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Probabilistic assessment of postfire debris-flow inundation in response to forecast rainfall

Communities downstream of burned steep lands face increases in debris-flow hazards due to fire effects on soil and vegetation. Rapid postfire hazard assessments have traditionally focused on quantifying spatial variations in debris-flow likelihood and volume in response to design rainstorms. However, a methodology that provides estimates of debris-flow inundation downstream of burned areas based on forecast rainfall would provide decision-makers with information that directly addresses the potential for downstream impacts. We introduce a framework that integrates a 24 h lead-time ensemble precipitation forecast with debris-flow likelihood, volume, and runout models to produce probabilistic maps of debris-flow inundation. We applied this framework to simulate debris-flow inundation associated with the 9 January 2018 debris-flow event in Montecito, California, USA. When the observed debris-flow volumes were used to drive the probabilistic forecast model, analysis of the simulated inundation probabilities demonstrates that the model is both reliable and sharp. In the fully predictive model, however, in which debris-flow likelihood and volume were computed from the atmospheric model ensemble's predictions of peak 15 min rainfall intensity, I 15 , the model generally under-forecasted the inundation area. The observed peak I 15 lies in the upper tail of the atmospheric model ensemble spread; thus a large fraction of ensemble members forecast lower I 15 than observed. Using these I 15 values as input to the inundation model resulted in lower-than-observed flow volumes which translated into under-forecasting of the inundation area. Even so, approximately 94 % of the observed inundated area was forecast to have an inundation probability greater than 1 %, demonstrating that the observed extent of inundation was generally captured within the range of outcomes predicted by the model. Sensitivity analyses indicate that debris-flow volume and two parameters associated with debris-flow mobility exert significant influence on inundation predictions, but reducing uncertainty in postfire debris-flow volume predictions will have the largest impact on reducing inundation outcome uncertainty. This study represents a first step toward a near-real-time hazard assessment product that includes probabilistic estimates of debris-flow inundation and provides guidance for future improvements to this and similar model frameworks by identifying key sources of uncertainty.

California

Background and geologic model for the 2024 U.S. Geological Survey assessment of undiscovered conventional petroleum resources in the Norphlet Formation, U.S. Gulf Coast

The Upper Jurassic Norphlet Formation is a stratigraphic unit located in the subsurface of the United States Gulf coastal plain and offshore Gulf of America (Gulf of Mexico). The Norphlet consists of clastic sediments derived from the southern Appalachian highlands, and was deposited in a continental dryland setting on the margins of the early Gulf of America rift basin. The formation contains a variety of sedimentary facies, including facies representing updip alluvial fans, wadis, red beds, and a widespread erg, or eolian sand sea. The erg facies form the primary reservoirs from which hydrocarbons sourced from the overlying Smackover Formation are produced. Development of the Norphlet began in earnest in the late 1960s, with new discoveries, including large fields at Flomaton, Hatters Pond, and Mobile Bay, continuing through the 2000s–2010s with the discovery of major oil fields in the offshore Desoto Canyon area. Onshore, Norphlet hydrocarbon traps typically are faulted salt anticlines, although many fields also have a stratigraphic trapping component because of the preservation of dune-scale bedforms. Thermal maturity across the Norphlet play varies significantly with depth, resulting in oil production transitioning downdip to natural gas liquids production, then dry gas production; production depths reach to greater than 21,000 feet. A 2024 U.S. Geological Survey assessment divided the Norphlet into four conventional assessment units defined by the type and frequency of trapping mechanisms, sedimentology, and thermal maturity of each area, with little potential for hydrocarbon development observed west of the Mississippi River. Although petroleum resources in the Norphlet were revised downward, it is probable that significant resources remain to be discovered.

