey Scientific Investigations Report 2022–5042, 57 p., https://doi.org/10.3133/sir20225042.","productDescription":"Report: ix, 57 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-129114","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":400241,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5042/sir20225042.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5042"},{"id":502397,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112991.htm","linkFileType":{"id":5,"text":"html"}},{"id":400244,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L6B4XE","text":"USGS data release","linkHelpText":"Lumped parameter models of groundwater age, South Loup River, Nebraska"},{"id":400243,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5042/images"},{"id":400242,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5042/sir20225042.XML"},{"id":400333,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225042/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400240,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5042/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"South Loup River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.8599853515625,\n 41.075210270566636\n ],\n [\n -98.5089111328125,\n 41.075210270566636\n ],\n [\n -98.5089111328125,\n 42.07376224008719\n ],\n [\n -100.8599853515625,\n 42.07376224008719\n ],\n [\n -100.8599853515625,\n 41.075210270566636\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
A study was conducted to compile and evaluate data used to identify groundwater sources that are under the direct influence of surface water (GUDI) in Pennsylvania. In the early 1990s, the Pennsylvania Department of Environmental Protection (PADEP) implemented the Surface Water Identification Protocol (SWIP) for the identification of GUDI sources. Since the establishment of the SWIP, PADEP has classified more than 500 individual sources across Pennsylvania as GUDI, but Pennsylvania’s complex geology and physiography provide a challenge for a uniform method of GUDI determination. Components used in this study to compile and evaluate data associated with GUDI determination include: (1) a preliminary review of file information for 43 public water-supply wells, (2) quality control and addition of data to PADEP’s database for public water-supply systems to prepare data for analysis, and (3) exploratory evaluation of existing GUDI sources in the database with respect to hydrogeologic and source-construction characteristics that are currently utilized in the assessment methodology.
Case files for 43 wells from PADEP’s Northcentral and Southcentral regions were reviewed to: (1) provide a better understanding of how the SWIP was applied in practice, (2) verify and compile missing data, and (3) find additional attributes not previously available that might explain a well’s categorization as GUDI. Review of file information showed that the SWIP outlined in PADEP technical guidance was usually followed, but for some sources, the GUDI determination was more complex and could not be easily summarized.
Data compiled for study analyses provided by PADEP include source data derived from public water-supply system case files, a source-information database for public water-supply systems, and Microscopic Particulate Analysis (MPA) results and associated water-quality data for public water-supply system groundwater sources. Data from the Pennsylvania Drinking Water Information System (PADWIS), which is PADEP’s database for public water-supply systems, were also used for this study. The PADWIS database originally included data for 12,147 groundwater sources (11,812 groundwater sources not under the direct influence of surface water (non-GUDI) wells and 335 GUDI wells). A subset (4,018 wells consisting of 3,842 non-GUDI wells and 175 GUDI wells) of the PADWIS database was created for an analysis and includes only community wells evaluated in accordance with the SWIP. MPA results for 631 community and noncommunity wells were compiled, along with associated water-quality data (alkalinity, chloride, Escherichia coli, fecal coliform, nitrate, pH, sodium, specific conductance, sulfate, total coliform, total dissolved solids, total residue, and turbidity) populated from the PADEP Bureau of Laboratories Sample Information System. Data compiled from sources other than PADEP include spatial data, both naturogenic (for example, average precipitation or distance to closest hydrologic feature) and anthropogenic (for example, percentage of developed or agricultural land cover within a specific vicinity of a public water-supply system well) data representing spatially derived variables.
Comparison among wells in the PADWIS dataset subset using the nonparametric Kruskal-Wallis test showed that GUDI wells had significantly older median construction years, shallower depths, and static water levels closer to the land surface than non-GUDI wells and that carbonate aquifers had the highest percentages of wells designated as GUDI (12 percent; 57 wells). Further comparison of wells in the PADWIS database subset using the Spearman’s rho monotonic correlation test illustrated that public water-supply wells designated as GUDI largely occur in unconfined aquifers and have high average yield and shallow static water levels. Assessment of the MPA database subset using the Kruskal-Wallis test showed wells with MPA total risk-factor scores that exceeded zero had older median construction years and shallower casing depths than wells with MPA total risk-factor scores of zero and that carbonate aquifers had the highest percentages of wells with MPA total risk-factor scores exceeding zero (30 percent; 63 wells). Spearman’s rho correlations showed that wells completed in aquifers with depths to major water-bearing zones closer to the land-surface had higher total risk-factor scores resulting from MPA samples.
