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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↗
USGS2025

False positives in the identification of dynamic earthquake triggering

Dynamic earthquake triggering is commonly identified through the temporal correlation between increased seismicity rates and global earthquakes that are possible triggering events. However, correlation does not imply causation. False positives may occur when unrelated seismicity rate changes coincidently occur at around the time of candidate triggers. We investigate the expected false positive rate in Southern California with global M ≥ 6 earthquakes as candidate triggers. We compute the false positive rate by applying the statistical tests used by DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 to synthetic earthquake catalogs with no real dynamic triggering. We find a false positive rate of ∼3.5%–8.5% when realistic earthquake clustering is present, consistent with the 95% confidence typically used in seismology. However, when this false positive rate is applied to the tens of thousands of spatial-temporal windows in Southern California tested in DeSalvio and Fan (2023), https://doi.org/10.1029/2023jb026487 , thousands of false positives are expected. The expected false positive occurrence is large enough to explain the observed apparent triggering following 70% of large global earthquakes (DeSalvio & Fan, 2023, https://doi.org/10.1029/2023jb026487 ), without requiring any true dynamic triggering. Aside from the known triggering from the nearby El Mayor-Cucapah, Mexico, earthquake, the spatial and temporal characteristics of the reported triggering are indistinguishable from random false positives. This implies that best practice for dynamic triggering studies that depend on temporal correlation is to estimate the false positive rate and investigate whether the observed apparent triggering is distinguishable from the correlations that may occur by chance.

JGR Solid Earth↗
USGS2026

Hazard potential of compound flooding from rainfall, storm surge, and groundwater in coastal New York and Connecticut

Compound flood events, the co-occurrence of multiple flood drivers, can result in flood hazard potential exceeding that of any single driver alone. To evaluate compound flooding in a semi-urbanized coastal area, historical records dating back to 1970 are used to study the co-occurrences of high precipitation, storm surge, and shallow groundwater conditions along the coastlines of New York and Connecticut. Joint return periods for coincident precipitation-surge events were computed using statistical dependence models and compared to the assumption of independence as a ratio, referred to here as a return period adjustment. Results indicate distinct seasonality where compound events in the area disproportionately occur in the cold season between October and April. Return period adjustments range from a factor of 1 to almost 9, demonstrating the range in precipitation-storm surge dependence across the study area. Across all 24 station triad locations, groundwater levels were elevated during times of precipitation- surge co-occurrence, reflecting the tendency for coastal storms and shallow groundwater conditions to co-occur seasonally. The result is a pseudo-trivariate compound flood hazard score and corresponding hazard map that integrates dependence between daily precipitation-surge events and overall monthly groundwater levels (as a precondition) into a relative compound hazard score. The location with the highest compound flood hazard score is on the south shore of Long Island, as well as locations across coastal Connecticut where groundwater levels compound the co-occurrence of heavy precipitation and storm surge.

Connecticut, New York↗
USGS2025

Fault displacement model for surface principal rupture of strike-slip faults

The probability distribution model for principal displacement accommodated on the surface main trace is a critical input to the fault displacement hazard analysis. This article presents a new model for strike-slip ruptures in the moment magnitude ( M ) range of 6 to 8.3. The new model is the outcome of a multi-year research effort to update the widely used model developed by Petersen and others in 2011. Updates include the adoption of the Fault Displacement Hazard Initiative database and enhancements to rupture and displacement data preparation. Statistical formulation and estimation have also been updated substantially. A three-parameter modified normal distribution that we refer to as the negative Exponentially Modified Gaussian distribution is adopted to model the probability distribution of the natural logarithm of principal displacement. Formulation for the mean parameter of the modified normal includes a random earthquake term, a nonlinear scaling relation with M , and an ellipse function for along-main-trace variation. The aleatory variability of the updated model now depends on M as well as site’s along-main-trace position. These updates not only significantly improve the fit to the distribution of the observed displacements but also yield reasonable 95th percentile predictions for M > 7.5 events. Alternative models representing the estimation uncertainty of the M -scaling relation are also developed. These new models are compared to the previous model in terms of percentile predictions and the calculated hazard curves. The steeper hazard curves from the new models yield a lower exceedance rate than the normal-distribution based model developed previously by Petersen and others.

