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At least 307 records · Page 17Linked to original sources
USGS2013

Effectiveness of an integrated hatchery program: Can genetic-based performance differences between hatchery and wild Chinook salmon be avoided?

Performance of wild (W) and hatchery (H) spring Chinook salmon ( Oncorhynchus tshawytscha ) was evaluated for a sixth generation hatchery program. Management techniques to minimize genetic divergence from the wild stock included regular use of wild broodstock and volitional releases of juveniles. Performance of HH, WW, and HW (hatchery female spawned with wild male) crosses was compared in hatchery and stream environments. The WW juveniles emigrated from the hatchery at two to three times the rate of HH fish in the fall (HW intermediate) and 35% more HH than WW adults returned (27% more HW than WW adults). Performance in the stream did not differ statistically between HH and WW fish, but outmigrants (38% WW, 30% HW, and 32% HH fish) during the first 39 days of the 16-month sampling period composed 74% of total outmigrants. Differences among hatchery-reared crosses were partially due to additive genetic effects, were consistent with domestication (increased fitness for the hatchery population in the hatchery program), and suggested that selection against fall emigration from the hatchery was a possible mechanism of domestication.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2014

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these downscaled datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.

Open-File Report↗
USGS2025

One-hundred fundamental, open questions to integrate methodological approaches in lake ice research

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real-time in situ monitoring, remote sensing, and modeling. The recent progress in low-power in situ sensing and data telemetry allows continuous tracing of under-ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process-based or statistical data-driven), with the recent technological advancements and integration of machine learning and hybrid process-based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less-studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross-pollination of intensive and extensive modes of study to broaden knowledge of the winter period.

Water Resources Research↗
USGS1996

Occurrence of the gasoline oxygenate MTBE and BTEX compounds in urban stormwater in the United States, 1991-95

Methyl tert -butyl ether (MTBE) is a gasoline oxygenate. Oxygenates such as MTBE, when added to gasoline, increase the gasoline's oxygen level and decrease vehicular carbon monoxide emissions and ozone levels in the atmosphere. MTBE disperses rapidly in water, was the second most frequently detected volatile organic compound (VOC) in a study of shallow urban ground water, and is less biodegradable than common gasoline compounds, such as benzene, toluene, ethylbenzene, and total xylene (BTEX). Urban Stormwater is a possible source of MTBE found in shallow ground water. The U.S. Geological Survey (USGS) sampled stormwater in 16 cities and metropolitan areas that are required to obtain permits to discharge Stormwater from their municipal storm-sewer system into surface water. Concentrations of 62 VOCs, including MTBE and BTEX compounds, were measured in 592 Stormwater samples collected in these cities and metropolitan areas from 1991 through 1995. Concentration data for MTBE and BTEX compounds in stormwater were compiled and analyzed, and the findings are summarized in this report. This effort was part of an interagency assessment of the scientific basis and effectiveness of the Nation's oxygenated fuel program and was coordinated by the Office of Science and Technology Policy, Executive Office of the President. MTBE was the seventh most frequently detected VOC in urban Stormwater, following toluene, total xylene, chloroform, total trimethylbenzene, tetrachloroethene, and naphthalene. MTBE was detected in 6.9 percent (41 of 592) of Stormwater samples collected. When detected, concentrations of MTBE ranged from 0.2 to 8.7 micrograms per liter (µg/L), with a median of 1.5 µg/L. All detections of MTBE were less than the lower limit of the U.S. Environmental Protection Agency (EPA) draft lifetime health advisory (20 µg/L) for drinking water. Eighty-three percent of all detections of MTBE in Stormwater were in samples collected during the October through March season of each year (1991-95), which corresponds with the expected seasonal use of oxygenated gasoline in areas where carbon monoxide exceeds established air-quality standards. The median concentration of MTBE and benzene for all samples was statistically different and higher in samples collected during the October through March season than samples collected during the April through September season. Sixty-six percent of all MTBE detections occurred with BTEX compounds, and a proportionate increase in concentrations was found when these compounds occurred together. The proportionate increase could indicate a common source of MTBE and BTEX for those samples. Toluene and total xylene were the most frequently detected BTEX compounds and the most frequently detected VOCs in these investigations. Detected concentrations of toluene and total xylene ranged from 0.2 to 6.6 µg/L and 0.2 to 15 µg/L with median concentrations of 0.3 and 0.4 iig/L, respectively

