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No abstract available.
We develop a partially nonergodic ground-motion model (GMM) for Fourier amplitude spectra for the San Francisco Bay Area, California, USA, using the Bayless and Abrahamson (2019) GMM as a reference ergodic GMM and developing location-dependent adjustments to the predicted median and variance. We compile regional ground-motion data from moment magnitude (𝑀 w ) >3 earthquakes occurring during 2000–2022 for which magnitude information is available in the U.S. Geological Survey Comprehensive Catalog (Guy et al., 2015). The data set predominantly consists of records from 𝑀 w 3.5–4.5 earthquakes but includes three well-recorded 𝑀 w > 5 events. Ground-motion residuals are evaluated using the time-averaged shear-wave velocity in the top 30 m (𝑉 S30 ) from the California-specific map of Thompson et al. (2018) and basin-depth site parameters from the seismic velocity model of Aagaard and Hirakawa (2021). The 𝑉 S30 dependence and basin-depth scaling of the reference ergodic GMM of Bayless and Abrahamson (2019) are evaluated and modified with the updated data set. We compute maps of site adjustments using a varying-coefficient model that considers the spatial correlation structure and uncertainties at each observation location. The spatial covariance model is developed using ground-motion residuals that are standardized by the uncertainty model, which allows for consideration of the aleatory variability in developing the site adjustments. The covariance model is fit considering the means and standard deviations of the site terms at all locations. The use of partially nonergodic median adjustments results in modified variance components of the within-event variability. Due to the low number of large-magnitude earthquakes that control seismic hazard in the data set, we do not modify between-event variance; however, we present adjustments to site-to-site variability for use in partially nonergodic hazard assessments.
Efficient operation of streamflow monitoring networks requires investments in technology and labor that provide the greatest benefits from available resources. Economic analyses comparing the costs and benefits from different types of alternatives for monitoring have not been practical to implement. Streamflow information provides a generic measure of benefits that can be incorporated into operational decisions as an objective for monitoring networks. A methodology for comparing how accuracy, monitoring period, and monitoring instead of modeling affects streamflow information is developed from information-theoretic approaches for network design but contributes three novel features: (1) a probability-difference model for conditional probability of monotonically paired variables, (2) explicit discounting of unverified information that may exceed the accuracy of streamflow records, and (3) run analysis to account for non-stationarity in streamflow probabilities. Application of the methodology to the U.S. Geological Survey streamflow monitoring network indicates the value of monitoring period to reduce the uncertainty of streamflow probabilities and, thus, increase streamflow information. The methodology has important limitations, particularly for sites with non-perennial streamflow, but demonstrates that probability difference could be used to evaluate operational alternatives to increase the efficiency of monitoring networks.
We develop the ground-motion characterization (GMC) for the 2025 U.S. National Seismic Hazard Model for Puerto Rico and the U.S. Virgin Islands (NSHM-PRVI) for earthquakes in active crustal, subduction interface, and subduction intraslab regimes. Using ground-motion models (GMMs) from the Next-Generation Attenuation (NGA)-West2 and NGA-Subduction projects, the GMC is parameterized by scaled-backbone models for median ground motions and by independent logic trees of aleatory variability. We introduce several novel GMC features into the U.S. NSHM: (1) use of regional ground-motion data for modeling median ground motions; (2) development of scaled-backbone models for median ground motions; and (3) development of independent logic trees of aleatory variability from variance components of GMMs and computed from multiple ground-motion datasets, and incorporating regional ground-motion data effects on variability. We compute probabilistic seismic hazard curves and maps to evaluate the contributions from the GMC components and for comparison with the 2003 GMC. Contributions of the GMC to epistemic uncertainty in seismic hazard are evaluated through spatial variations in epistemic uncertainty in hazard maps, comparisons between mean hazard curves and fractiles, as well as investigations of the contributions of logic-tree branches to hazard maps and curves. Comparisons between seismic hazard from the 2025 and 2003 GMCs allow examination of the changes in hazard curves and mapped values with 2% and 10% probabilities of exceedance in 50 years. The 2025 GMC exhibits modest changes in median predictions, relative to the 2003 GMC; overall, values of aleatory variability are higher, except at long periods ( ), resulting in changes to probabilistic ground motions at low probabilities of exceedance (<10% probability of exceedance in 50 years). Changes in hazard at 2% and 10% probabilities of exceedance in 50 years are also relatively modest (within 20%) at most sites, with the impacts from the 2025 GMC exhibiting minor reductions and increases at 0.2 and 1.0 s periods, respectively, relative to the 2003 GMC.
