Oklahoma Groundwater Law (Oklahoma Statute § 82-1020.5) requires that the Oklahoma Water Resources Board conduct hydrologic investigations to determine the maximum annual yield for the State’s groundwater basins. The Boone and Roubidoux aquifers (also known as the Springfield Plateau aquifer and Ozark aquifer, respectively) are bedrock aquifers that extend from northeastern Oklahoma into Kansas, Arkansas, and Missouri. At present (2024), the Oklahoma Water Resources Board has yet to legally issue orders for the final determination of maximum annual yields for the Boone and Roubidoux aquifers. To support determination of a maximum annual yield, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, developed a hydrogeologic framework, a conceptual groundwater-flow model, and a calibrated numerical groundwater-flow model for the Boone and Roubidoux aquifers.
Three types of groundwater-availability scenarios were simulated by using the calibrated numerical model. These scenarios were used to (1) estimate equal-proportionate-share groundwater withdrawal rates (groundwater withdrawal applied equally over the aquifer), (2) quantify the potential effects of projected groundwater withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought. For the Boone aquifer, equal-proportionate-share groundwater withdrawal rates were 1.10, 0.98, and 0.96 acre-feet per acre per year for the 20-, 40-, and 50-year scenarios, respectively. For the Roubidoux aquifer, equal-proportionate-share groundwater withdrawal rates were 1.76, 1.34, and 1.25 acre-feet per acre per year for the 20-, 40-, and 50-year simulations, respectively. For the 50-year scenarios, stream seepage was minimally affected. Over the 10-year drought scenario, groundwater storage in the Boone and Roubidoux aquifers decreased by 660,451 acre-feet (6.7 percent) and 508,472 acre-feet (1.0 percent), respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245093","issn":"2328-0328","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Trevisan, A.R., Russell, C.A., Lockmiller, H.A., Wagner, D.L., Correll, J.S., and Knierim, K.J., 2024, Conceptualization and simulation of groundwater flow and groundwater availability in the Boone and Roubidoux aquifers in northeastern Oklahoma, 1980–2017: U.S. Geological Survey Scientific Investigations Report 2024–5093, 105 p., https://doi.org/10.3133/sir20245093.","productDescription":"Report: xiv, 105 p.; Data Release","numberOfPages":"124","onlineOnly":"Y","ipdsId":"IP-142594","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":462876,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245093/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5093 HTML"},{"id":462875,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5093/sir20245093.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5093 XML"},{"id":462874,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5093/sir20245093.pdf","size":"38.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5093"},{"id":462873,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5093/images"},{"id":462872,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5093/coverthb.jpg"},{"id":462877,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWWUAV","text":"USGS Data Release","linkHelpText":"- MODFLOW-NWT model used for the simulation of groundwater flow and analysis of groundwater availability in the Boone and Roubidoux aquifers in northeastern Oklahoma, 1980–2017"},{"id":497943,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117647.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oklahoma","otherGeospatial":"Boone and Roubidoux aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.61835569487307,\n 37.340465151367\n ],\n [\n -95.61835569487307,\n 34.999403485947965\n ],\n [\n -93.99237913237286,\n 34.999403485947965\n ],\n [\n -93.99237913237286,\n 37.340465151367\n ],\n [\n -95.61835569487307,\n 37.340465151367\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"For more information about this publication, contact
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Contact Us- USGS Publications Warehouse
","tableOfContents":"Knowledge of snow and firn-density change is needed to use elevation-change measurements to estimate glacier mass change. Additionally, firn-density evolution on glaciers is closely connected to meltwater percolation, refreezing and runoff, which are key processes for glacier mass balance and hydrology. Since 2016, the U.S. Geological Survey Benchmark Glacier Project has recovered firn cores from a site on Wolverine Glacier in Alaska's Kenai Mountains. We use annual horizons in repeat cores to track firn densification and meltwater retention over seasonal and interannual timescales, and we use density measurements to quantify how the firn air content (FAC) changes through time. The results suggest the firn is densifying due primarily to compaction rather than refreezing. Liquid-water retention in the firn is transient, likely due to gravity-fed drainage and irreducible-water-content decreases that accompany decreasing porosity. We show that the uncertainty (±60 kg m−3) in the commonly used volume-to-mass conversion factor of 850 kg m−3 is an underestimation when glacier-wide FAC variability exceeds 12% of the glacier-averaged height change. Our results demonstrate how direct measurements of firn properties on mountain glaciers can be used to better quantify the uncertainty in geodetic volume-to-mass conversions.
In 2020, Colorado experienced the most severe wildfire season in recorded history, with wildfires burning 625 357 acres across the state. Two of the largest fires burned parts of Rocky Mountain National Park (RMNP), and a study was initiated to address concerns about potential effects on drinking water quality from mobilization of ash and sediment. The study took advantage of a wealth of pre-fire data from adjacent burned and unburned basins in western RMNP. Pre- and post-fire data collection included discrete sample collection and high-frequency water-quality measurements using in-stream sensors. Kruskal–Wallis tests on discrete data indicated that specific conductance, base cations, sulphate, chloride, nitrate, and total dissolved nitrogen concentrations increased post-fire, whereas silica and dissolved organic carbon (DOC) did not (p ≤ 0.05). In-stream sensors captured large spikes in concentrations of nutrients, turbidity, and DOC in the burned basin that were missed by discrete sampling. Sensor data indicated nitrate and turbidity increased by up to one and two orders of magnitude, respectively, from pre-event concentrations during storms, and DOC increased up to 3.5×. Empirical regression equations were developed using pre-fire data and applied to the post-fire period to estimate expected stream chemistry in the absence of fire (a ‘no-fire’ scenario). Overlays of actual post-fire chemistry showed the timing and magnitude of differences between observed and ‘estimated’ chemistry. For most solutes, observed post-fire concentrations were notably greater than expected under the ‘no-fire’ scenario, and differences were greatest during storm events. Comparison of data from the burned and unburned basins indicated DOC concentrations were affected by climate as well as fire. Results from this study demonstrate the importance of both pre-fire data and high-frequency data for characterizing dynamic hydrochemical responses in wildfire-affected areas.
Designated in 1978, Kaloko-Honokōhau National Historical Park is located on the west coast of the Island of Hawaiʻi. The Kaloko-Honokōhau National Historical Park encompasses about 1,200 acres of coastal land and nearshore ecosystems, which include wetlands, anchialine pools (landlocked bodies of brackish water with hydrologic connections to the ocean), fishponds, a fishtrap, and coral reefs. These nearshore ecosystems are dependent on groundwater discharge with a freshwater component and provide habitat for threatened and endangered, endemic species, such as the orangeblack Hawaiian damselfly (Megalagrion xanthomelas) and the Hawaiian coot (ʻAlae keʻokeʻo, Fulica alai). The populations of these native species, however, are threatened because of habitat loss related to urban development and environmental changes. Kaloko-Honokōhau National Historical Park is within the Keauhou aquifer system and the North Kona District, which experienced a 52 percent resident-population increase between 2000 and 2020 and a 41 percent visitor increase between 2008 and 2019. To support the current water demand associated with this growing population, groundwater is the primary source of freshwater used in the North Kona District, with about 15 million gallons of groundwater withdrawn from the Keauhou aquifer system per day since 2009. With anticipated development, future (2015–35) groundwater withdrawal from the Keauhou aquifer system is projected to be about 55 percent greater than recent (2012–14) withdrawal. Because Kaloko-Honokōhau National Historical Park is located within a coastal aquifer, natural and human-induced changes can affect the quality and quantity of groundwater, which can threaten groundwater-dependent ecosystems.
To improve understanding of recent groundwater conditions, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to document correlations between hydrologic time-series datasets from sites in and near Kaloko-Honokōhau National Historical Park using the nonparametric (distribution-free) Kendall’s tau statistical test.
For the statistical analyses, dependent variables representing the groundwater system include groundwater level, the groundwater-level difference between pairs of sites, and specific conductance, and independent variables include datasets of sea level, rainfall, and groundwater withdrawal. About 34 percent of the 140 non-time-lagged Kendall’s tau statistical tests evaluated in this report are statistically significant (p-value ≤ 0.050) with generally weak (0.1 ≤ tau ≤ 0.2) to moderate (0.2 ≤ tau ≤ 0.3) correlations. Groundwater levels measured at monitoring sites have the strongest correlation with the multivariate El Niño–Southern Oscillation index and withdrawal from production wells at the nearby Kohanaiki Private Club Community. Specific conductance is not consistently and significantly correlated with the independent hydrologic variables investigated in this report.