Based on the results of the analyses described in this report, broad conclusions can be drawn regarding site-specific well characteristics as well as anthropogenic and naturogenic factors that could be responsible for a well being designated as GUDI, but the accuracy of these results is dependent on the quality of the data being analyzed. Ultimately, study results serve as an added resource for initial desktop screening of wells to determine if additional site-specific investigation is warranted and underscore the need for field evaluation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221023","collaboration":"Prepared in cooperation with the Pennsylvania Department of Environmental Protection, Bureau of Safe Drinking Water","usgsCitation":"Gross, E.L., Conlon, M.D., Risser, D.W., and Reisch, C.E., 2022, Compilation and evaluation of data used to identify groundwater sources under the direct influence of surface water in Pennsylvania (ver. 2.0, June 2023): U.S. Geological Survey Open-File Report 2022–1023, 41 p., https://doi.org/10.3133/ofr20221023.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101611","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":417972,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2022/1023/versionHist.txt","size":"49.8 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\"}}]}","edition":"Version 1.0: May 2022; Version 2.0: June 2023","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
A state-space model (SSM) of infiltration estimates daily groundwater recharge using time-series of groundwater-level altitude and meteorological inputs (liquid precipitation, snowmelt, and evapotranspiration). The model includes diffuse and preferential flow through the unsaturated zone, where preferential flow is a function of liquid precipitation and snowmelt rates and a threshold rate, above which there is direct recharge to the water table. Model parameters are estimated over seasonal periods and the SSM is coupled with the Kalman Filter (KF) to assimilate recent observations (hydraulic head) and meteorological inputs into recharge estimates. The approach can take advantage of real-time hydrologic and meteorological data to deliver real-time recharge estimates. The model is demonstrated on daily observations from two bedrock wells in carbonate aquifers of northwestern New York (USA) between 2013 and 2018. Meteorological inputs for liquid precipitation and snowmelt are compiled from SNODAS (2021). Results for recharge during winter and spring seasons show preferential flow events to the water table from liquid precipitation, snowmelt, or a combination of the two. Recharge estimates summed annually are consistent with previous estimates of recharge reported from groundwater flow and surface-process models. Results from the SSM and KF point to errors in meteorological inputs, such as the snowmelt rate, that are not compatible with hydraulic head observations. Whereas liquid and solid precipitation are measured at discrete stations and extrapolated to 1-km2 grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.
","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwat.13206","usgsCitation":"Shapiro, A.M., Day-Lewis, F., Kappel, W.M., and Williams, J., 2022, Incorporating snowmelt into daily estimates of recharge using a state-space model of infiltration: Groundwater, v. 60, no. 6, p. 721-746, https://doi.org/10.1111/gwat.13206.","productDescription":"26 p.","startPage":"721","endPage":"746","ipdsId":"IP-130903","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447877,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13206","text":"Publisher Index Page"},{"id":435854,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MRGR88","text":"USGS data release","linkHelpText":"Algorithms for model parameter estimation and state estimation applied to a state-space model for one-dimensional vertical infiltration incorporating snowmelt rate as a system input"},{"id":400497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":842636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":842637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kappel, William M. 0000-0002-2382-9757 wkappel@usgs.gov","orcid":"https://orcid.org/0000-0002-2382-9757","contributorId":1074,"corporation":false,"usgs":true,"family":"Kappel","given":"William","email":"wkappel@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John 0000-0002-6054-6908 jhwillia@usgs.gov","orcid":"https://orcid.org/0000-0002-6054-6908","contributorId":1553,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"jhwillia@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":842639,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231649,"text":"70231649 - 2022 - A forested wetland at a climate-induced tipping-point: 17-year demographic evidence of widespread tree recruitment failure","interactions":[],"lastModifiedDate":"2022-05-18T13:55:43.960202","indexId":"70231649","displayToPublicDate":"2022-05-06T08:46:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"A forested wetland at a climate-induced tipping-point: 17-year demographic evidence of widespread tree recruitment failure","docAbstract":"Regeneration and survival of forested wetlands are affected by environmental variables related to the hydrologic regime. Climate change, specifically alterations to precipitation patterns, may have outsized effects on these forests. In Tennessee, USA, precipitation has increased by 15% since 1960. The goal of our research was to assess the evidence for whether this change in precipitation patterns resulted in shorter growing seasons and recruitment failure in common canopy trees for a forest wetland. In 2001 and 2018, the density of Quercus lyrata (overcup oak), Liquidambar styraciflua (sweetgum), Quercus phellos (willow oak), and Betula nigra (river birch) seedling, sapling and adult density were mapped in an area of 2.3 ha within a seasonally flooded karst depression. Overall, the percentage of the growing season experiencing inundation was 26% greater in the deep than in shallow areas between 2001 and 2018. Saplings and small adults of all four species were restricted to shallow areas, and their abundance has declined substantially. Overcup oak and sweetgum individuals that were recruited into the adult life history stage were repelled from the deep zone. Overcup oak and sweetgum adults experienced lower mortality across the 2.3-ha study area (11% and 26%, respectively) relative to willow oak (56%) and river birch (64%) over the 17-year study. Growing-season inundation showed no relation to mortality in adult sweetgum and willow oak, a positive relation to mortality among adult river birch across size classes and among small adult overcup oak, and an inverse relation to mortality among large adult overcup oak. In shallow regions, overcup oak and sweetgum adults had greater basal area increment relative to the intermediate and deep regions of the pond. Results of hydrologic modeling for the study area, based on rainfall and temperature records covering 1855–2019, show ponding durations after 1970 considerably longer than the historical baseline, across ponding-depth classes. Our results strongly suggest that climate change is a driving factor suppressing tree regeneration since 1970 in this seasonally flooded karst depression.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2022.120247","usgsCitation":"Evans, J., McCarthy-Neumann, S., Pritchard, A., Cartwright, J.M., and Wolfe, W., 2022, A forested wetland at a climate-induced tipping-point: 17-year demographic evidence of widespread tree recruitment failure: Forest Ecology and Management, v. 517, 120247, 12 p., https://doi.org/10.1016/j.foreco.2022.120247.","productDescription":"120247, 12 p.","ipdsId":"IP-135244","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":447887,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2022.120247","text":"Publisher Index Page"},{"id":400755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Arnold Engineering Development Complex, Sinking Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.09521865844725,\n 35.38932985634939\n ],\n [\n -86.04337692260742,\n 35.38932985634939\n ],\n [\n -86.04337692260742,\n 35.42151066245934\n ],\n [\n -86.09521865844725,\n 35.42151066245934\n ],\n [\n -86.09521865844725,\n 35.38932985634939\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"517","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Jonathan","contributorId":291851,"corporation":false,"usgs":false,"family":"Evans","given":"Jonathan","affiliations":[{"id":62773,"text":"University of the South at Sewanee","active":true,"usgs":false}],"preferred":false,"id":843227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy-Neumann, Sarah","contributorId":291852,"corporation":false,"usgs":false,"family":"McCarthy-Neumann","given":"Sarah","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":843228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pritchard, Angus","contributorId":291853,"corporation":false,"usgs":false,"family":"Pritchard","given":"Angus","email":"","affiliations":[{"id":62773,"text":"University of the South at Sewanee","active":true,"usgs":false}],"preferred":false,"id":843229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843230,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wolfe, William J. 0000-0002-3292-051X","orcid":"https://orcid.org/0000-0002-3292-051X","contributorId":224729,"corporation":false,"usgs":false,"family":"Wolfe","given":"William J.