Alabama, Florida, Mississippi

Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake

Stochastic ground motion simulation models are often less accurate at lower frequencies than at higher frequencies when fitting recorded data unless supplemented by a deterministic forward directivity velocity pulse model. Moreover, time-modulated stochastic models, which adjust ground motion amplitudes over time, typically use functions that fail to capture multiple strong-motion phases. The February 2023 Turkey (Türkiye) earthquake exhibited diverse recordings, including near-fault and far-field motions with pulse-like and non-pulse-like characteristics, along with single and multiple strong-motion phases. To better represent such a diverse set of recordings, this study enhances a fully non-stationary site-based stochastic model without combining it with a deterministic model. Improvements include a new band-pass filter with upper- and lower-frequency limits, which refines the representation of the low-frequency content. Moreover, a time-modulating function that can represent energy arrival in multiple strong phases is introduced. The reference model’s parameters are identified by fitting to the energy content, zero-level crossings, and cumulative counts of positive-minima and negative-maxima of a target accelerogram. This fitting procedure is modified to address the increased number of parameters. These improvements broaden the reference model’s applicability while preserving its simplicity, a key aspect appealing to engineering practitioners. The improved model’s applicability is demonstrated by simulating a dataset from the February 2023 Türkiye earthquake, and the accuracy is tested using a pulse-like Next Generation Attenuation Relationships for Western United States dataset. Validations are performed based on total energy, zero-level crossings, Fourier amplitude spectrum, elastic response spectra, and peak ground motion parameters. Validations are performed schematically in the time and frequency domains and quantitatively using goodness-of-fit scores, various validation-metrics errors, and inter-period correlations. Overall, the improved stochastic model can effectively simulate a set of diverse ground motion recordings, including near-fault pulse-like records, records with multiple strong phases, and far-field motions across a broad frequency range.

Kahramanmaraş

The U.S. Geological Survey 2025 Puerto Rico and U.S. Virgin Islands time-independent earthquake rupture forecast

We present the 2025 U.S. Geological Survey Puerto Rico and U.S. Virgin Islands (PRVI) time‐independent earthquake rupture forecast (ERF), developed for the 2025 update to the National Seismic Hazard Model (NSHM) for PRVI. The updated ERF improves upon a prior model from 2003, including an expanded fault inventory with slip‐rate estimates, updated seismicity catalogs, and refined subduction zone geometries and deformation models. It applies the fault‐system inversion methodology to solve for rates of ruptures on modeled faults, adapted from the 2023 NSHM (NSHM23) for the western United States, including the first application of the inversion to model rates on a U.S. subduction interface. Off‐fault and intraslab seismicity are constrained by observed seismicity and use updated methods developed for NSHM23. Uncertainties in model components are substantial, and the ERF represents epistemic uncertainties through a comprehensive logic tree consisting of 1.7 billion logic‐tree branches combined across all sources.

Puerto Rico, U.S. Virgin Islands

Cross-fade sampling: Extremely efficient Bayesian inversion for a variety of geophysical problems

This paper introduces cross-fade sampling, a computationally efficient Markov Chain Monte Carlo simulation method that uses a semi-analytical approach to quickly solve Bayesian inverse problems that do not themselves have an analytical solution. Cross-fading is efficient in two ways. First, it requires fewer samples to obtain the same quality simulation of the target probability density function (PDF). Secondly, it is much faster to evaluate the posterior probability of each sample than conventional sampling methods for simulating Bayesian posterior PDFs. Conventional methods require evaluating the prior probability (which describes your a priori constraints) and data likelihood (which describes the fit between the observations and the predictions of the model) for each sample model. However, cross-fading does not require evaluating the data likelihood, meaning that ‘big data’ can be fit with zero additional computational cost. Further, the cross-fading approach can be used to calculate the marginal likelihood associated with a model design, facilitating model comparison and Bayesian model averaging. Topics covered in this paper include derivation of the cross-fade approach and how it can be used to simulate Bayesian posterior PDFs and compute the marginal likelihood, discussion of the class of problems to which cross-fading can be applied (with examples from earthquake statistics, earthquake ground motion modelling, volcanic eruption forecasting, and finite fault slip modelling), demonstration of efficiency relative to existing sampling methods and discussion of how cross-fading can be used to account for prediction errors (i.e. epistemic errors) as part of the geophysical inverse problem.