Because the relations between hydrologic variables are commonly not instantaneous, a second set of correlations was evaluated after applying a range of time lags to the independent variable datasets. Relative to the non-time-lagged case (the set of correlations that did not use time-lagged independent variables), some of the time-lagged independent variables improved correlations with some of the dependent variables. For a particular independent variable, similar time lags were expected between the independent variable and dependent variable at all four monitoring sites. However, different time lags among the four sites sometimes produced the strongest correlations.
This study identified several correlations that are statistically significant and hydrologically plausible, but the correlations could indicate that multiple concurrent factors are controlling the observed groundwater-system response, which might be better addressed using multivariate analyses. This study only investigates bivariate correlations, which may not explain all the variance in the data. The correlations analyzed in this report are limited by the quantity of available hydrologic data in the area near Kaloko-Honokōhau National Historical Park and are based on 14 years of time-series data, which were aggregated to a relatively coarse monthly temporal resolution that represents the minimum resolution common to all datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245084","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Okuhata, B.K., and Oki, D.S., 2024, Correlation analysis of groundwater and hydrologic data, Kaloko-Honokōhau National Historical Park, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2024–5084, 38 p., https://doi.org/10.3133/sir20245084.","productDescription":"ix, 38 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-154287","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":462626,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5084/covrthb.jpg"},{"id":462627,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5084/sir20245084.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462628,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5084/sir20245084.xml"},{"id":462629,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5084/images"},{"id":462630,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245084/full"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaloko-Honokōhau National Historical Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.083351222759,\n 19.721737271204077\n ],\n [\n -156.083351222759,\n 19.65199485292854\n ],\n [\n -155.98955903724118,\n 19.65199485292854\n ],\n [\n -155.98955903724118,\n 19.721737271204077\n ],\n [\n -156.083351222759,\n 19.721737271204077\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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Volcanic unrest can trigger appreciable change to surface waters such as streams, springs, and volcanic lakes. Magma degassing produces gases and soluble salts that are absorbed into groundwater that feeds streams and lakes. As magma ascends, the amount of heat and degassing will increase, and so will any related geochemical and thermal signal. Subsurface magma movement can cause pressurization that alters hydrostatic head and may induce groundwater discharge. Fluid-pressure changes have been linked to distal volcano-tectonic earthquakes (White and McCausland, 2016; Coulon and others, 2017) and phreatic eruptions (for example, Yamaoka and others, 2016). Clearly, changes in groundwater and surface waters are both indicators of unrest and clues to how and where magma is rising toward the surface. Where possible, it is prudent to incorporate real-time hydrologic data into multiparameter monitoring of restless volcanoes. Hydrologic dynamics can also be tracked by changes in groundwater levels that are commonly measured in shallow boreholes (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume, on boreholes; Hurwitz and Lowenstern, 2024).
Although inferred to be common, relatively few volcano-hydrology anomalies are well documented, and many are essentially anecdotal (Newhall and others, 2001), reflecting the fact that high-resolution time series remain rare. Extreme examples include the 2008 eruption of Nevado del Huila, Colombia, where relatively minor phreatomagmatic eruptions were accompanied by expulsion of as much as 300 million cubic meters of groundwater from fissures high on the volcano (Worni and others, 2011), generating large lahars. Substantial decreases in flow rate from springs about 8 kilometers from the summit of Mayon Volcano, Philippines, have been noted before most eruptions in the 20th century (Newhall and others, 2001). Stream monitoring at Redoubt Volcano in 2009 allowed Werner and others (2012) to recognize that groundwater was unable to absorb (or scrub) the high flux of volcanic gas and that a high CO2/SO2 precursor signal had been evident for 5 months prior to the eruption. A key to better interpreting hydrologic anomalies—or even identifying them—is therefore obtaining adequate baseline data.
Most hydrologic monitoring at U.S. volcanoes has been accomplished by intermittent sampling surveys with annual or less frequent sampling (for example, https://hotspringchem.wr.usgs.gov/index.php). More frequent sampling, however, generally is needed to establish reliable baselines. A recent hydrologic and hydrothermal monitoring experiment at 25 sites and 10 of the 12 level 4 (very high threat) volcanoes in the U.S. portion of the Cascade Range demonstrated that there is sufficient temporal variability in hydrothermal fluxes, even during quiescent periods, that one-time measurements will commonly have limited interpretive value (Crankshaw and others, 2018). Thus, surveys are best augmented with data from streamgages (for example, Evans and others, 2004; Bergfeld and others, 2008). Streamflow (water discharge) data allow measured temperature and specific conductance to be converted to heat and solute mass fluxes, which could be insightful parameters for detecting anomalous activity (McCleskey and others, 2012). At the Yellowstone Caldera, long-term monitoring of river solutes has allowed calculation of the chloride flux, a proxy for heat discharge (Hurwitz and others, 2007; McCleskey and others, 2016) from the subsurface magma. This is readily accomplished because data from streamgages are continuously recorded and archived by the U.S. Geological Survey (USGS) National Water Information System (NWIS) (USGS, 2024).
Similar studies on stratovolcanoes or shield volcanoes would be scientifically useful, and yet are logistically challenging, requiring streamgages on numerous radial drainages complemented by either frequent manual sampling or numerous deployments of equipment to measure water temperature and specific conductance as a proxy for water chemistry. Another challenge is that some volcanic areas, especially shield volcanoes, are characterized by near-surface porous rocks and soils, such that surface streams are rare and replaced by distant, dilute large-volume springs with only a trace of any original volcanically sourced water (Manga, 2001; Hurwitz and others, 2021).
Volcanic lakes are worthy of special attention for monitoring efforts, as their temperature and composition can provide evidence of increased flux of volatile-rich fluids from below. Quantifying changes in volatile and heat release from magma can be simpler in lakes than for volcanoes with radial drainages and no major lakes. Moreover, volcanic lakes pose a range of hazards themselves, including phreatomagmatic eruptions, debris flows, flank collapse, tsunamis, and toxic gas release (Mastin and Witter, 2000; Delmelle and others, 2015; Manville, 2015; Rouwet and others, 2015)—hazards that have historically been responsible for substantial loss of life at many volcanoes worldwide (Manville, 2015). Catastrophic CO2 release at Lake Nyos, Cameroon, in 1986 suffocated about 1,750 people and about 3,500 livestock and was probably triggered by a large landslide into the gas-saturated lake (Kling and others, 1987; Evans and others, 1993). Gas-charged springs in Soda Bay within Clear Lake (California) have caused almost a dozen deaths to bathers in the past hundred years (ABC News, 2000). A 2005 example of lake overturn and abundant gas release was documented at Mount Chiginagak in Alaska (Schaefer and others, 2008) but did not result in any human casualties. Although thermally stratified lakes, which promote trapping of exsolved magmatic gas, tend to develop in tropical regions, the phenomenon can also arise where salinity creates meromixis (a condition in which a lake does not mix completely), as occurs in Mono Lake, California (Jellison and Melack, 1993; Jellison and others, 1998).
If magma erupts or flows into a lake, the interaction between hot magma and cold water can be explosive (Mastin and others, 2004; Zimanowski and others, 2015) and substantially expand the area affected by the eruption. Another hazard is the breaching of crater rims by landslides triggered by volcanic and (or) seismic activity. Under some circumstances, substantial volumes of water can be displaced, leading to large floods and lahars. Late Holocene lake flooding from Aniakchak Crater in the Alaska Peninsula (Waythomas, 2022) and from Paulina Lake in Newberry Crater, Oregon (Chitwood and Jensen, 2000), caused by the failure of outlet sills, testify to the substantial hazards at lake-filled calderas.