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":843231,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231318,"text":"70231318 - 2022 - Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America","interactions":[],"lastModifiedDate":"2022-05-06T14:29:19.84111","indexId":"70231318","displayToPublicDate":"2022-05-05T09:26:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America","docAbstract":"The Younger Dryas (YD) climate event is the preeminent example of abrupt climate change in the recent geologic past. Climate conditions during the YD were spatially complex, and high-resolution sediment cores in the North Atlantic, western Europe, and East Asia have revealed it unfolded in two distinct stages, including an initial stable climatic period between ~ 12.9 and 12.2 ka associated with a weakened Atlantic Meridional Overturning Circulation (AMOC) and a second phase characterized by variable conditions until 11.7 ka as the AMOC recovered. Decades of investigations into the climate of western North America during the YD have failed to identify this stepped phenomenon. Here we present hydroclimate data from paleospring deposits in Death Valley National Park (California, USA) that demonstrate unequivocal evidence of two-stage partitioning within the YD event. High groundwater levels supported persistent and long-lived spring ecosystems between ~ 13.0 and 12.2 ka, which were immediately replaced by alternating wet and dry environments until ~ 11.8 ka. These results establish the mid-YD climate transition extended into western North America at approximately the same time it was recorded by hydrologic systems elsewhere in the Northern Hemisphere and show that even short-lived changes in the AMOC can have profound consequences for ecosystems worldwide.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-022-11377-4","usgsCitation":"Pigati, J.S., and Springer, K.B., 2022, Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America: Scientific Reports, v. 12, 7373, 7 p., https://doi.org/10.1038/s41598-022-11377-4.","productDescription":"7373, 7 p.","ipdsId":"IP-122733","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":447910,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-11377-4","text":"Publisher Index Page"},{"id":435857,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U5XSRY","text":"USGS data release","linkHelpText":"Data release for Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America"},{"id":400284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.070068359375,\n 36.217687122250574\n ],\n [\n -116.66931152343749,\n 36.217687122250574\n ],\n [\n -116.66931152343749,\n 37.4356124041315\n ],\n [\n -118.070068359375,\n 37.4356124041315\n ],\n [\n -118.070068359375,\n 36.217687122250574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":842301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":842302,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231182,"text":"tm4D3 - 2022 - U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data","interactions":[],"lastModifiedDate":"2022-05-05T13:50:19.759656","indexId":"tm4D3","displayToPublicDate":"2022-05-04T13:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"4-D3","displayTitle":"U.S. Geological Survey Hydrologic Toolbox — A Graphical and Mapping Interface for Analysis of Hydrologic Data","title":"U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data","docAbstract":"The Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey Groundwater and Surface-Water Toolboxes and provides several enhancements to these programs. The main analytical methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the Base-Flow Index (BFI), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the U.S. Geological Survey National Water Information System and outputs text reports that describe their analyses.
The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project. DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. Advantages of the DotSpatial system include its pure .NET implementation for both the user interface and the GIS mapping engine, and thus the DotSpatial system simplifies software deployment and installation. In addition to combining the functionality of the separate Groundwater and Surface-Water Toolboxes, the Hydrologic Toolbox also organizes the functionality by theme (Groundwater Tools, Surface-Water Tools, and general Time-Series Tools).