Geophysical Journal International

Case study of deep learning image segmentation for the purposes of rapid 2D petrographic analysis in volcanic rocks

Automation using deep learning methods is a useful alternative to manual methods of petrographic segmentation, but often requires user familiarity with coding and/or algorithms. We examine the Dragonfly TM program's deep learning tools for application by users with a variety of skill levels as a method for petrographic image segmentation. An image processing methodology, bimodal image stacking, was created for low-input-data, high-efficacy training of models which can then be applied to varied samples. Using backscatter electron images we show that the resulting model segmentations agree with manual segmentation total and modal crystallinity values within 5%, and calculated plagioclase crystal size distribution (CSD) values within 2σ, despite limitations in discriminating mafic phases. Model creation and training takes <24 hours, 1–3 hours of which are supervised, and the resultant model can then be applied to new uncharacterized samples in <15 minutes per image. This allows for non-experts to create and utilize deep learning models to segment images of variable brightness and texture, at low user-time cost and resulting in size and shape data which are within uncertainty of manual segmentation. While some limitations are noted (for example, sieve-textured phases may need manual correction, and different minerals with similar BSE intensity may not be resolved as separate phases), this methodology can be utilized for general application of models to wide ranges of volcanic crystalline and bubble textures, and to create a library of models for rapid petrological analysis during volcanic eruptions.

Alaska

The potential impact of three-dimensional distributed slip models derived from real-time GNSS data on the performance of the ShakeAlert earthquake early warning system for slab interface earthquakes

The ShakeAlert® earthquake early warning (EEW) system is designed to warn users of imminent strong ground motion with sufficient time to take protective actions. ShakeAlert currently uses three algorithms to characterize the earthquake source. One estimates the location and magnitude using the first few seconds of the P wave and, while fast, tends to underestimate magnitude for M w 7.0+ earthquakes. A second estimates the location, orientation, length, and corresponding magnitude of a line source using observed peak ground acceleration and contributes primarily to M w 5.5+ earthquakes. The third infers earthquake magnitude from peak ground displacement measured using Global Navigation Satellite System (GNSS) data and offers nonsaturating magnitudes for M w 7.0+ earthquakes. Other EEW algorithms exist that infer temporally evolving spatially variable slip on a 3D fault surface from real‐time GNSS data, information that might enable more accurate and timely alerts in the event of large‐magnitude subduction interface earthquakes. Here, we evaluate the potential contribution of one such algorithm, BEFORES ( Minson et al. , 2014 ), to improve ShakeAlert performance through a simulated real‐time implementation of Bayesian evidence‐based fault orientation and real‐time earthquake slip (BEFORES) and other ShakeAlert algorithms using data for eight M w 7.6+ earthquakes. The test results demonstrate that BEFORES can produce well‐constrained and accurate magnitude estimates as soon as or sooner than other EEW algorithms, in turn enabling it to increase the amount of warning time users receive in many cases. However, with a modified Mercalli intensity (MMI) threshold of 3.5, which is commonly used for issuing alerts, BEFORES would tend to alert large geographic regions that did not feel strong shaking (MMI 6+). This effect can be mitigated using a higher alert threshold of MMI 4.5 without negative impact on the amount of warning time obtainable with BEFORES.

Bulletin of the Seismological Society of America

Characterization of groundwater resources near the southeastern part of Puget Sound, Washington

More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders. This multichapter volume documents the development of the conceptual and numerical groundwater-flow models of groundwater flow. Chapters A, B, and C provide an overall introduction to the multichapter volume (Chapter A), the conceptual hydrogeologic framework (Chapter B), and the groundwater budget (Chapter C). Chapters D and E describe numerical groundwater-flow model construction and calibration (Chapter D) and the numerical groundwater-flow model results (Chapter E). Collectively, these reports present a characterization and simulation tool for groundwater resources near the southeastern part of Puget Sound, Washington.