Several volcanic systems in the United States host lakes known to receive heat and gas from underlying magma. These lakes vary widely in area, depth, and chemical composition. Lakes are present at level 4 volcanoes, including Crater Lake and Newberry Volcano in Oregon; Yellowstone Caldera in Wyoming; Long Valley Caldera, Clear Lake volcanic field, Medicine Lake, and Salton Buttes in California; and Aniakchak Crater, Mount Katmai, Fisher Caldera, Mount Okmok, and Kaguyak Crater, among others, in Alaska. A water lake was present in Halemaʻumaʻu, the crater of Kīlauea, Hawai‘i (fig. F1), from October 2019 to December 2020. Level 3 volcanoes with lakes include Mono Lake volcanic field (Calif.), Mount Bachelor (Ore.), Ukinrek Maars and Mount Chiginagak (Alaska), and Soda Lake (Nevada). In addition, there are lakes at many levels 1 and 2 volcanoes. In the United States, there are no strongly acidic lakes that receive abundant input of magmatic gas, such as those found at Mount Ruapehu (New Zealand), Ijen and Kelud (Indonesia), and Poás (Costa Rica). Nevertheless, many contain fluids that provide clues to magmatic processes below.
Since publication of a previous report on recommended instrumentation for volcano monitoring (Moran and others, 2008), continuous hydrologic monitoring has become increasingly feasible. However, changes in water pressure, temperature, and chemistry remain, in general, poorly studied phenomena at volcanoes (Sparks, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Recent efforts by the USGS have included the temporary study of Cascade Range volcanoes, which included frequent (15 minute to hourly) temporal sampling of temperature, depth, and conductivity (Crankshaw and others, 2018; Ingebritsen and Evans, 2019). At Yellowstone Caldera, many streamgages have now added thermistors and specific conductance sensors, allowing estimation of time-dependent chloride flux as a proxy for variations in subsurface heat flux (McCleskey and others, 2012, 2016). Efforts to better understand lakes have also accelerated, with bathymetric mapping and sampling carried out at several locations in the United States. Especially thorough work was done at Yellowstone Lake thanks to the Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE, https://hdylake.org) project, funded primarily by the National Science Foundation. In addition to geophysical surveys and recovery of cores and other samples, HD-YLAKE investigations included remotely operated vehicle (ROV) investigations of hydrothermal vents on the lake floor (fig. F2). Data collected by the ROV provided a better understanding of the thermal and chemical influx from lake-bottom hydrothermal systems (Sohn and others, 2017).
In this chapter, we focus on detecting changes in the chemistry, temperature, discharge, or water levels of streams, springs, and lakes that can be caused by seismicity, volumetric strains, or increases in gas flux associated with ascending magma. There is unavoidable overlap with other chapters of this report. Samples of water and gas can also be obtained in boreholes (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume; Hurwitz and Lowenstern, 2024), both shallow and deep. Gas monitoring (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062e.\">chapter E, this volume; Lewicki and others, 2024) relies in part on samples from springs and wells, particularly where measurable gas plumes are absent. Water acts as a trigger and lubricant for landslides and sediment-rich floods, and so hydrology has obvious relevance for lahar monitoring, as discussed in of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062h. \">chapter H (this volume; Thelen and others, 2024). Shared situational awareness among scientists engaged in geophysical, gas, and hydrologic monitoring will improve overall understanding of the volcanic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062F","usgsCitation":"Ingebritsen, S.E., and Hurwitz, S., 2024, Streams, springs, and volcanic lakes for volcano monitoring, chap. F of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–F, 9 p., https://doi.org/10.3133/sir20245062F.","productDescription":"iii, 9 p.","numberOfPages":"9","onlineOnly":"N","ipdsId":"IP-149695","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/covrthbf.jpg"},{"id":462450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/sir20245062f.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
As magma rises through the crust, decreasing pressure conditions allow volatiles to exsolve from the magma. These volatiles then migrate upward through the crust, where they can be stored at shallower levels or escape to the atmosphere. Rising magma also heats rock masses beneath volcanic centers, causing water in shallow aquifers and hydrothermal systems to boil and release additional gases and steam (see chapter F, this volume; Ingebritsen and Hurwitz, 2024). The chemistry and quantity of gases that reach the surface during periods of quiescence or volcanic unrest can reveal that gas-rich magma is ascending, crystallizing, or alternatively stalling, with important implications for volcanic hazard (for example, Sutton and others, 1992; Aiuppa and others, 2007, 2021; Werner and others, 2009, 2011, 2012; Moretti and others, 2013; de Moor and others, 2016; Lewicki and others, 2019; Edmonds and others, 2022; Kern and others, 2022; Kunrat and others, 2022).
Most volcanoes in Alaska and the western United States are characterized by weak degassing, with one or more low-temperature fumaroles (typically near the local boiling temperature of water) and connect to a deeper and sometimes extensive hydrothermal system (for example, McGee and others, 2001; Symonds and others, 2003a, b). Hydrothermal systems will affect the chemistry of rising gases exsolved from deeper magma (Symonds and others, 2001), including sulfur dioxide (SO2), hydrogen chloride (HCl), and water vapor (for example, Doukas and Gerlach, 1995; Gerlach and others, 1998, 2008; Symonds and others, 2001; Werner and others, 2013). As an example, depending on factors such as temperature, pressure, and oxidation state, rising SO2 will react with groundwater to form hydrogen sulfide (H2S) gas, dissolved sulfate (SO42−), or elemental sulfur (Christenson, 2000; Symonds and others, 2001; Werner and others, 2008). The reaction and dissolution of SO2 into shallow groundwater is commonly referred to as scrubbing, and can reduce the likelihood that ascending, degassing magma can be detected. Carbon dioxide, however, in addition to exsolving from magma early in the ascent process, is not easily removed by hydrothermal fluids (Lowenstern, 2001). As scrubbing and other processes take place, the SO2/H2S, CO2/SO2, and CO2/H2S ratios may change. High rates of SO2 emission indicate that magma has moved to relatively shallow levels in the volcano and that the system has heated up enough to establish dry pathways from depth to the surface. Monitoring multiple gas species and the total output of those species is thereby useful for volcano monitoring during both periods of quiescence, to establish background degassing conditions, and during unrest, when gas geochemistry and emission rates can provide information on changing conditions, such as magma ascent.
To provide context for multidisciplinary volcano forecasts, we focus on the following two key required capabilities: (1) characterizing baseline geochemistry and gas discharge from volcanoes and volcanic regions and (2) monitoring changes in gas geochemistry and discharge to inform forecasts of volcanic eruptions and their effects. Sufficient baseline data must be collected to identify and interpret anomalous degassing associated with volcanic unrest (for example, Sorey and others, 1998; Rouwet and others, 2014). Differences in volcano type, baseline degassing rates, local hydrology, and geography (for example, high versus low latitude) will result in a different baseline for each volcano. Volcanoes of any threat level that exhibit one or more degassing phenomena would ideally be monitored by techniques needed to establish baseline degassing data, with the sampling frequency of baseline data dictated by the threat level (table E1). Additional monitoring techniques become necessary during periods of unrest.
In general, three of the most important techniques for gas monitoring are (1) direct sampling of fumarole, spring, and soil gases for laboratory geochemical measurements, (2) measurements of the chemical composition of the volcanic plume and emission rates of major gas species (for example, H2O, CO2, SO2, and H2S) by satellite, airborne, or ground-based techniques, and (3) measurements of diffuse emissions of CO2 and other gases through soils. Various methods and instruments may be useful both for baseline studies and during unrest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062E","usgsCitation":"Lewicki, J.L., Kern, C., Kelly, P.J., Nadeau, P.A., Elias, T., and Clor, L.E., 2024, Volcanic gas monitoring, chap. E of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062E.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"N","ipdsId":"IP-150252","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/e/sir20245062e.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/e/covrthbe.jpg"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
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Anchorage, AK 99508
Long‐term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
We investigated the feasibility of using two analytical methods, N‐mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long‐term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions.
When compared with abundance estimates from mark–recapture methods, N‐mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N‐mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N‐mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models.
N‐mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long‐term data sets.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.11026","usgsCitation":"Briggs, M., Glassic, H.C., Guy, C.S., Opitz, S., Rotella, J., and Schmetterling, D., 2024, Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA: North American Journal of Fisheries Management, v. 44, no. 5, p. 947-961, https://doi.org/10.1002/nafm.11026.","productDescription":"15 p.","startPage":"947","endPage":"961","ipdsId":"IP-172896","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.11026","text":"Publisher Index Page"},{"id":486239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"upper Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.27790695947738,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.00210734009434\n ],\n [\n -110.23435146225052,\n 45.77750283140904\n ],\n [\n -111.27790695947738,\n 45.77750283140904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"44","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Briggs, Michelle A.","contributorId":355621,"corporation":false,"usgs":false,"family":"Briggs","given":"Michelle A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glassic, Hayley Corrine 0000-0001-6839-1026","orcid":"https://orcid.org/0000-0001-6839-1026","contributorId":305858,"corporation":false,"usgs":true,"family":"Glassic","given":"Hayley","email":"","middleInitial":"Corrine","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":937792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":937793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Opitz, Scott T.","contributorId":355622,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott T.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rotella, Jay J.","contributorId":355623,"corporation":false,"usgs":false,"family":"Rotella","given":"Jay J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmetterling, David A.","contributorId":355624,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David A.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":937796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266769,"text":"70266769 - 2024 - Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA","interactions":[],"lastModifiedDate":"2025-05-13T15:43:28.159582","indexId":"70266769","displayToPublicDate":"2024-10-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA","docAbstract":"Objective
Long-term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark-recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the last two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.