Water-Resources Investigations Report↗
USGS2012

Social values for ecosystem services (SolVES): Documentation and user manual, version 2.0

In response to the need for incorporating quantified and spatially explicit measures of social values into ecosystem services assessments, the Rocky Mountain Geographic Science Center (RMGSC), in collaboration with Colorado State University, developed a geographic information system (GIS) application, Social Values for Ecosystem Services (SolVES). With version 2.0 (SolVES 2.0), RMGSC has improved and extended the functionality of SolVES, which was designed to assess, map, and quantify the perceived social values of ecosystem services. Social values such as aesthetics, biodiversity, and recreation can be evaluated for various stakeholder groups as distinguished by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with the previous version, SolVES 2.0 derives a quantitative, 10-point, social-values metric, the Value Index, from a combination of spatial and nonspatial responses to public attitude and preference surveys and calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. Additionally, SolVES 2.0 integrates Maxent maximum entropy modeling software to generate more complete social value maps and to produce robust statistical models describing the relationship between the social values maps and explanatory environmental variables. The performance of these models can be evaluated for a primary study area, as well as for similar areas where primary survey data are not available but where social value mapping could potentially be completed using value-transfer methodology. SolVES 2.0 also introduces the flexibility for users to define their own social values and public uses, model any number and type of environmental variable, and modify the spatial resolution of analysis. With these enhancements, SolVES 2.0 provides an improved public domain tool for decisionmakers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among different ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

Open-File Report↗
USGS2003

Effect of different sampling schemes on the spatial placement of conservation reserves in Utah, USA

We evaluated the effect of three different sampling schemes used to organize spatially explicit biological information had on the spatial placement of conservation reserves in Utah, USA. The three sampling schemes consisted of a hexagon representation developed by the EPA/EMAP program (statistical basis), watershed boundaries (ecological), and the current county boundaries of Utah (socio-political). Four decision criteria were used to estimate effects, including amount of area, length of edge, lowest number of contiguous reserves, and greatest number of terrestrial vertebrate species covered. A fifth evaluation criterion was the effect each sampling scheme had on the ability of the modeled conservation reserves to cover the six major ecoregions found in Utah. Of the three sampling schemes, county boundaries covered the greatest number of species, but also created the longest length of edge and greatest number of reserves. Watersheds maximized species coverage using the least amount of area. Hexagons and watersheds provide the least amount of edge and fewest number of reserves. Although there were differences in area, edge and number of reserves among the sampling schemes, all three schemes covered all the major ecoregions in Utah and their inclusive biodiversity. ?? 2003 Elsevier Science Ltd. All rights reserved.