Scientific Investigations Report

Simulation of groundwater flow in the Silurian aquifer, eastern Iowa, 2020–45

The Silurian aquifer is an important water source for municipalities, industry, and rural households and communities in eastern Iowa, including Johnson County. Increasing demand for groundwater from the aquifer indicated that analytical tools may be beneficial to quantify groundwater resources and inform water-management decisions for the aquifer. The U.S. Geological Survey, in cooperation with the Johnson County Board of Supervisors, developed conceptual and numerical groundwater models to simulate water levels in the Silurian aquifer, determine groundwater budgets, and forecast changes in groundwater levels through 2045. A MODFLOW numerical model was constructed to match water levels for 2020 through 2022. Model performance was assessed by comparing simulated and observed water-level hydrographs at several pumping and monitoring wells. Hydrograph comparison indicates that the model simulates water levels accurately at monitoring wells that are not near areas of pumping and generally overpredicts water levels near pumping wells, while accurately simulating the drawdown trend over time at pumping wells. This transient model framework was then used to predict water levels through 2045 in a scenario of moderate drought and increased groundwater withdrawals. Results from the predictive model indicate as much as 13 meters of additional drawdown in 2045 from 2020 water levels in the area of pumping wells.

Iowa

Ground-motion simulations for the 2024 Mw 4.8 Tewksbury, New Jersey, earthquake

Ground-motion simulations of notable earthquakes in the central and eastern United States are limited and typically assume one-dimensional (1D) Earth structure. In this study, we use a three-dimensional (3D) seismic velocity model to better constrain the depth and focal mechanism of the April 5th, 2024, moment magnitude 4.8 Tewksbury earthquake and investigate the spatial variability of earthquake ground motions and the effects of nearby sedimentary basins. We perform earthquake ground-motion simulations up to 0.5 Hz using the 3D spectral-element wave-propagation solver SPECFEM3D over a region 280-km wide by 260-km long by 77-km deep. Topography and subsurface geophysical structure are assigned using the U.S. Geological Survey National Crustal Model with a minimum shear-wave velocity of 200 m/s. We use earthquake time series from 13 broadband seismic stations in the region that have a uniform azimuthal distribution and epicentral distances ranging from 76 to 131 km to compare with synthetics and explore the effects of 1D versus 3D seismic structure on focal mechanism and depth solutions. Ground-motion intensity metrics are also presented relative to the NGA-East ground-motion models (GMMs) currently used in seismic hazard assessments for the region. We find that the 3D model, which reveals a wide spatial variability of period-dependent ground motions, yields better predictions of earthquake ground motions relative to the 1D model and the NGA-East ergodic ground-motion model, with 76 percent reduction of residual variance in observed ground motions averaged over 3-, 5-, 7-, and 10-second periods. Use of the 3D model to solve for a focal mechanism yields a shallower focal depth at 4 km and a shallower east-dipping focal plane relative to the U.S. Geological Survey regional moment tensor and Global Centroid Moment Tensor. Our study demonstrates that use of 3D seismic velocity models can improve estimates of earthquake focal mechanisms, ground motions, and seismic hazard.

New Jersey

Aftershock forecasting

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models, and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing, but don’t yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine learning forecasts shows promise, and new machine learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts.