Methods
We investigated the feasibility of using two analytical methods, N-mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River; these methods allow abundance to be estimated when a mark-recapture estimate cannot be obtained due to hydrologic conditions.
Result
When compared to abundance estimates from mark-recapture methods, N-mixture models most often resulted in negatively biased abundance estimates while mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N-mixture models produced negatively biased estimates compared to true abundance values from simulated datasets. Bias in N-mixture model estimates was caused by poor model fit and variation in capture probability. Bias in mean capture probability estimates was caused by heterogeneity in capture probability that was not accounted for in the models.
Conclusion
N-mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining long-term datasets.
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The groundwater levels estimated from these record extensions were used to compute monthly percentiles to improve future determinations of a groundwater index. In Massachusetts, 27 of 29 short-record study wells with continuous groundwater levels between 0.8 and 8.1 years were suitable for record extensions; 37 long-record index wells were used to extend the groundwater level records at the study wells. The index well selected to pair with a study well was chosen based on Pearson correlation coefficient values; cross-correlation between the two wells; geologic and topographic similarity; and smallest distance spanning the wells. Each study well and its corresponding index well have 1 or more years of concurrent, overlapping data; a Pearson correlation coefficient that exceeded a threshold value of 0.8; and a similar aquifer type and hydrologic characteristics. Of the 29 study wells, 2 showed poor correlations with all index wells and were not considered for record extensions.
Performance metrics used to assess the accuracy of the MOVE.1 models indicated that most models provided reasonable estimates of groundwater levels. Root mean square error values ranged from 0.097 to 2.292 feet, with a median of 0.536 foot. Nash-Sutcliffe efficiency coefficient values ranged from 0.623 to 0.996, with a median value of 0.759. Generally, study wells in close geographical proximity to their index well resulted in stronger model performance.
The average length of groundwater level records was extended by 14.1 years to a new average of 18.1 years. The estimated groundwater level records from the MOVE.1 models resulted in an increase in the range of highest and lowest groundwater levels at 23 of 27 wells. The increase in range of groundwater levels was between 0.08 to 7.95 feet. Monthly percentiles for State drought indices were computed from the estimated MOVE.1 records and observed records through December 31, 2021. Percentiles computed from estimated records show an average groundwater level about 1.0 foot lower than observed data at the 2d percentile and 0.1 foot lower at the 30th percentile.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Vegetation communities in restored bottomland hardwood forests in northeast Indiana were studied 6–21 years after restoration to assess progress toward restoration objectives. The study focused on four sites that were restored to compensate for resource injuries after contaminant releases. The restored sites were compared with four reference-site conditions, including crops (prerestoration condition), old field communities representing a no-management alternative, locally sampled second-growth mature forests, and forest community types described by the US National Vegetation Classification (USNVC), which represent ideal or defining conditions of recognized vegetation communities. Fixed-area plots provided data on field-sampled environmental variables, vegetation, soil, and hydrological conditions for crops, old fields, restored areas, and mature forests. The USNVC database provided quantitative data for three historically and geographically relevant reference forest community types for comparison with the sampled communities. Results of nonmetric multidimensional scaling based on species cover revealed clear gradients relating to site age and canopy development. Along those gradients, restored areas demonstrated increasing similarity to mature forest reference communities in terms of floristic composition. Specifically, the floristic quality of restored areas was significantly greater than that of crops and old fields. Furthermore, soil health measurements of physical, chemical, and hydrological conditions indicated significant improvements in restored site soils compared with prerestoration conditions represented by cropland soils. Descriptions and data from the USNVC provided ecological context for restoration target conditions and facilitated the assessment of restoration recovery along a trajectory from starting conditions to those target conditions. Descriptions by USNVC also helped identify deviations from the intended restoration objectives (e.g., invasive species recruitment) and potential adaptive management actions to return sites to their intended trajectories. Integr Environ Assess Manag 2024;00:1–22. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Elevated nitrogen loads are pervasive in the Long Island Sound, an estuary that receives freshwater and nutrients from both surface-water and groundwater discharge. Surface-water nitrogen loads to the Long Island Sound are relatively well characterized, but less is known about groundwater-transported nitrogen loads. Prior work on the northern shore of Long Island Sound (Connecticut and areas of New York and Rhode Island) suggested that groundwater travel times are relatively short (median less than 2 years) and that decade-long nutrient legacies are not widespread. Because the travel times are short, groundwater flow and nutrient loads likely vary substantially between months. In the current study, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection, developed a set of models to better characterize spatial and temporal patterns of groundwater-transported nitrogen loading from atmospheric deposition, septic systems, and fertilizers within the study area. The models provide an estimate, with uncertainty, of groundwater-transported nitrogen loads in the study area, filling a key gap in the nitrogen budget for Long Island Sound. The models also highlight the spatial and temporal variation in nitrogen loading throughout the study area.
The modeling workflow involved four models. (1) A soil-water-balance model was developed by using the Soil-Water-Balance software to simulate groundwater recharge across the study area for water years 2005 through 2022. The simulated mean monthly recharge from the soil-water-balance model was used as input into a groundwater-flow model. (2) The groundwater-flow model was developed by using the MODFLOW 6 software and data for water years 1993 through 2022 and simulates average monthly hydrologic conditions. The groundwater-flow model was calibrated by using the Iterative Ensemble Smoother method within the PEST++ software. The Iterative Ensemble Smoother method generates an ensemble of sets of parameter values, with each set producing reasonable simulated hydrologic parameter values. (3) An ensemble of MODPATH particle-tracking simulations were run to generate particle flow paths and travel times, with each simulation using a different set of the flow model parameters. (4) A nitrogen load model uses the MODPATH simulation outputs to track nitrogen from the land surface through multiple attenuation zones until it discharges into fresh or saline surface water. As with the groundwater-flow model, the nitrogen model simulated average monthly groundwater-transported nitrogen loads for water years 1993 through 2022. One novel aspect of the nitrogen load model is that the nitrogen attenuation parameters were calibrated to observed nitrogen loads.
Across the ensemble of simulated nitrogen loads, the median study-area-wide monthly simulated nitrogen loads from the aquifer to Long Island Sound throughout the year ranged from 900 to 18,600 kilograms of nitrogen per day, with a median load of 5,100 kilograms of nitrogen per day. The simulated loads were based on average monthly conditions for water years 1993 through 2022. Loads were highest during the winter and early spring and lowest during the late summer. However, simulated travel times for groundwater and nitrogen loads discharged to Long Island Sound during summer were longer than travel times for groundwater and loads discharged during the winter, indicating that, on average, groundwater discharged during summer traveled along different, and longer, flow paths, than groundwater discharged during winter. This indicates that summer loads would respond more slowly to changes in nitrogen inputs at the water table than winter loads. Over the entire study area, approximately 15 percent of the simulated load is from atmospheric deposition sources, 30 to 40 percent is from fertilizer, and 50 to 60 percent is from septic systems.