This report provides a description of how to build a Hydrologic Toolbox project and to download and manage hydrologic time-series data. It includes an overview of the analytical and statistical capabilities of the Hydrologic Toolbox and highlights the primary differences between the Hydrologic Toolbox and the Groundwater and Surface-Water Toolboxes. The report supplements information available in an extensive online Help manual and is intended to provide a set of instructions that will allow users to quickly develop skills to use the mapping, data-retrieval, and computational tools of the program.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Book 4, Hydrologic Analysis and Interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4D3","programNote":"Water Availability and Use Science Program","usgsCitation":"Barlow, P.M., McHugh, A.R., Kiang, J.E., Zhai, T., Hummel, P., Duda, P., and Hinz, S., 2022, U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data: U.S. Geological Survey Techniques and Methods, book 4, chap. D3, 23 p., https://doi.org/10.3133/tm4D3.","productDescription":"Report: vi, 23 p.; Software release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130481","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":400024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/d03/tm4d3.pdf","text":"Report","size":"4.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-D3"},{"id":400023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/d03/coverthb.jpg"},{"id":400027,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9DBLL43","text":"USGS software release","linkHelpText":"- U.S. Geological Survey Hydrologic Toolbox software archive"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abj2479","usgsCitation":"Uhlemann, S., Dafflon, B., Wainwright, H.M., Williams, K.H., Minsley, B.J., Zamudio, K.D., Carr, B., Falco, N., Ulrich, C., and Hubbard, S.S., 2022, Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics: Science Advances, v. 8, no. 12, 15 p., https://doi.org/10.1126/sciadv.abj2479.","productDescription":"15 p.","ipdsId":"IP-134172","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":447933,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abj2479","text":"External Repository"},{"id":400125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.02726364135741,\n 38.86671143315032\n ],\n [\n -106.9134521484375,\n 38.86671143315032\n ],\n [\n -106.9134521484375,\n 38.97595868249733\n ],\n [\n -107.02726364135741,\n 38.97595868249733\n ],\n [\n -107.02726364135741,\n 38.86671143315032\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Uhlemann, Sebastian","contributorId":291360,"corporation":false,"usgs":false,"family":"Uhlemann","given":"Sebastian","email":"","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":842137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dafflon, Baptiste","contributorId":245441,"corporation":false,"usgs":false,"family":"Dafflon","given":"Baptiste","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":842138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wainwright, Haruko Murakami","contributorId":291362,"corporation":false,"usgs":false,"family":"Wainwright","given":"Haruko","email":"","middleInitial":"Murakami","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":842139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Kenneth Hurst","contributorId":291364,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"Hurst","affiliations":[{"id":62696,"text":"LBNL, Rocky Mountain Biological Lab","active":true,"usgs":false}],"preferred":false,"id":842140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":842141,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zamudio, Katrina D. 0000-0003-0278-0154","orcid":"https://orcid.org/0000-0003-0278-0154","contributorId":203252,"corporation":false,"usgs":true,"family":"Zamudio","given":"Katrina","email":"","middleInitial":"D.","affiliations":[],"preferred":true,"id":842142,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carr, Bradley","contributorId":175482,"corporation":false,"usgs":false,"family":"Carr","given":"Bradley","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":842143,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Falco, Nicola","contributorId":245431,"corporation":false,"usgs":false,"family":"Falco","given":"Nicola","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":842144,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ulrich, Craig","contributorId":175248,"corporation":false,"usgs":false,"family":"Ulrich","given":"Craig","email":"","affiliations":[],"preferred":false,"id":842145,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hubbard, Susan S.","contributorId":175249,"corporation":false,"usgs":false,"family":"Hubbard","given":"Susan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":842146,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70230421,"text":"sir20215083 - 2022 - Areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York","interactions":[],"lastModifiedDate":"2026-04-03T14:12:25.021685","indexId":"sir20215083","displayToPublicDate":"2022-05-02T14:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5083","displayTitle":"Areas Contributing Recharge to Selected Production Wells in Unconfined and Confined Glacial Valley-Fill Aquifers in Chenango River Basin, New York","title":"Areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York","docAbstract":"In the Chenango River Basin of central New York, unconfined and confined glacial valley-fill aquifers are an important source of drinking-water supplies. The risk of contaminating water withdrawn by wells that tap these aquifers might be reduced if the areas contributing recharge to the wells are delineated and these areas protected from land uses that might affect the water quality. The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 to improve understanding of groundwater flow and delineate areas contributing recharge to 16 production wells clustered in three study areas in the basin as part of an effort to protect the source of water to these wells. Areas contributing recharge were delineated on the basis of numerical steady-state groundwater-flow models representing long-term average hydrologic conditions.
In the Cortland study area, four water suppliers operate 10 production wells that withdraw a total average rate of 2,480 gallons per minute from an unconfined aquifer consisting of well-sorted sand and gravel deposits. Simulated areas contributing recharge to these wells at their average pumping rates covered a total area of 6.93 square miles. Simulated areas contributing recharge extend upgradient from the wells to upland till deposits and to groundwater divides. Some simulated areas contributing recharge include isolated areas remote from the wells. Short simulated groundwater traveltimes from recharging locations to discharging wells indicated that the wells are vulnerable to contamination from land-surface activities; 50 percent of the traveltimes were 10 years or less. Land cover in some of the areas contributing recharge included a substantial amount of urban and agriculture land use.