Annual Review of Earth and Planetary Sciences

The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program

The U.S. Geological Survey is initiating the 3D Hydrography Program (3DHP), the first systematic remapping of the Nation’s surface waters since the original 1:24,000-scale topographic mapping program was active from 1947 to 1992. Building on decades of experience maintaining the National Hydrography Dataset (NHD), the Watershed Boundary Dataset (WBD), and the NHDPlus High Resolution (NHDPlus HR), the 3DHP will completely refresh the Nation’s hydrography data and improve discovery and sharing of water-related data. The design of the 3DHP is based on the results of a study that estimated that the fully implemented program would have the potential to provide more than $1 billion in benefits to Federal, State, Tribal, Territorial, and local governments and to private and nonprofit organizations every year, in addition to myriad societal benefits. The 3DHP would directly support better decision making regarding water resources by providing more accurate, complete, and integrated information than is currently available. The 3DHP datasets will include a three-dimensional (3D) hydrography network generated from and integrated with elevation data from the 3D Elevation Program (3DEP) to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. The 3DHP datasets will inherit key attributes of the NHD, WBD, and NHDPlus HR, and they also will include new attributes and links to other data such as the U.S. Fish and Wildlife Service National Wetlands Inventory, groundwater data, and engineered hydrologic systems such as stormwater networks. The 3DHP will be designed to provide a set of open and interoperable web-based tools, maps, and data catalogs, creating a robust system for users to reference their information about water; the system elements are collectively referred to as the “infostructure.” The 3DHP and the infostructure can provide a foundational geospatial underpinning for the Internet of Water, a community-based effort to modernize tools and technologies to share water data. As proposed, the 3DHP would begin providing products and services to the public in 2024.

Circular

Protected from Pterygoplichthys? Predicting thermal habitat suitability for nonnative armored catfish in the Suwannee River

Objective Nonnative fishes can modify ecosystems and harm economies when they are introduced to new environments. Climate change is likely to assist the spread and establishment of some nonnative fishes (e.g., warmwater species), but spatiotemporal gaps in water temperature monitoring and modeling may prevent ecologists and managers from forecasting thermal habitat suitability for these taxa. The purpose of this study was to develop a predictive model of winter water temperatures and thermal habitat suitability for two priority nonnative armored catfish, Vermiculated Sailfin Catfish Pterygoplichthys disjunctivus and Orinoco Sailfin Catfish P. multiradiatus , in the Suwannee River, Florida and Georgia. Methods Precipitation- and groundwater-corrected air–water temperature models were developed and evaluated using a model selection procedure to predict water temperatures at four sites in the Suwannee River. These models were chosen because they blend the simplicity of air–water temperature models with the accuracy of hydrometeorological models to create an efficient, economical, management-relevant approach for analyzing and forecasting water temperature. Results Most of the top-performing water temperature models (92%) had precipitation or groundwater corrections to air–water temperature formulations. Projected mean and maximum water temperatures increased as simulated climate change intensified. All four Suwannee River sites studied were projected to be thermally hospitable to the survival of Vermiculated Sailfin Catfish. Lower river sites, noticeably warmer than upper river sites, were conducive to the survival of Orinoco Sailfin Catfish throughout the winter months. The upper river sites were too cold for Orinoco Sailfin Catfish survival in some climate-change scenarios, but the Suwannee River has an abundance of constant-temperature springs that are likely hospitable to Vermiculated Sailfin Catfish and Orinoco Sailfin Catfish throughout the year. Conclusions The findings suggest that winter water temperatures will likely not be a barrier to the survival of Pterygoplichthys catfish in the Suwannee River, amplifying the importance of conservation and management approaches to inhibit their spread and establishment. If the Pterygoplichthys population remains small and isolated and decision makers are able to devote required staff time and resources to managing these species, removal and eradication at local if not broader scales may be reasonable goals. This study provides a water temperature modeling approach that can aid ecologists and managers in prioritizing sites to prevent the introduction, slow the dispersal, eradicate, and control Pterygoplichthys catfish and other nonnative fishes in the Suwannee River and beyond.

Florida, Georgia

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California

PEST++IES how many iterations & realizations, finding the point of diminishing returns

PEST++IES (White 2018; White et al. 2020) is widely used in the groundwater modeling community for its ability to perform computationally efficient history matching and uncertainty analysis in a highly parameterized context. One primary advantage of using an iterative ensemble smoother is that the number of model runs required per iteration depends on the number of realizations in an ensemble, not the number of parameters in each realization. However, this raises the question: what is the optimal number of realizations and iterations to use for any one model before the point of diminishing returns? Using a modified version of the Freyberg model (Freyberg 1988; Hunt et al. 2020), different parameter and observation scenarios were evaluated for four iterations and ensembles of 10, 25, 50, 100, 250, 500, 1000, and 2000 realizations. To match observations, PEST++IES altered hydraulic conductivity ( k ), both globally across the model and locally at three different pilot point densities, as well as global recharge (via a single multiplier), global river conductance, and individual well flow rates. Risk-based well capture zone results (Fienen et al. 2022a) and estimated hydraulic conductivity fields from each scenario were quantitatively and qualitatively compared against the “truth” model and its outputs. Across the cases examined, ensemble sizes of 100 to 250 realizations and two PEST++IES iterations were generally sufficient to achieve good results.