The final analysis of the study involved simulating the change in groundwater-transported nitrogen load in response to upgrading septic systems or reducing fertilizing inputs to areas of turf grass. Both management interventions reduced the groundwater-transported nitrogen load, and reductions were greater in areas with greater loads from septic systems or turf-grass fertilizers. The delay between management actions and substantial reductions in groundwater-transported nitrogen loads varied seasonally; loads during the late summer months remained elevated longer than the winter loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., Holland, M.J., and Mullaney, J.R., 2024, Simulated mean monthly groundwater-transported nitrogen loads in watersheds on the north shore of Long Island Sound, 1993–2022: U.S. Geological Survey Scientific Investigations Report 2024–5090, 63 p., https://doi.org/10.3133/sir20245090.","productDescription":"Report: xi, 63; 3 Data Releases","numberOfPages":"63","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-150246","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":462294,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1HKENGV","text":"USGS data release","linkHelpText":"MODFLOW6 groundwater flow model, MODPATH particle-tracking simulation, and groundwater-transported nitrogen load model of average monthly conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, 1993–2022"},{"id":497952,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117502.htm","linkFileType":{"id":5,"text":"html"}},{"id":462296,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215116","text":"Scientific Investigations Report 2021–5116","linkHelpText":"- Simulation of Groundwater Budgets and Travel Times for Watersheds on the North Shore of Long Island Sound, With Implications for Nitrogen-Transport Studies"},{"id":462295,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1GUC7FE","text":"USGS data release","linkHelpText":"Soil-Water-Balance model developed to simulate net infiltration in watersheds on the north shore of the Long Island Sound"},{"id":462293,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XEN74S","text":"USGS data release","linkHelpText":"Summary simulated groundwater-transported nitrogen loads on the north shore of Long Island Sound and associated data"},{"id":462292,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5090/sir20245090.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5090 XML"},{"id":462291,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5090/images/"},{"id":462290,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245090/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5090 HTML"},{"id":462289,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5090/sir20245090.pdf","text":"Report","size":"24.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5090 PDF"},{"id":462288,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5090/coverthb.jpg"}],"country":"United States","state":"Connecticut, Rhode Island","otherGeospatial":"Long Island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.60521599677895,\n 40.99974278286342\n ],\n [\n -71.33663482988541,\n 40.99974278286342\n ],\n [\n -71.33663482988541,\n 41.7908892811372\n ],\n [\n -73.60521599677895,\n 41.7908892811372\n ],\n [\n -73.60521599677895,\n 40.99974278286342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Transitions between dry and wet hydrologic states are the defining characteristic of non-perennial rivers and streams, which constitute the majority of the global river network. Although past work has focused on stream drying characteristics, there has been less focus on how hydrology, ecology and biogeochemistry respond and interact during stream wetting. Wetting mechanisms are highly variable and can range from dramatic floods and debris flows to gradual saturation by upwelling groundwater. This variation in wetting affects ecological and biogeochemical functions, including nutrient processing, sediment transport and the assembly of biotic communities. Here we synthesize evidence describing the hydrological mechanisms underpinning different types of wetting regimes, the associated biogeochemical and organismal responses, and the potential scientific and management implications for downstream ecosystems. This combined multidisciplinary understanding of wetting dynamics in non-perennial streams will be key to predicting and managing for the effects of climate change on non-perennial ecosystems.
","language":"English","publisher":"Nature","doi":"10.1038/s44221-024-00298-3","usgsCitation":"Adam N. Price, Zimmer, M.A., Anna J. Bergstrom, Amy J Burgin, Erin C. Seybold, Corey A. Krabbenhoft, Zipper, S., Busch, M.H., Dodds, W.K., Walters, A.W., Rogosch, J., Stubbington, R., Walker, R.H., Stegen, J.C., Thibault Datry, Messager, M.L., Olden, J., Godsey, S., Shanafield, M., Lytle, D.E., Burrows, R., Kaiser, K.E., Allen, G., Mims, M.C., Tonkin, J.D., Bogan, M., Hammond, J.C., Boersma, K., Myers-Pigg, A., DelVecchia, A., Allen, D., Yu, S., and Ward, A., 2024, Biogeochemical and community ecology responses to the wetting of non-perennial streams: Nature Water, v. 2, p. 815-826, https://doi.org/10.1038/s44221-024-00298-3.","productDescription":"12 p.","startPage":"815","endPage":"826","ipdsId":"IP-155510","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490043,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.inrae.fr/hal-04750033","text":"External Repository"},{"id":462708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2024-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Adam N. Price","contributorId":337487,"corporation":false,"usgs":false,"family":"Adam N. Price","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":902461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmer, Margaret Ann 0000-0001-8287-1923","orcid":"https://orcid.org/0000-0001-8287-1923","contributorId":337488,"corporation":false,"usgs":true,"family":"Zimmer","given":"Margaret","email":"","middleInitial":"Ann","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":902462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anna J. Bergstrom","contributorId":337489,"corporation":false,"usgs":false,"family":"Anna J. Bergstrom","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":902463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amy J Burgin","contributorId":337490,"corporation":false,"usgs":false,"family":"Amy J Burgin","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":902464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erin C. Seybold","contributorId":337492,"corporation":false,"usgs":false,"family":"Erin C. Seybold","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":902465,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Corey A. Krabbenhoft","contributorId":337495,"corporation":false,"usgs":false,"family":"Corey A. Krabbenhoft","affiliations":[{"id":40126,"text":"University of Buffalo","active":true,"usgs":false}],"preferred":false,"id":902466,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zipper, Sam","contributorId":337500,"corporation":false,"usgs":false,"family":"Zipper","given":"Sam","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":902467,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Busch, Michelle H.","contributorId":337503,"corporation":false,"usgs":false,"family":"Busch","given":"Michelle","email":"","middleInitial":"H.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":902468,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dodds, Walter K.","contributorId":337505,"corporation":false,"usgs":false,"family":"Dodds","given":"Walter","email":"","middleInitial":"K.","affiliations":[{"id":12661,"text":"Kansas State 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0000-0003-4252-5991","orcid":"https://orcid.org/0000-0003-4252-5991","contributorId":225165,"corporation":false,"usgs":false,"family":"DelVecchia","given":"Amanda","email":"","affiliations":[{"id":41061,"text":"Flathead Lake Biological Station, University of Montana, Polson, MT 59860","active":true,"usgs":false}],"preferred":false,"id":915335,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Allen, Daniel C. 0000-0002-0451-0564","orcid":"https://orcid.org/0000-0002-0451-0564","contributorId":225169,"corporation":false,"usgs":false,"family":"Allen","given":"Daniel","middleInitial":"C.","affiliations":[{"id":41064,"text":"Department of Biology, University of Oklahoma, Norman OK, 73019","active":true,"usgs":false}],"preferred":false,"id":915336,"contributorType":{"id":1,"text":"Authors"},"rank":31},{"text":"Yu, Songyan","contributorId":340707,"corporation":false,"usgs":false,"family":"Yu","given":"Songyan","email":"","affiliations":[],"preferred":false,"id":915337,"contributorType":{"id":1,"text":"Authors"},"rank":32},{"text":"Ward, Adam 0000-0002-6376-0061","orcid":"https://orcid.org/0000-0002-6376-0061","contributorId":296003,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","affiliations":[{"id":40154,"text":"Indiana University Bloomington","active":true,"usgs":false}],"preferred":false,"id":915338,"contributorType":{"id":1,"text":"Authors"},"rank":33}]}} ,{"id":70259292,"text":"70259292 - 2024 - Evaluating a process-guided deep learning approach for predicting dissolved oxygen in streams","interactions":[],"lastModifiedDate":"2024-10-03T13:42:22.479607","indexId":"70259292","displayToPublicDate":"2024-09-19T08:39:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating a process-guided deep learning approach for predicting dissolved oxygen in streams","docAbstract":"Dissolved oxygen (DO) is a critical water quality constituent that governs habitat suitability for aquatic biota, biogeochemical reactions and solubility of metals in streams. Recently introduced high-frequency sensors have increased our ability to measure DO, but we still lack the capacity to understand and predict DO concentrations at high spatial resolutions or in unmonitored locations. Machine learning (ML) has been a commonly used approach for modelling DO, however, conventional ML models have no representation of the limnological processes governing DO dynamics. Here we implement and evaluate two process-guided deep learning (PGDL) approaches for predicting daily minimum, mean and maximum DO concentrations in rivers from the Delaware River Basin, USA. In both cases, a multi-task approach was taken in which the PGDL models predicted stream metabolism and gas exchange rates in addition to the DO concentrations themselves. Our results showed that for these sites, the PGDL approaches did not improve upon baseline predictions in temporal and spatially similar holdout experiments. One of the approaches did, however, improve predictions when applied to spatially dissimilar sites. Although this particular PGDL approach did not improve predictive accuracy in most cases, our results suggest that process guidance, perhaps a more constrained approach, could benefit a data-driven DO model.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.15270","usgsCitation":"Sadler, J., Koenig, L.E., Gorski, G., Carter, A.M., and Hall, R.O., 2024, Evaluating a process-guided deep learning approach for predicting dissolved oxygen in streams: Hydrological Processes, v. 38, no. 9, e15270, 13 p., https://doi.org/10.1002/hyp.15270.","productDescription":"e15270, 13 p.","ipdsId":"IP-158066","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":498265,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.15270","text":"Publisher Index Page"},{"id":462530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Sadler, Jeffrey M 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":302989,"corporation":false,"usgs":false,"family":"Sadler","given":"Jeffrey M","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":914809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koenig, Lauren Elizabeth 0000-0002-7790-330X","orcid":"https://orcid.org/0000-0002-7790-330X","contributorId":295259,"corporation":false,"usgs":true,"family":"Koenig","given":"Lauren","email":"","middleInitial":"Elizabeth","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":914810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorski, Galen 0000-0003-0083-4251","orcid":"https://orcid.org/0000-0003-0083-4251","contributorId":329714,"corporation":false,"usgs":true,"family":"Gorski","given":"Galen","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":914811,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carter, Alice M. 0000-0002-7225-7249","orcid":"https://orcid.org/0000-0002-7225-7249","contributorId":298702,"corporation":false,"usgs":false,"family":"Carter","given":"Alice","email":"","middleInitial":"M.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":914812,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hall, Robert O. Jr.","contributorId":203473,"corporation":false,"usgs":false,"family":"Hall","given":"Robert","suffix":"Jr.","email":"","middleInitial":"O.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":914813,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258497,"text":"sir20245079 - 2024 - Geomorphic change, hydrology, and hydraulics of Caulks Creek, Wildwood, Missouri","interactions":[],"lastModifiedDate":"2025-12-23T22:25:56.752501","indexId":"sir20245079","displayToPublicDate":"2024-09-18T10:40:07","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5079","displayTitle":"Geomorphic Change, Hydrology, and Hydraulics of Caulks Creek, Wildwood, Missouri","title":"Geomorphic change, hydrology, and hydraulics of Caulks Creek, Wildwood, Missouri","docAbstract":"Caulks Creek is a small stream that flows through the city of Wildwood in western St. Louis County, Missouri. The U.S. Geological Survey, in cooperation with the city of Wildwood, has documented historical and recent geomorphic change along Caulks Creek, simulated the hydrologic and hydraulic response of Caulks Creek to a variety of design storm scenarios, and simulated bank retreat resulting from fluvial erosion and mass failure processes.