Potential-field geophysical data such as gravity can enhance understanding of geothermal resources at all stages of the resource life cycle, including assessment, exploration, development, and monitoring, and at multiple scales, from the reservoir scale to regional scale. However, to make gravity data useful for geothermal resource characterization, several processing steps are required to isolate the effects of density variations in the Earth’s crust to enable the identification of structural features associated with geothermal resources. Although this process is well-established, standard computational implementations for processing gravity data that are FAIR (Findable, Accessible, Interoperable, and Reproduceable) are still lacking. This paper details ongoing efforts at the U.S. Geological Survey (USGS) to develop a standard set of open-source Python tools for gravity data reduction that align with the FAIR principles. This workflow makes use of existing open-source tools for geophysical data processing with the goal of maximizing opportunities for rapid improvements, interoperability, and adaptability to other types of geophysical data.
No abstract available.
We present a simple method to estimate the risk of experiencing strong shaking from aftershocks during entry into earthquake-damaged buildings. We compute wait times until the probability of strong ground shaking from aftershocks reaches a predefined risk threshold; for example, a 0.4 percent probability of experiencing Modified Mercalli Intensity 7 or greater shaking during the planned building entry. We also develop a relation between aftershock probability and the probability of strong shaking, so that users can reference the U.S. Geological Survey aftershock forecast during an ongoing aftershock sequence to determine if the risk threshold has been met. We apply our method to active continental regions (for example, the Western United States), stable continental regions (for example, the Central and Eastern United States), and subduction zones (for example, Cascadia or Alaska).
Diagnostic laboratories receive carcasses and samples for diagnostic evaluation and pathogen/toxin detection. Case definitions bring clarity and consistency to the evaluation process. Their use within and between organizations allows more uniform reporting of diseases and etiologic agents. The intent of a case definition is to provide scientifically based criteria for determining: (a) if an individual carcass has a specific disease and degree of confidence in that diagnosis and (b) if there is evidence of a pathogen or toxin in a carcass or sample (for example, swab, tissue sample, skin scraping, blood/serum sample, environmental sample, or other). This case definition is specific to aspergillosis and applies to all avian species.
We introduce pySATSI, a Python package for computing earthquake focal mechanism stress inversions. This algorithm can handle a wide variety of types of stress inversion problems with a single script and can duplicate many capabilities of preceding methodologies. We also add new capabilities that include spatiotemporally variable inversion grids, damped stress estimates for clusters with few or no focal mechanisms, and variable fault‐plane ambiguities that the user can assign to individual events. In addition, we added the ability to use damped stress inversions with fault‐plane ambiguity probabilities that are weighted by fault instabilities. Our algorithm is computationally efficient with faster runtimes than previous algorithms, scales well for large datasets, and can be easily parallelized.
Demand for freshwater in the State of Hawaiʻi is expected to increase by roughly 13 percent from 2020 to 2035. Groundwater availability in Hawaiʻi is affected by a number of factors, including land cover, rainfall, runoff, evapotranspiration, and climate change. To evaluate the availability of fresh groundwater under projected future-climate conditions, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of groundwater recharge for Kauaʻi, Oʻahu, Molokaʻi, Lānaʻi, Maui, and the Island of Hawaiʻi for recent climate conditions and three future-climate scenarios. Climate conditions from 1978 to 2007 were used as the reference period for recent climate conditions on each island. The three future-climate scenarios were developed using available high-resolution downscaled climate projections that include (1) a mid-century scenario using projected rainfall conditions for the Representative Concentration Pathway (RCP) scenario during 2041–71 with a total radiative forcing of 8.5 watts per square meter by the year 2100 (RCP8.5 2041–71 scenario), (2) a dry-climate scenario using projected rainfall conditions for the RCP8.5 scenario during 2071–99, and (3) a wet-climate scenario using projected rainfall conditions for the “Special Report on Emissions Scenarios” A1B scenario during 2080–99 for Maui, the RCP4.5 scenario during 2080–99 for Kauaʻi, Lānaʻi, and the Island of Hawaiʻi, and the RCP8.5 scenario during 2080–99 for Oʻahu and Molokaʻi. An additional drought scenario was added for Lānaʻi to assess the effect of extreme drought conditions during 2008–12 on groundwater recharge. All scenarios used 2020 land cover. Mean annual groundwater recharge is estimated to decrease between 5 and 55 percent on all six islands in this study for the mid-century and dry-climate scenarios relative to the reference-period recharge. Recharge is estimated to increase for Kauaʻi, Oʻahu, Molokaʻi, Lānaʻi, and Maui between 2 and 43 percent and decrease for the Island of Hawaiʻi by about 4 percent for the wet-climate scenario. Comparing the mid-century and dry-climate scenarios, all 110 aquifer systems (management areas defined by the State of Hawaiʻi Commission on Water Resource Management) from all six islands show similar direction in drying (104 aquifer systems) or wetting (6 aquifer systems) changes for recharge. However, among the three future scenarios, only 35 of 110 aquifer systems show similar direction in drying (30 aquifer systems) or wetting (5 aquifer systems) changes for recharge.