Groundwater

A time-space model of graphite mineral systems

Increasing demand for graphite in energy storage systems warrants review of graphite ore genesis in a mineral systems framework. Orogenic graphite encompasses the metamorphic and orogenic mineral systems that produce flake graphite and hydrothermal vein (lump and chip) graphite deposits, respectively. A common feature of orogenic graphite deposits is an association with upper amphibolite- to granulite-facies metasedimentary rocks in continent-continent or continent-island arc collisional orogens. Orogenic flake graphite deposits form primarily through graphitization of organic carbon during regional metamorphism, but strain localization and partial melting of pelitic protoliths are likely important processes for graphite grade and quality enrichment. Orogenic vein graphite deposits precipitate from hydrothermal fluids, possibly derived from metamorphism or anatexis at depth. Decarbonation reactions in mixed calcareous-carbonaceous metasedimentary sequences are the most likely carbon sources for the veins. In contrast, intrusion-related graphite includes magmatic-hydrothermal and metamorphic mineral systems that form primarily in continental arc settings via the interaction of magmas with carbonaceous sedimentary packages. Magmatic-hydrothermal flake graphite deposits are hosted in plutonic and volcanic rocks, and result from the exsolution of CO 2 -CH 4 -rich fluids from melts contaminated by such packages. Contact metamorphism of carbonaceous sedimentary rocks by plutons produces some microcrystalline (amorphous) graphite deposits, including many in China. Compilation of geologic data from known graphite deposits globally suggests that pulses of carbon deposition in the Paleoproterozoic, Mesoproterozoic, and Neoproterozoic provided source carbon material. Subsequent supercontinent orogenesis at ca. 2,100 to 1,700 Ma (Columbia), ca. 1,300 to 1,000 Ma (Rodinia), and ca. 650 to 500 Ma (Gondwana) resulted in the genesis of orogenic flake and vein graphite deposits, where favorable geologic components overlapped with organic ± carbonate carbon-rich strata. Cryogenian deposition of graphite protoliths and Cryogenian – Cambrian metamorphic mineralization account for nearly 75% of all known resources globally and coincide with profound carbon isotope excursions and climate variability, implying a link with the global carbon budget. Comparatively few graphite deposits are associated with Pangea-forming orogens, attributed to less exhumation and/or denudation. High-temperature metasedimentary belts containing organic carbon-rich protoliths are most favorable for hosting orogenic flake graphite deposits, whereas sequences that also contain carbonate protoliths are favorable for orogenic graphite veins. Continent-scale orogenic belts may host both deposit types along with vanadium deposits. Use of a time-space mineral systems framework for graphite deposits can improve exploration models needed to ensure future supply of this critical mineral and provide insights into Earth’s long-term carbon cycle.

Mineralium Deposita

Detecting hidden sedimentary geothermal systems in the Upper Colorado River Basin