Six study reaches were selected for monitoring geomorphic change based on known locations of erosion issues documented by the city of Wildwood. Recent short-term rates and patterns of geomorphic change in the study reaches, with a focus on bank retreat, were determined from repeat terrestrial light detection and ranging surveys and field observations of the six study reaches. Historical aerial photographs of the study reaches were analyzed to determine long-term rates of bank retreat and channel widening. In general, rapid bank retreat and widening was observed at the outer banks of meander bends and where banks are unforested. Short-term bank retreat varied substantially within individual study reaches, across the study area, and during the study period from no change to as much as 16 feet of retreat between consecutive surveys (5 to 8 months). The field surveys and visual observations indicated that bank retreat occurs episodically owing to a combination of fluvial erosion and mass failure processes, as well as freezing and thawing cycles. Long-term rates of bank retreat ranged from 0.6 to 4.4 feet per year.
Hydrologic and hydraulic models of Caulks Creek were used to quantify the peak, volume, and timing of the flow response and the spatial distribution of hydraulic drivers of erosion (velocity and shear stress) along Caulks Creek for design storm scenarios that represent current (as of this publication) and projected future climate. The projected climate conditions resulted in higher peak flows compared to current conditions, including 6 to 21 percent for the year 2050 and 10 to 42 percent for the year 2099 at the downstream end of the study area. Additionally, for a given design storm, projected climate change is predicted to result in faster flows with greater shear stress, as well as more within-stream variability in velocity and shear stress. Many factors affect the velocity and shear stress at a given location, but in general, somewhat fast velocities and high shear stresses tended to occur where the channel is relatively narrow and straight. The velocity and shear stress in the study reaches (known areas of widening and bank retreat) were not particularly high, at least in part owing to the relatively large widths and high sinuosity of the present-day channel in these reaches.
The potential mitigating effect of adding runoff storage to the basin also was examined for a selection of design storm scenarios. Additional runoff storage was more effective at mitigating peak flows and total runoff volumes for higher-frequency, lower-intensity storms than for lower-frequency, higher-intensity storms. The additional storage also resulted in an overall reduction in velocity (by as much as 28 percent) and shear stress (by as much as 40 percent) in the study area. However, the effect of the additional storage on peak flows, total runoff volumes, velocity, and shear stress decreased with distance downstream through the study area. For the simulated scenarios, added runoff storage was effective at mitigating the increases in peak flows, total runoff volumes, velocity, and shear stress caused by projected climate change.
Lastly, the bank stability and toe erosion model (BSTEM) was used to predict bank erosion and potential bank failure surfaces at five locations along Caulks Creek for a selection of design storm scenarios. The lower-frequency, higher-magnitude design storms resulted in more bank retreat than the higher-frequency, lower-magnitude design storms, though the magnitude of the difference was site dependent. Although scenarios with additional storage were not directly simulated in BSTEM, it is likely that the additional storage would result in reduced bank retreat compared to the same design storm with existing storage, based on the hydraulic modeling results for scenarios with added runoff storage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245079","collaboration":"Prepared in cooperation with the City of Wildwood","usgsCitation":"LeRoy, J.Z., Heimann, D.C., Hix, K.D., Cigrand, C.V., and Burk, T.J., 2024, Geomorphic change, hydrology, and hydraulics of Caulks Creek, Wildwood, Missouri (ver. 1.1, November 2024): U.S. Geological Survey Scientific Investigations Report 2024–5079, 118 p., https://doi.org/10.3133/sir20245079.","productDescription":"Report: x, 118 p.; 4 Data Releases; 2 Datasets","numberOfPages":"132","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-136190","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497919,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117492.htm","linkFileType":{"id":5,"text":"html"}},{"id":463779,"rank":12,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5079/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}},{"id":434867,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P138PRQL","text":"USGS data release","linkHelpText":"Topographic and bank erosion pin data used in monitoring of bank erosion in Caulks Creek, Wildwood, Missouri, 2022–23"},{"id":434866,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PBF12F","text":"USGS data release","linkHelpText":"Archive of hydrologic and hydraulic models used in the simulation of hydraulic characteristics of Caulks Creek, Wildwood, Missouri"},{"id":434865,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KZCM54","text":"USGS data release","linkHelpText":"National Land Cover Database (NLCD) 2019 products (ver. 2.0, June 2021)"},{"id":434864,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245079/full"},{"id":434862,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5079/images/"},{"id":434861,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5079/sir20245079.XML"},{"id":434870,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://mesonet.agron.iastate.edu/rainfall/","text":"Iowa State University, Iowa Environmental Mesonet","linkHelpText":"- MRMS estimates—GIS raster in ERDAS imagine (.IMG) format"},{"id":434869,"rank":10,"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":434868,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9STTC43","text":"USGS data release","linkHelpText":"Archive of Bank Stability and Toe Erosion Model (BSTEM) simulations of Caulks Creek, Wildwood, Missouri"},{"id":463778,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5079/sir20245079.pdf","text":"Report","size":"28.4","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5079"},{"id":434859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5079/coverthb2.jpg"}],"country":"United States","state":"Missouri","city":"Wildwood","otherGeospatial":"Caulks Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.5,\n 38.66\n ],\n [\n -90.66,\n 38.66\n ],\n [\n -90.66,\n 38.5667\n ],\n [\n -90.5,\n 38.5667\n ],\n [\n -90.5,\n 38.66\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: September 18, 2024; Version 1.1: November 7, 2024","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The seasonal behavior of fluvial dissolved silica (DSi) concentrations, termed DSi regime, mediates the timing of DSi delivery to downstream waters and thus governs river biogeochemical function and aquatic community condition. Previous work identified five distinct DSi regimes across rivers spanning the Northern Hemisphere, with many rivers exhibiting multiple DSi regimes over time. Several potential drivers of DSi regime behavior have been identified at small scales, including climate, land cover, and lithology, and yet the large-scale spatiotemporal controls on DSi regimes have not been identified. We evaluate the role of environmental variables on the behavior of DSi regimes in nearly 200 rivers across the Northern Hemisphere using random forest models. Our models aim to elucidate the controls that give rise to (a) average DSi regime behavior, (b) interannual variability in DSi regime behavior (i.e., Annual DSi regime), and (c) controls on DSi regime shape (i.e., minimum and maximum DSi concentrations). Average DSi regime behavior across the period of record was classified accurately 59% of the time, whereas Annual DSi regime behavior was classified accurately 80% of the time. Climate and primary productivity variables were important in predicting Average DSi regime behavior, whereas climate and hydrologic variables were important in predicting Annual DSi regime behavior. Median nitrogen and phosphorus concentrations were important drivers of minimum and maximum DSi concentrations, indicating that these macronutrients may be important for seasonal DSi drawdown and rebound. Our findings demonstrate that fluctuations in climate, hydrology, and nutrient availability of rivers shape the temporal availability of fluvial DSi.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JG008141","usgsCitation":"Johnson, K., Jankowski, K.J., Carey, J.C., Sethna, L.R., Bush, S.A., McKnight, D.M., McDowell, W.H., Wymore, A.S., Kortelainen, P., Jones, J.B., Lyon, N., Laudon, H., Poste, A., and Sullivan, P.L., 2024, Climate, hydrology, and nutrients control the seasonality of Si concentrations in rivers: Journal of Geophysical Research Biogeosciences, v. 129, no. 9, e2024JG008141, 21 p., https://doi.org/10.1029/2024JG008141.","productDescription":"e2024JG008141, 21 p.","ipdsId":"IP-164085","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":466920,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jg008141","text":"Publisher Index 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97331","active":true,"usgs":false}],"preferred":false,"id":917640,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70262256,"text":"70262256 - 2024 - Evaluating habitat use and relative abundance of Iowa's river otter with harvest data","interactions":[],"lastModifiedDate":"2025-01-22T16:57:13.643782","indexId":"70262256","displayToPublicDate":"2024-09-09T10:53:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating habitat use and relative abundance of Iowa's river otter with harvest data","docAbstract":"The North American river otter (Lontra canadensis) was extirpated from much of the United States in the early 20th century due to habitat loss, pollution of waterways, and overharvesting. The Iowa Department of Natural Resources began a river otter reintroduction effort in 1985, which placed otters in 14 sites across the state. Otters have since been known to occur in every county in Iowa and appear to have successfully repopulated their former range throughout the state. Our objective was to relate land cover characteristics and otter abundance using harvest data. We used data collected by agency staff to map the locations of otter harvest in Iowa from 2006 to 2016. We mapped otter harvest locations at the subwatershed level (also called 12-digit Hydrologic Unit Code or HUC-12). We related otter harvest to land cover variables and predicted otter abundance by land cover type. We found that roads, forests, larger waterways, and Ictaluridae (catfish) presence were negatively correlated with otter harvest. Variables positively correlated with otter harvest were areas with greater land cover diversity, wetland patch density, average stream density, and waterway and wetland areas. The land cover model predicted otters in equal or greater numbers than the harvest data in 62.8% of HUC-12s. The areas of greatest otter abundance estimates were located near recreation areas and urban areas, indicating the underutilization of these heavy-trafficked areas by trappers. Areas of fewer predicted otters were not concentrated in a single area of the state but occurred along the Interstate 80 corridor.