The groundwater-flow model of the Cortland study area was calibrated to available hydrologic data by inverse modeling using nonlinear regression. The parameter variance-covariance matrix from model calibration was used to create parameter sets that reflect the uncertainty of the parameter estimates and the correlation among parameters to evaluate the uncertainty associated with the single, predicted contributing areas to the wells. This analysis led to contributing areas expressed as a probability distribution. Because of the effects of parameter uncertainty, the size of the probabilistic contributing areas was larger than the size of the single, predicted contributing area for the wells. Thus, some areas not in the single, predicted contributing area might actually be in the contributing area, including additional areas of urban and agriculture land use that have the potential to contaminate groundwater. Additional areas that might be in the contributing area included recharge originating near the pumping wells that have relatively short groundwater-flow paths and traveltimes.
In each of the Greene and Cincinnatus study areas, one water supplier operates three wells that are screened near the top of the bedrock surface in a confined aquifer consisting of poorly to well-sorted sand and gravel deposits. This confined aquifer is overlain by a lacustrine confining unit of very fine sand, silt, and clay, which in turn is overlain by a thin unconfined aquifer of sand and gravel. The groundwater-flow models for these two areas were manually calibrated because of the limited hydrologic data. Simulated areas contributing recharge to the Greene study area wells covered a total area of 0.35 square mile for the average pumping rate of 170 gallons per minute. The contributing areas extended southeastward of the wells to the groundwater divide in the till uplands. The contributing areas also included remote, isolated areas on the opposite side of the Chenango River from the wells primarily in the till uplands. For the Cincinnatus study area wells, which have a low average pumping rate (34 gallons per minute), the simulated contributing areas totaled 0.06 square mile and were on the same side of the river as the wells, but they are isolated areas remote from the wells primarily in the till-covered bedrock uplands. Land cover in these contributing areas for both study areas is primarily agriculture and forested, with the contributing areas to the Greene study area wells also including some urban land uses. Because the Greene and Cincinnatus study area wells are screened relatively deep and some flow paths to the wells partly travel through the confining unit, which impedes the connection with surface sources of recharge, overall groundwater traveltimes are greater than for wells in the Cortland study area. Fifty percent of Cortland study area wells, but only 9 and 44 percent of Greene and Cincinnatus study area wells, respectively, have groundwater traveltimes of 10 years or less.
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U.S. Geological Survey
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Northborough, MA 01532
Baseflow is the portion of streamflow that comes from groundwater and subsurface sources. Although baseflow is essential for sustaining streams during low flow and drought periods, we have little information about how and why it has changed over large regions of the continental United States. The objective of this study was to evaluate how changes in the climate system have affected observed monthly baseflow records at 3,283 USGS gauges over the last 30 years (1989–2019). We developed a statistical modeling framework to determine the relationship between monthly baseflow and monthly climate predictors (i.e., precipitation, temperature, and antecedent wetness). Overall, we found that baseflow trends and the factors influencing them vary by region and month. In the US Northeast, increases were detected earlier in the year (February and March) and in the summer (May and June), and were likely due to increasing precipitation, warmer temperature, and subsequent changes in snowmelt. Increasing baseflow in the US Pacific Northwest and Midwest were associated with increases in precipitation and antecedent wetness throughout the year. Decreasing trends were located in the US Southeast and Southwest. Baseflow trends in the US Southeast were only detected in March, possibly as a result of decreased precipitation during the spring. On the other hand, decreases in baseflow in the Central Southwestern United States occurred throughout the year. These trends were associated with a lack of precipitation and increases in temperature. Finally, we examined the relationship between monthly baseflow trends and changes in total water storage using monthly Gravity Recovery and Climate Experiment mascon products from the Jet Propulsion Laboratory. In this study, trends in total water storage were strongly associated with baseflow trends across the United States. The spatial and temporal variability in baseflow response to climate reported here can aid water managers in adapting to future climate change.