Geothermal resources exist in sedimentary rock where circulation of water for efficient extraction or storage of heat is possible. Except in rare instances where hot water is expressed at the land surface, sedimentary geothermal resources are hidden, so the identification of these systems is optimally accomplished using predictive subsurface modeling. An integrated approach using detailed paleogeographic interpretations, subsurface geologic mapping, and numerical modeling has produced regional geologic and temperature models for the Upper Colorado River Basin, a large watershed in central North America that contains many sedimentary basins. These models identify areas of hidden sedimentary geothermal resource potential in low temperature (<90°C), moderate temperature (90–150°C), and high temperature (>150°C) fairways across the study area. These models incorporate maps of key horizons in outcrop and the subsurface to create a robust structural framework that can be used to target favorable geology for natural or engineered permeability. This framework is populated with lithologies derived from detailed palaeogeographical maps and over 40,000 bottom hole temperature (BHT) values were used to create a calibrated three-dimensional (3D) temperature model across the region. The resulting maps serve as a regional sedimentary geothermal play fairway screening tool for evaluating different grades of sedimentary geothermal resources and for identifying areas of interest where more detailed, prospect-scale studies can be undertaken.

Arizona, Colorado, New Mexico, Utah, Wyoming

Simulation of groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado

Salinity, or total dissolved solids (TDS), of the Colorado River affects agricultural, municipal, and industrial water users and is an important concern in the Western United States. In the Paradox Valley of southwestern Colorado, natural discharge of sodium-chloride brine to the Dolores River from the underlying core of a salt-valley anticline accounts for about 6 percent of the salinity load to the Colorado River. Formation of the Paradox Valley began during the Miocene, and subsequent erosion exposed the Pennsylvania Paradox Formation in the core of the anticline where a cap rock, collapse features, breccia, and sodium-chloride saturated brine developed at the top of the exposed salt diapir. The discharge of brine to the Dolores River is affected by these dissolution features, along with seasonal hydrologic conditions and density-dependent flow between older dense brine and the younger fresh groundwater in the overlying alluvial aquifer. To reduce TDS concentrations in the Dolores River through the Paradox Valley, the Bureau of Reclamation has pumped brine from a series of shallow wells adjacent to the river since July 1996. The pumped brine is collected and piped to a deep disposal well where it is injected into the Mississippian Leadville Limestone at a depth of about 4,570-meters below land surface. The pumping and injection operation is collectively known as the Paradox Valley Unit (PVU), and by 2015, the PVU had substantially reduced TDS concentrations in the Dolores River by about 70 percent. Since 2019, injection-pressure limits and related seismic activity have constrained deep-well injection and thus brine pumping at the PVU. In cooperation with the Bureau of Reclamation, the U.S. Geological Survey developed a MODFLOW-6 three-dimensional, variable-density groundwater flow and TDS transport model of the Paradox Valley to evaluate the effects of PVU pumping operations on brine discharge to the Dolores River and to guide additional research. The finite-difference model grid consists of 76 rows and 48 columns oriented from northwest to southeast in alignment with valley topography and groundwater-flow directions in the near-surface freshwater alluvial aquifer. A 7-layer hydrogeologic framework was developed from existing datasets to represent the alluvial aquifer, cap rock, collapse breccia, and groundwater flow and TDS transport from the underlying Paradox Formation salt to the Dolores River. The model represents a 33-year transient calibration period from 1987 through 2020 that includes pre-PVU conditions from 1987 through June 1996 and post-PVU conditions from July 1996 through 2020. A 1,000-year simulation of groundwater flow and coupled TDS transport computed the initial conditions for the subsequent 33-year transient simulation. Observations of precipitation, streamflow, evaporation, agricultural land use, and PVU brine pumping rates were used to specify appropriate boundary conditions to the model representing time-varying recharge, tributary streamflow, groundwater underflow, evapotranspiration (ET), and PVU pumping. Values for average monthly streamflow and TDS concentration at the upstream streamgage, the Dolores River at Bedrock (USGS streamgage 09169500), were specified as model input where the Dolores River enters Paradox Valley. Observed pumping from the PVU, water levels and TDS concentrations in groundwater, and streamflow and estimated TDS concentrations at the downstream streamgage, the Dolores River near Bedrock (USGS streamgage 09171100), were calibration targets that constrained the manual calibration of model parameters representing aquifer hydraulic conductivity, storage, streambed