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1543","usgsCitation":"Nixon, B., Evelsizer, V., and Klaver, R.W., 2024, Evaluating habitat use and relative abundance of Iowa's river otter with harvest data: Wildlife Society Bulletin, v. 48, no. 3, e1543, 13 p., https://doi.org/10.1002/wsb.1543.","productDescription":"e1543, 13 p.","ipdsId":"IP-155037","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1543","text":"Publisher Index Page"},{"id":480935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nixon, Bridget A.","contributorId":348634,"corporation":false,"usgs":false,"family":"Nixon","given":"Bridget A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evelsizer, Vince","contributorId":348636,"corporation":false,"usgs":false,"family":"Evelsizer","given":"Vince","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klaver, Robert W. 0000-0002-3263-9701 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transformer","docAbstract":"The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning, and forest management. Despite noisy and incorrectly classified points, the current 3DEP classification specifications result in data that can be useful for Digital Terrain Model (DTM) extraction; however, the quality of the classification application can be improved to match state-of-the-art capabilities. Deep Learning (DL)-based approaches have been developed with outstanding performance for point cloud classification. This study will utilize the proven DL technologies to prepare for developing a user-friendly open-source toolkit that would automate classification to refine and enrich the results of existing and future 3DEP data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2024 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 7-12, 2024","conferenceLocation":"Athens, Greece","language":"English","publisher":"The Institute of Electrical and Electronics Engineers (IEEE)","doi":"10.1109/IGARSS53475.2024.10641055","usgsCitation":"Liu, J., Qin, R., and Song, S., 2024, Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer, in Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, July 7-12, 2024, p. 8518-8521, https://doi.org/10.1109/IGARSS53475.2024.10641055.","productDescription":"4 p.","startPage":"8518","endPage":"8521","ipdsId":"IP-159942","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":439175,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/poster/Automated_Deep_Learning-based_Point_Cloud_Classification_on_USGS_3DEP_LiDAR_Data_Using_a_Transformer/26169397","text":"External Repository"},{"id":434765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jung-Kuan 0000-0001-8461-8200","orcid":"https://orcid.org/0000-0001-8461-8200","contributorId":333940,"corporation":false,"usgs":true,"family":"Liu","given":"Jung-Kuan","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":913062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qin, Rongjun","contributorId":333939,"corporation":false,"usgs":false,"family":"Qin","given":"Rongjun","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":913063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Song, Shuang","contributorId":344174,"corporation":false,"usgs":false,"family":"Song","given":"Shuang","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":913064,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70258078,"text":"sir20235038 - 2024 - Three-dimensional geologic framework model of the Rio San Jose groundwater basin and adjacent areas, New Mexico","interactions":[],"lastModifiedDate":"2026-01-29T22:50:07.82264","indexId":"sir20235038","displayToPublicDate":"2024-09-04T14:30:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5038","displayTitle":"Three-Dimensional Geologic Framework Model of the Rio San Jose Groundwater Basin and Adjacent Areas, New Mexico","title":"Three-dimensional geologic framework model of the Rio San Jose groundwater basin and adjacent areas, New Mexico","docAbstract":"As part of a U.S. Geological Survey study in cooperation with the Bureau of Reclamation and the Pueblo of Acoma, New Mexico, and the Pueblo of Laguna, New Mexico, a digital three-dimensional geologic framework model was constructed for the Rio San Jose and its surface-water drainage basin in west-central New Mexico. This three-dimensional model defines the altitude, thickness, and extent of 18 geologic units for use in a regional numerical hydrologic model. The model included an undifferentiated Proterozoic basement layer, 13 consolidated Paleozoic and Mesozoic rock units, and 4 Cenozoic units. Model input data were compiled from published cross sections, well data, structure contour maps, selected geophysical data, and data derived from geologic maps and structural features in the study area. These data were used to construct faulted surfaces that represent the upper and lower subsurface geologic unit boundaries. The digital three-dimensional geologic framework model combines faults, the altitude of the tops of each geologic unit, and boundary lines depicting the subsurface extent of each geologic unit. The digital three-dimensional geologic model described in this report and the corresponding data release represents the generalized geometry of the subsurface geologic units; it reproduces with reasonable accuracy the input geologic data and is consistent with previously published subsurface conceptualizations of the region. The geologic framework model is at a scale and resolution appropriate for use as the foundation for a numerical hydrologic model of the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235038","collaboration":"Prepared in cooperation with the Bureau of Reclamation, the Pueblo of Acoma, New Mexico, and the Pueblo of Laguna, New Mexico","usgsCitation":"Sweetkind, D.S., and Galanter, A.E., 2024, Three-dimensional geologic framework model of the Rio San Jose groundwater basin and adjacent areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5038, 35 p., https://doi.org/10.3133/sir20235038.","productDescription":"Report: vii, 35 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-137937","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":433454,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MPAGA7","text":"USGS data release","linkHelpText":"Digital data for three-dimensional geologic framework model of the Rio San Jose groundwater basin, New Mexico"},{"id":433422,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5038/coverthb.jpg"},{"id":433423,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038.pdf","text":"Report","size":"43.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5038"},{"id":433462,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038.xml"},{"id":433425,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038_ReadMe.txt","size":"8.00 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2023-5038 Read me file"},{"id":433426,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038.mp4","text":"Animation","size":"29.2 MB","description":"SIR 2023-5038 animation","linkHelpText":"Video to accompany Figure 9. Perspective view of three-dimensional geologic framework solid model showing modeled geologic units"},{"id":433461,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5038/images"},{"id":433580,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235038/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5038"},{"id":499302,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111885.htm","linkFileType":{"id":5,"text":"html"}},{"id":433424,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5038/sir20235038_optimized.pdf","text":"Report","size":"24.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5038 optimized for screen reading","linkHelpText":"Optimized for screen reading"}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Groundwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.77168949319592,\n 36.208846271635096\n ],\n [\n -108.77168949319592,\n 34.24361847680689\n ],\n [\n -106.69527347757077,\n 34.24361847680689\n ],\n [\n -106.69527347757077,\n 36.208846271635096\n ],\n [\n -108.77168949319592,\n 36.208846271635096\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
Communities around the world rely on snowmelt to meet water demands, and periods of lower than normal snow accumulation, snow droughts, can decrease water supplies. Leveraging 172 minimally disturbed and seasonally snow-covered watersheds, we developed an approach to examine the effects of cool & dry, warm & dry, and warm & wet snow droughts on streamflow timing and magnitude by hydrologic region. Our results showed all types of snow droughts in all regions correlate with lower annual streamflow, lower maximum and minimum flows, and lower runoff ratios, with more numerous low flow days and earlier streamflow timing. However, departures from non-snow drought conditions differed substantially between drought types and regions. Consecutive snow droughts further reduced runoff ratios and increased low flow days, likely due to additional subsurface storage depletion. With warm snow drought occurrence expected to increase, we discuss impacts for water management systems whose design specifications may not reflect the changing hydroclimate.