Biological Conservation↗
USGS2014

Preliminary testing of flow-ecology hypotheses developed for the GCP LCC region

The Ecological Limits of Hydrological Alteration (ELOHA) framework calls for the development of flow-ecology hypotheses to support protection of the flow regime from ecologically harmful alteration due to human activities. As part of a larger instream flow project for the Gulf Coast Prairie Landscape Conservation Cooperative (GCP LCC), regional flow-ecology hypotheses were developed for fish, mussels, birds, and riparian vegetation (Davis and Brewer 20141 ). The objective of this study was to assess the usefulness of existing ecological and hydrological data to test these hypotheses or others that may be developed in the future. Several databases related to biological collections and hydrologic data from Oklahoma, Texas, and Louisiana were compiled. State fish-community data from Oklahoma and Louisiana were summarized and paired with existing USGS gage data having at least a 40-year period of record that could be separated into reference and current conditions for comparison. The objective of this study was not to conduct exhaustive analyses of these data, the hypotheses, or analyses interpretation, but rather to use these data to determine if existing data were adequate to statistically test the regional flow-ecology hypotheses. The regional flow-ecology hypotheses were developed for the GCP LCC by a committee chaired by Shannon Brewer and Mary Davis (Davis and Brewer 2014). Existing data were useful for informing the hypotheses and suggest support for some hypotheses, but also highlight the need for additional testing and development as some results contradicted hypotheses. Results presented here suggest existing data are adequate to support some flow-ecology hypotheses; however, lack of sampling effort reported with the fish collections and the need for ecoregion-specific analyses suggest more data would be beneficial to analyses in some ecoregions. Additional fish sampling data from Texas and Louisiana will be available for future analyses and may ameliorate some of the data concerns and improve hypothesis interpretation. If the regional hydrologic model currently under development by the U.S. Geological Survey for the South-Central Climate Science Center is improved to produce daily hydrographs, it will enable use of fish data at ungaged locations. In future efforts, exhaustive analyses using these data, in addition to the development of more complex multivariate hypotheses, would be beneficial to understanding data gaps, particularly as relevant to species of conservation concern.

Cooperator Science Series↗
USGS2024

Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition

In stream systems, disentangling relationships between biology and flow and subsequent prediction of these relationships to unsampled streams is a common objective of large-scale ecological modeling. Often, streamflow metrics are derived from aggregating continuous streamflow records available at a subset of stream gages into long-term flow regime descriptors. Despite demonstrated value, shortcomings of these long-term approaches include spatial restriction to locations with long-term continuous flow records (commonly, biased toward larger systems) and omission of potentially ecologically important short-term (i.e., ≤1 year) antecedent streamflow information. We used long-term flow regime and short-term antecedent streamflow alteration information to evaluate relative performance in modeling stream fish biological condition. We compared results to understand whether short-term antecedent streamflow information improved models of fish biological condition. Results indicated that models incorporating short-term antecedent data performed better than those relying solely on long-term flow regime data (kappa statistic = 0.29 and 0.23, respectively) and improved prediction accuracy among stream sizes and in six of nine ecoregions. Additionally, models relying solely on short-term streamflow information performed similarly to those with only long-term streamflow information (kappa = 0.23). Incorporating short-term antecedent streamflow metrics may provide added ecological information not fully captured by long-term flow regime summaries in macroscale modeling efforts or perform similarly to long-term streamflow data when long-term data are not available.

Science of the Total Environment↗
USGS2024

Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022

Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global-scale spatiotemporal change detection of surface water has significantly improved in recent years due to planetary-scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38 years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas with intermittent seasonal coverage have increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman's rank correlations (rho) above 0.05 and p values less than 0.05. The seasonal river extent is nearly 32 % larger than the previously observed annual mean river extent, suggesting large seasonal variations that impact not only ecosystem health but also estimations of terrestrial biogeochemical cycles of carbon. The outcomes of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, serving as a valuable resource for monitoring and research of hydrological cycles, ecosystem accounting, and water management.

Hydrology and Earth System Sciences↗
USGS1998

Lake bed classification using acoustic data

As part of our effort to identify the lake bed surficial substrates using remote sensing data, this work designs pattern classifiers by multivariate statistical methods. Probability distribution of the preprocessed acoustic signal is analyzed first. A confidence region approach is then adopted to improve the design of the existing classifier. A technique for further isolation is proposed which minimizes the expected loss from misclassification. The devices constructed are applicable for real-time lake bed categorization. A mimimax approach is suggested to treat more general cases where the a priori probability distribution of the substrate types is unknown. Comparison of the suggested methods with the traditional likelihood ratio tests is discussed.