conductance, recharge, and (ET). Two primary model-calibration targets were the match between observed and simulated TDS mass flux from PVU pumping wells and the match between estimated and simulated TDS mass flux to the Dolores River. The simulated TDS mass withdrawn by pumping wells is calculated by the model as the product of the assigned pumping rate and simulated groundwater TDS concentrations. Because actual pumping rates were assigned as simulated values, the total simulated PVU pumping for the 33-year calibration is within 0.5 percent of the observed values. However, simulated concentrations and thus mass flux of TDS withdrawn by the PVU pumping wells were consistently about 26 percent less than observed values for all the simulated time periods (33-year simulation, pre-PVU, and post-PVU). The representation of brine inflow was explored through additional modeling to evaluate the effect of the simulated brine source on groundwater TDS concentrations. Results indicated that a saturated-salt constant-flux brine source best replicated the magnitude and transient pattern observed for TDS mass flux from PVU pumping wells. The simulated TDS mass flux to the Dolores River is compared to estimates based on observed streamflow and specific conductance (SC) data for the downstream streamgage. The calibrated model provided a close fit of simulated to measured streamflow at the downstream streamgage, and the calibrated model fit to estimated TDS concentrations at the downstream streamgage was reasonable. The greatest differences between simulated and estimated values occurred during drought periods from June 2000 to March 2003, May 2012 to June 2013, and October 2013 to October 2014, when simulated TDS concentrations in the river were greater than estimated concentrations. In general, simulated TDS mass flux to the river for the pre-PVU period is in good agreement with estimated values (2-percent difference), but the model overestimated TDS mass flux to the river by about 41 percent during the post-PVU period. The model uncertainty with respect to TDS mass flux to the river indicates other processes or model parameters not well represented by the model are affecting the system, especially during drought. During model calibration, the most sensitive parameters were identified as vertical hydraulic conductivity of the alluvial aquifer, conductance of the Dolores River streambed, ET extinction depth and rate, and recharge rate. Five 5-year scenarios of conditions for 2021–25 were simulated to assist evaluation of alternative strategies to manage the discharge of brine into the Dolores River. The first scenario simulates no PVU pumping and serves as a base case for comparison to the other scenarios. Two scenarios simulate the effects of varying withdrawal timing at an annual rate about one-third less than during 2010 through 2018. During high-flow spring snowmelt runoff periods when brine discharge is naturally minimized, PVU pumping does not substantially affect salinity in the Dolores River, and comparison of these two scenarios indicates that scheduling brine withdrawals during times of low river stage is nearly as effective at reducing TDS mass flux to the river as pumping brine year-round. Cessation of pumping during periods of high river stage may be advantageous for system maintenance, brine injection, and seismic-risk reduction. The fourth scenario tested the effect of reducing irrigation-return flow on brine discharge and predicted a slight reduction of TDS mass flux to the Dolores River, but not as great a reduction as that of using the PVU to remove brine. The fifth scenario simulated 5 years of drought conditions without PVU pumping and indicates brine discharge during drought about 15 percent greater than during average hydrologic conditions. Results from scenario 5 are consistent with the calibrated model results and indicate that aquifer properties and ET processes and parameters may be affecting simulation results during drought. The Paradox Valley groundwater model provides a reasonable overall match to observed conditions in the Dolores River. The model is useful for evaluating relative differences between brine management scenarios to inform PVU operational decisions and to identify gaps in data and process understanding. Representation of the brine source, hydraulic-conductivity parameters, and recharge and ET processes were identified as potential areas for additional field and modeling research. Additional research in the Paradox Valley might include field-data collection that provides additional information on the hydrogeologic framework, groundwater levels, groundwater TDS concentrations, stream characteristics, and aquifer properties. Additional modeling efforts could benefit from applying advanced tools for model development, calibration, and visualization including parameter-estimation and sensitivity analysis. Statistical evaluation of known model uncertainties such as hydraulic conductivity, streambed conductance, representations of the brine source, recharge, and ET could improve the match between simulated and estimated TDS mass flux from PVU pumping wells and to the Dolores River further informing model predictions and system understanding for the Paradox Valley.

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