The National Park Service (NPS) is charged with maintaining natural riverine resources and processes in its parks along the Yampa River and downstream along the Green River. This mission requires information on how proposed water withdrawals would affect resources. We present a methodology that quantifies the impact on natural riverine and riparian features of Dinosaur National Monument based on alternative withdrawals that vary in volume and timing. This methodology uses a reverse quantification and develops tools to enable the NPS to ensure that if withdrawals must occur, the adverse impacts would be minimized by prescribing or constraining the timing, magnitude, and duration of withdrawal. The reverse quantification, well-suited for unregulated rivers such as the Yampa, strives to protect all flows minus extractions from daily flows based on three parameters: 1) a minimum flow, below which water diversion does not occur; 2) the percentage of the flow above the minimum that is diverted; 3) the maximum daily flow that is diverted. We apply 350 flow extraction scenarios, each defined by a unique set of parameters, to the 99 historic annual hydrographs of daily flows (water year (WY) 1922–2020), and to the more recent 20 years (WY 2001–2020). We also consider how hydrologic year type (wet to dry) influences the flow volume extracted and impact to the resource. Recognizing the seasonal differences in flow and ecological and geomorphic response, we divide each year into four distinct seasonal periods and use relations from the literature between flow, channel change, riparian vegetation and fish behavior, physiology, and habitat to define hydrograph and resource metrics used to evaluate impacts to the resource. While our analysis demonstrates that all withdrawals will damage the resource, extractions during the Early Runoff Period (March 15 – April 30) are least detrimental and extractions during the Summer Baseflow Period (July 16 – October 31) are most detrimental. We find that most aspects of the resource are more sensitive to increasing extractions during drier years than during wetter years. Recent decades have seen a shift towards more frequent drier years, resulting in less water in most periods. As a result, our analysis suggests that extractions in recent decades would have had a greater impact on the resource when compared to similar extractions during the full historical record. Finally, we demonstrate how the NPS may use these results to develop limits on extractions for resource protection.
","language":"English","publisher":"National Park Service","doi":"10.36967/2305338","usgsCitation":"Diehl, R., and Friedman, J.M., 2024, Modelling effects of flow withdrawal scenarios on riverine and riparian features of the Yampa River in Dinosaur National Monument: Science Report NPS/SR-2024-178, ix, 61 p., https://doi.org/10.36967/2305338.","productDescription":"ix, 61 p.","ipdsId":"IP-147809","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":433500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Dinosaur National Monument, Yampa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.34123049693821,\n 40.54854333761875\n ],\n [\n -109.34123049693821,\n 40.4021164901732\n ],\n [\n -108.48839549264547,\n 40.4021164901732\n ],\n [\n -108.48839549264547,\n 40.54854333761875\n ],\n [\n -109.34123049693821,\n 40.54854333761875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diehl, Rebecca","contributorId":343881,"corporation":false,"usgs":false,"family":"Diehl","given":"Rebecca","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":912266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":912267,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70262022,"text":"70262022 - 2024 - Water-level changes impact angler effort in a large lake: Implications for climate change","interactions":[],"lastModifiedDate":"2025-01-10T17:36:46.969292","indexId":"70262022","displayToPublicDate":"2024-08-28T11:25:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Water-level changes impact angler effort in a large lake: Implications for climate change","docAbstract":"Climate change is expected to influence aquatic habitats and associated fish populations, yet we know little about the impact on recreational anglers. Our goal was to explore whether interannual fluctuations in waterbody surface area and other explanatory variables could be used as indicators of changes in angler fishing effort. Our approach leveraged a combination of remotely sensed waterbody surface area, environmental and fish population data, and onsite angler survey monitoring data for Devils Lake, North Dakota, USA during the open-water fishing period (May 1st to August 31st) for 9 years (1992–2021). The information was used to develop a dynamic waterbody size-angler effort model. Changes in waterbody surface area reliably predicted changes in angler effort (r2 = 0.60). Increases in waterbody surface area led to increases in angler effort, and decreases in waterbody surface area led to decreases in angler effort. Our findings show promise that remotely sensed fluctuations in waterbody surface area could be used as an indicator of interannual angler effort dynamics. Dynamic waterbody size-angler effort models could provide managers the ability to predict changes in angler effort via climate-related hydrological cycles that affect the size and distribution of waterbodies on the landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2024.107156","usgsCitation":"Maldonado, M., Mahmood, T., Coulter, D., Coulter, A., Chipps, S.R., Siller, M., Neal, M., Saha, A., and Kaemingk, M., 2024, Water-level changes impact angler effort in a large lake: Implications for climate change: Fisheries Research, v. 279, 107156, 5 p., https://doi.org/10.1016/j.fishres.2024.107156.","productDescription":"107156, 5 p.","ipdsId":"IP-160734","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466947,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.fishres.2024.107156","text":"Publisher Index Page"},{"id":466011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Devils Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.36050675879237,\n 48.39597416139583\n ],\n [\n -99.36050675879237,\n 47.77201003721444\n ],\n [\n -98.24927832713699,\n 47.77201003721444\n ],\n [\n -98.24927832713699,\n 48.39597416139583\n ],\n [\n -99.36050675879237,\n 48.39597416139583\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maldonado, Matthew L.","contributorId":347887,"corporation":false,"usgs":false,"family":"Maldonado","given":"Matthew L.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922731,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahmood, Taufique H.","contributorId":347888,"corporation":false,"usgs":false,"family":"Mahmood","given":"Taufique H.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922732,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coulter, David P.","contributorId":347889,"corporation":false,"usgs":false,"family":"Coulter","given":"David P.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":922733,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coulter, Alison A.","contributorId":347890,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":922734,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922735,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Siller, Maddy K.","contributorId":347891,"corporation":false,"usgs":false,"family":"Siller","given":"Maddy K.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":922736,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neal, Michaela L.","contributorId":347892,"corporation":false,"usgs":false,"family":"Neal","given":"Michaela L.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922737,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saha, Ayon","contributorId":347893,"corporation":false,"usgs":false,"family":"Saha","given":"Ayon","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922738,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kaemingk, Mark A.","contributorId":347895,"corporation":false,"usgs":false,"family":"Kaemingk","given":"Mark A.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":922739,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257851,"text":"70257851 - 2024 - Will there be water? Climate change, housing needs, and future water demand in California","interactions":[],"lastModifiedDate":"2024-08-29T11:46:31.786221","indexId":"70257851","displayToPublicDate":"2024-08-28T06:45:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Will there be water? Climate change, housing needs, and future water demand in California","docAbstract":"Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.