Applied Mathematics and Computer Science↗
USGS2019

Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate

To support Levee Ready Columbia’s (LRC’s) effort to re-certify levees along the Columbia and Willamette Rivers and remain accredited, two 2-dimensional hydraulic models, Adaptive Hydraulics and Delft3D-Flexible Mesh, were used to simulate the effects of plausible extreme high water during the 2030 to 2059 period. The Columbia River was simulated from Bonneville Dam, situated at river mile (RM) 145, to the mouth of Columbia River, and the Willamette River was simulated from Willamette Falls, RM 26.2, to the Columbia River confluence. Inputs to the models included light detection and ranging (lidar) and bathymetric mapping data to determine bed level, and boundary conditions in the form of daily inflow hydrographs and water levels in the ocean offshore of the mouth of the Columbia River. Future conditions were based on climate science data developed by the U.S. Army Corps of Engineers and others. These conditions included future streamflow and coastal ocean water levels. The hypothetical, extreme but plausible, upstream boundary was based on scaling up the hydrographs from the 1996 flood. Scaling factors were determined by comparing the peak flow rankings determined from flood frequency analyses of historical unregulated periods and 2040s simulated unregulated winter streamflow. The comparison resulted in scaling up the Columbia River hydrograph by 40-percent and scaling up the Willamette River and Lower Columbia River tributaries hydrographs by 20-percent. The downstream ocean boundary was based on a combination of sea-level change, high tide, and storm surge. The models were calibrated for two historical periods: (1) from January 15 to February 28, 1996, and (2) from April 12 to July 12, 1997. The two models compared well to the measured water-surface elevation over the historical periods and had good performance statistics, with root-mean square error ranging from 0.085 to 0.32 meters, Nash-Sutcliffe values greater than 0.96, and bias ranging from -0.03 to 0.28 meters. The simulated peak stage in the Columbia River at Vancouver, Washington, for 1996 was 9.60 and 9.98 meters (31.5 and 32.7 feet) compared to the measured peak of 9.89 meters (32.5 feet). Future peak stage then was simulated with boundary conditions representing extreme but plausible future conditions at the inflow sites and the ocean boundary. The two calibrated models compared well in their simulations of extreme but plausible future conditions. For the 0-meter sea-level change scenario, the simulated peak stage in the Columbia River at Vancouver was 11.15 and 11.39 meters (36.6 and 37.4 feet); and for the 1-meter sea-level change scenario, the simulated peak stage in the Columbia River was 11.25 and 11.54 meters (36.9 and 37.9 feet). The total increase in stage as compared to the 1996 measured peak stage ranged from 1.26 to 1.65 meters (4.13 to 5.40 feet).

Oregon↗
USGS1990

Fire - Southern Oscillation relations in the southwestern United States

Fire scar and tree growth chronologies (1700 to 1905) and fire statistics (since 1905) from Arizona and New Mexico show that small areas burn after wet springs associated with the low phase of the Southern Oscillation (SO), whereas large areas burn after dry springs associated with the high phase of the SO. Through its synergistic influence on spring weather and fuel conditions, climatic variability in the tropical Pacific significantly influences vegetation dynamics in the southwestern United States. Synchrony of fire-free and severe fire years across diverse southwestern forests implies that climate forces fire regimes on a subcontinental scale; it also underscores the importance of exogenous factors in ecosystem dynamics.

Arizona, New Mexico↗
USGS2016

Effects of stream-adjacent logging in fishless headwaters on downstream coastal cutthroat trout

To investigate effects of headwater logging on downstream coastal cutthroat trout ( Oncorhynchus clarkii clarkii ) populations, we monitored stream habitat and biotic indicators including biomass, abundance, growth, movement, and survival over 8 years using a paired-watershed approach. Reference and logged catchments were located on private industrial forestland on ∼60-year harvest rotation. Five clearcuts (14% of the logged catchment area) were adjacent to fishless portions of the headwater streams, and contemporary regulations did not require riparian forest buffers in the treatment catchment. Logging did not have significant negative effects on downstream coastal cutthroat trout populations for the duration of the sample period. Indeed, the only statistically significant response of fish populations following logging in fishless headwaters was an increase in late-summer biomass (g·m −2 ) of age-1+ coastal cutthroat trout in tributaries. Ultimately, the ability to make broad generalizations concerning effects of timber harvest is difficult because response to disturbance (anthropogenically influenced or not) in aquatic systems is complex and context-dependent, but our findings provide one example of environmentally compatible commercial logging in a regenerated forest setting.