We use combinatorial optimization to find the optimal spatial distribution of random samples of earthquakes (≥6.5) that minimize the misfit in target slip rates for all faults in the northeast Caribbean, and we derive magnitude-frequency relationships with uncertainties for these faults. Slip rates for many faults are derived from GPS block models, not direct measurements, because of their underwater locations. Predicted recurrence rates for eastern Hispaniola and Puerto Rico Trench faults are 220-450 yr for M7 and 3-5 kyr for M8, with maximum feasible magnitude of M8.2. The most frequent earthquakes with magnitudes ≥7.0 are predicted on the large upper plate strike-slip faults, Enriquillo (EF) and Septentrional Fault, commensurate with the historical record. Calais et al. (2023) suggested that shortening in western Hispaniola is accommodated on the offshore Jérémie and onshore Malpasse faults north and south of EF, instead of on terrestrial faults in western Hispaniola and EF. Because of our system-modeling approach, such a configuration predicts less frequent earthquakes on EF and on western Hispaniola and Muertos convergent zones. Recurrence times of a few 100s yr for M6.7 earthquakes is predicted on the submerged faults in Mona Passage, and infrequent M>7 earthquakes are predicted on the Virgin Islands faults.
Techniques for management of drainage basins that use water budgets to balance available water resources with actual or anticipated water use require accurate and precise estimates of basin withdrawals, interbasin transfers of freshwater, unaccounted-for use, water use, consumptive use, inflow and infiltration, basin return flow, and interbasin transfers of wastewater. Frequently, interbasin transfers of freshwater and wastewater are not included in basin water budgets because they occur within public water-delivery and wastewater-collection systems. A new 10-step method was developed to improve estimates of inflow and infiltration and interbasin transfers using readily available statewide data. The accuracy and precision of water-use estimates determined by this method are improved through careful application of coefficients for small users and the use of metered values for large users. The method was developed and tested with data for the Ten Mile River Basin in southeastern Massachusetts. This report uses examples from the basin to illustrate each step of the method.
This report presents two techniques to estimate generalized skew coefficients used for log-Pearson Type III peak-streamflow frequency analysis of natural basins in Texas. A natural basin has less than 10 percent impervious cover, and less than 10 percent of its drainage area is controlled by reservoirs. The estimation of generalized skew coefficients is based on annual peak and historical peak streamflow for all U.S. Geological Survey streamflow-gaging stations having at least 20 years of annual peak-streamflow record from natural basins in Texas. Station skew coefficients calculated for each of 255 Texas stations were used to estimate generalized skew coefficients for Texas. One technique to estimate generalized skew coefficients involved the use of regression equations developed for each of eight regions in Texas, and the other involved development of a statewide map of generalized skew coefficients. The weighted mean of the weighted mean standard errors of the regression equations for the eight regions is 0.36 log 10 skew units, and the weighted mean standard error of the map is 0.35 log 10 skew units. The technique based on the map is preferred for estimating generalized skew coefficients because of its smooth transition from one region of the State to another.
The distribution functions for statistics that may be used to assess the significance of differences between sample means, standard deviations, coefficients of skewness, and coefficients of variation are obtained by Monte Carlo experiments. The distributions are expressed as functions of sample size, cross correlation, and skewness. In general, the distributions are more sensitive to cross correlation than to skewness. As sample size increases, however, the distributions tend to become more sensitive to skewness.