Oregon↗
USGS2010

Soil-test N recommendations augmented with PEST-optimized RZWQM simulations

Improved understanding of year-to-year late-spring soil nitrate test (LSNT) variability could help make it more attractive to producers. We test the ability of the Root Zone Water Quality Model (RZWQM) to simulate watershed-scale variability due to the LSNT, and we use the optimized model to simulate long-term field N dynamics under related conditions. Autoregressive techniques and the automatic parameter calibration program PEST were used to show that RZWQM simulates significantly lower nitrate concentration in discharge from LSNT treatments compared with areas receiving fall N fertilizer applications within the tile-drained Walnut Creek, Iowa, watershed (>5 mg N L-1 difference for the third year of the treatment, 1999). This result is similar to field-measured data from a paired watershed experiment. A statistical model we developed using RZWQM simulations from 1970 to 2005 shows that early-season precipitation and early-season temperature account for 90% of the interannual variation in LSNT-based fertilizer N rates. Long-term simulations with similar average N application rates for corn (Zea mays L.) (151 kg N ha-1) show annual average N loss in tile flow of 20.4, 22.2, and 27.3 kg N ha -1 for LSNT, single spring, and single fall N applications. These results suggest that (i) RZWQM is a promising tool to accurately estimate the water quality effects of LSNT; (ii) the majority of N loss difference between LSNT and fall applications is because more N remains in the root zone for crop uptake; and (iii) year-to-year LSNT-based N rate differences are mainly due to variation in early-season precipitation and temperature. Copyright ?? 2010 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Journal of Environmental Quality↗
USGS1967

Potassium-argon ages of recent rhyolites of the Mono and Inyo craters, California

Twenty-two KAr ages were determined for sanidine samples from 10 rhyolite domes of the Mono and Inyo Craters to test the applicability of KAr dating to volcanic rocks of Recent age. Comparison of the results with ‘blank’ and dosed analyses shows that radiogenic 40 Ar was detected and was measured to within a factor of two or better. The estimated standard deviation of precision is 12% for analyses containing 5% or more radiogenic 40 Ar. A statistical analysis suggests that real differences in apparent age were detected between three of the analyzed domes. The ages, which range from 6400 to 10,200 years for experiments with 5% or more radiogenic 40 Ar, are, in general, consistent with ionium ages on 5 of the same samples and with glacial and 14 C evidence of age. They suggest that most of the Mono Craters volcanos are on the order of 10,000 years old or less. The results also suggest that the problem of excess 40 Ar may not be severe for KAr dating of volcanic rocks.

Earth and Planetary Science Letters↗
USGS2015

Return to normal streamflows and water levels: Summary of hydrologic conditions in Georgia, 2013

The U.S. Geological Survey (USGS) South Atlantic Water Science Center (SAWSC) Georgia office, in cooperation with local, State, and other Federal agencies, maintains a long-term hydrologic monitoring network of more than 340 real-time continuous-record streamflow-gaging stations (streamgages), including 10 real-time lake-level monitoring stations, 67 real-time surface-water-quality monitors, and several water-quality sampling programs. Additionally, the SAWSC Georgia office operates more than 180 groundwater monitoring wells, 39 of which are real-time. The wide-ranging coverage of streamflow, reservoir, and groundwater monitoring sites allows for a comprehensive view of hydrologic conditions across the State. One of the many benefits of this monitoring network is that the analyses of the data provide a spatially distributed overview of the hydrologic conditions of creeks, rivers, reservoirs, and aquifers in Georgia. Streamflow and groundwater data are verified throughout the year by USGS hydrographers. Hydrologic conditions are determined by comparing the results of statistical analyses of the data collected during the current water year (WY) to historical data collected over the period of record. Changing hydrologic conditions emphasize the need for accurate, timely data to help Federal, State, and local officials make informed decisions regarding the management and conservation of Georgia’s water resources for agricultural, recreational, ecological, and water-supply needs and for use in protecting life and property. Drought conditions, persistent in the area since 2010, continued into the 2013 WY. In February 2013, Georgia was free of extreme (D3) drought conditions, as defined by the U.S. Drought Monitor, for the first time since August 2010 due to extended periods of heavy rainfall (U.S. Drought Monitor, 2013). According to the Office of the State Climatologist, the city of Savannah recorded 9.75 inches of rain in February 2013, the highest monthly total in February out of 143 years of record. Macon and Columbus also received record rainfalls in February 2013. Above-normal precipitation continued in June 2013, and the cities of Augusta and Savannah recorded the wettest June on record. In July, precipitation for the entire State of Georgia was 3.53 inches above normal (Dunkley, 2013). Above-normal rainfall from February to September 2013 increased streamflow and raised groundwater levels, and lakes and reservoirs were raised to full-pool elevations.

Georgia↗
USGS2023

Hydrologic compartmentalization and analytic-element groundwater-flow simulations for a draining mine tunnel

Draining mine tunnels contribute contaminants to groundwater and surface water, but remediation strategies may be hindered as hydrogeologic characterization and modeling of these heterogeneous features generally relies on sparse data sets. The Captain Jack mine site in Colorado, USA, presents a unique data set allowing for temporal evaluation of groundwater connectivity in the vicinity of an abandoned mine, where a hydraulic bulkhead is impounding water within the mine workings. This study applied statistical analysis of system pressure responses to bulkheading and used an analytic-element modeling approach to characterize heterogeneity and groundwater flow. Groundwater-level elevation data collected over a period of 4 years, both prior to and after bulkheading, indicate that the mine workings act as a sink to the local groundwater system. Despite groundwater flow being generally oriented towards the mine workings, there are also large vertical and horizontal hydraulic gradients which persist through time. Although the groundwater system is highly compartmentalized, statistical analysis using Kendall’s Tau indicates correlations between hydraulic head changes in the mine workings and several wells completed in crystalline bedrock, indicating the influence of fracture flow. An analytic-element model was parameterized to account for uncertainty in hydraulic conductivity, recharge, and discharge. Model results reproduced the range of observed hydraulic heads in the mine workings and adjacent igneous dikes but failed to closely simulate hydraulic heads in several wells located distal from the mine workings in granitic bedrock. The modeling approach shows potential promise, however, for conducting preliminary modeling to guide data collection at other similar mine sites.

Colorado↗
USGS2025

Machine learning provides reconnaissance-type estimates of carbon dioxide storage resources in oil and gas reservoirs

Oil and gas reservoirs represent suitable containers to sequester carbon dioxide (CO 2 ) in a supercritical state because they are accessible, reservoir properties are known, and they previously contained stored buoyant fluids. However, planners must quantify the relative magnitude of the CO 2 storage resource in these reservoirs to formulate a comprehensive strategy for CO 2 mitigation. Even reconnaissance-type estimates of CO 2 storage resources of known oil and gas reservoirs may require complicated calculations involving 1) estimates of recoverable oil and gas, 2) reservoir properties (depth, temperature, pressure, etc.), and 3) the physical qualities of the retained fluids. We demonstrate the application of machine learning (ML) algorithms to bypass these computations to yield more rapid estimates of CO 2 storage resources in reservoirs capable of hosting CO 2 in a supercritical state. ML algorithms are computationally efficient because they do not impose the strong assumptions on the data-generating process that standard statistical or engineering procedures require. Further, ML algorithms can capture highly complex, particularly nonlinear, relationships among predictor variables. We demonstrate the application of four different ML algorithms using data from onshore and offshore oil and gas reservoirs in Europe, and show they perform well when predictions are compared to engineering estimates. The proposed methods and models provide an effective and novel way to more rapidly and directly determine the subsurface CO 2 storage capacity of oil and gas reservoirs around the world, information that operators, researchers, and policymakers alike require to meet energy transition and decarbonization goals.

Frontiers in Enviornmental Science↗