As part of the periodic update of the geothermal energy assessments for the USA (e.g., last update by Williams and others, 2008), a new three-dimensional temperature map has been constructed for the Great Basin, USA. Williams and DeAngelo (2011) identified uncertainty in estimates of conductive heat flow near land surface as the largest contributor to uncertainty in previously published temperature maps. The new temperature maps incorporate new conductive heat flow estimates developed by DeAngelo and others (2023). Predicted temperatures at depth are compared with representative measurements (for conductively dominated conditions), showing good agreement under relatively simple uniform conditions. Inputs included radiogenic heat production for all layers of 1.89 μW/m 3 , effective bulk thermal conductivity of 2.7 W/m/°C for all rocks underlying sedimentary basins, and a previously published (Williams and DeAngelo, 2011) empirically driven estimate of increasing thermal conductivity with depth in sedimentary sequences. The resulting three-dimensional temperature model is published in a USGS data release associated with this manuscript (Burns and others, 2023).
Freshwater is a vital natural resource. Although New York is a water-rich State, the wise and economical use of water resources is needed to ensure that there is enough water of adequate quality for both human and ecological needs—both for today and for tomorrow. Nowhere in New York is this more evident than in Nassau and Suffolk Counties on Long Island, where the public water supply is obtained from the sole-source aquifers located directly beneath the nearly 3 million people who live there. In 2023, in eastern Long Island’s Suffolk County, groundwater was pumped from these aquifers by more than 1,100 public water-supply wells to meet the needs of about 1.5 million people.
Methods are presented for estimating the peak discharges of rural, unregulated streams in Virginia. A Pearson Type III distribution is fitted to the logarithms of the unregulated annual peak-discharge records from 363 stream-gaging stations in Virginia to estimate the peak discharge at these stations for recurrence intervals of 2 to 500 years. Peak-discharge characteristics for 284 unregulated stations are divided into eight regions based on physiographic province, and regressed on basin characteristics, including drainage area, main channel length, main channel slope, mean basin elevation, percentage of forest cover, mean annual precipitation, and maximum rainfall intensity. Regression equations for each region are computed by use of the generalized least-squares method, which accounts for spatial and temporal correlation between nearby gaging stations. This regression technique weights the significance of each station to the regional equation based on the length of records collected at each cation, the correlation between annual peak discharges among the stations, and the standard deviation of the annual peak discharge for each station. Drainage area proved to be the only significant explanatory variable in four regions, while other regions have as many as three significant variables. Standard errors of the regression equations range from 30 to 80 percent. Alternate equations using drainage area only are provided for the five regions with more than one significant explanatory variable. Methods and sample computations are provided to estimate peak discharges at gaged and engaged sites in Virginia for recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, and to adjust the regression estimates for sites on gaged streams where nearby gaging-station records are available.
The minimum 7-day, 2-year discharge (7Q2) was estimated for each of 97 streamflow-gaging stations in the Brazos River Basin, Texas, with at least 10 years of unregulated or 10 years of regulated discharge data using the Pearson Type III distribution fitted by L-moment statistics of the annual minimum 7-day discharges. Forty-six stations have at least 10 years of unregulated discharge data only, 26 stations have at least 10 years of regulated discharge data only, and 25 stations have at least 10 years of unregulated and 10 years of regulated discharge data. Statistical tests were applied to the annual minimum 7-day discharges to determine if the unregulated and regulated data have trends; and to determine if the data before regulation are significantly different from the data after regulation. Results of the Mann-Kendall test indicate a significant trend at the 95-percent confidence level for 8 of the 46 unregulated-only stations, for 6 of the 26 regulated-only stations, and for 5 stations before regulation and for 10 stations after regulation of the 25 stations with both unregulated and regulated data. Results of the Wilcoxon rank-sum test indicate a significant difference between the unregulated and regulated annual minimum 7-day discharges at the 95-percent confidence level for 16 of the 25 stations with both types of data. The 7Q2 is zero for 31 of the 46 unregulated-only stations and for 9 of the 26 regulated-only stations. The 7Q2 also is zero for 9 stations before regulation and for 3 stations after regulation of the 25 stations with both unregulated and regulated data. The 7Q2 ranges from 33 to 631 cubic feet per second for the eight regulated-only stations on the mainstem Brazos River. For the seven stations on the mainstem Brazos River with at least 10 years of unregulated and 10 years of regulated discharge data, the 7Q2 ranges from 0 to 568 cubic feet per second before regulation and from 0.30 to 670 cubic feet per second after regulation. The 7Q2 increased during regulation for 19 of the 25 stations with both unregulated and regulated data. The effect of regulation for most stations generally was an increase in the magnitude of the 7Q2.