Earthquake Spectra↗
USGS2021

Factors influencing distribution of Coccidioides immitis in soil, Washington State, 2016

Coccidioides immitis and Coccidioides posadasii are causative agents of Valley fever, a serious fungal disease endemic to regions with hot, arid climate in the United States, Mexico, and Central and South America. The environmental niche of Coccidioide s spp. is not well defined, and it remains unknown whether these fungi are primarily associated with rodents or grow as saprotrophs in soil. To better understand the environmental reservoir of these pathogens, we used a systematic soil sampling approach, quantitative PCR (qPCR), culture, whole-genome sequencing, and soil chemical analysis to identify factors associated with the presence of C. immitis at a known colonization site in Washington State linked to a human case in 2010. We found that the same strain colonized an area of over 46,000 m 2 and persisted in soil for over 6 years. No association with rodent burrows was observed, as C. immitis DNA was as likely to be detected inside rodent holes as it was in the surrounding soil. In addition, the presence of C. immitis DNA in soil was correlated with elevated levels of boron, calcium, magnesium, sodium, and silicon in soil leachates. We also observed differences in the microbial communities between C. immitis -positive and -negative soils. Our artificial soil inoculation experiments demonstrated that C. immitis can use soil as a sole source of nutrients. Taken together, these results suggest that soil parameters need to be considered when modeling the distribution of this fungus in the environment.

Washington↗
USGS2024

From exploration to production: Understanding the development dynamics of lithium mining projects

Recently, there has been considerable recent controversy whether current and new lithium mines will be able to supply the rapidly growing needs of the electromobility transition. Mineral exploration projects are typically active for many years, and only some become operational mines. From exploration to production, the projects go through several stages of characterisation and evaluation. At each stage, decisions are made by companies and stakeholders to advance, continue or stop the project. This is a complex process, and even projects with very similar geological and technical characteristics may take very different trajectories, depending on external factors such as global market conditions and local regulatory environments. The present study investigates the dynamics of this process for lithium exploration projects. A global database of 397 lithium projects was compiled, covering their progression through major development stages between 2004 and 2022. Ordinal logistic regression was used for the statistical analysis of this data. Different explanatory variables were tested, including economic, geological, technical, and geographic factors, to identify the best predictors for project progress at each development stage. The results suggest an essential role for lithium carbonate prices, and a variable role for other factors at each stage. Critically, the already elapsed lead time and project economics, which are traditionally considered important for the prediction of the start-up of individual mines, do not appear to be relevant in all cases. The results provide important insights into the dynamics of lithium supply and may eventually allow more realistic forecasts to be made for future lithium market dynamics.

Resources Policy↗
USGS2026

Microclimate mediates the strength and direction of avian biotic interactions

Theory predicts that that the strength and direction of species interactions can shift from being competitive in benign environments toward being facilitative in stressful environments. However, the environmental context dependency of species interactions has rarely been tested in animal communities. We capitalized on a 15-year, landscape-scale dataset, collected annually in a relatively stable old-growth forest environment to test the long-held hypothesis that the strength and direction of species interactions might be mediated by climatic conditions. It is generally accepted that competitive and facilitative interactions drive the distributions of many species. Using multi-species dynamic occupancy models applied to long-term data, we tested whether annual settlement by bird species could affect either the persistence or settlement by other phylogenetically related species, and whether these interactions are mediated by microclimate. We found that species interactions were influenced by microclimate for some, but not all avian species pairs. Related species pairs more often showed settlement dynamics that were indicative of attraction rather than repulsion. In some cases, competitive interactions at warmer microclimates flipped to become facilitative in colder ones. However, the reverse was also true: facilitative interactions amplified for some species under warm conditions. Furthermore, species pairs that were closely related were more likely to exhibit competitive relationships along at least part of the microclimatic gradient. Our results highlight the importance of using long-term data to incorporate competitive and facilitative interactions into species distribution models and support the notion that the strength and direction of species interactions can be dependent on microclimatic environmental conditions.

Oregon↗
USGS2024

Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning

We developed a suite of models using deep learning to make hindcast predictions of the 7‐day average backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.

Water Resources Research↗
USGS1999

The mineral economy of Brazil--Economia mineral do Brasil

This study depicts the Brazilian government structure, mineral legislation and investment policy, taxation, foreign investment policies, environmental laws and regulations, and conditions in which the mineral industry operates. The report underlines Brazil's large and diversified mineral endowment. A total of 37 mineral commodities, or groups of closely related commodities, is discussed. An overview of the geologic setting of the major mineral deposits is presented. This report is presented in English and Portuguese in pdf format.

Data Series↗
USGS2026

Potential impacts of groundwater pumping on stream temperature are greatest in streams with substantial cold groundwater inflows

Groundwater pumping-induced reductions in streamflow (known as ‘streamflow depletion’) have been documented worldwide, but potential impacts of streamflow depletion on stream temperature are not well understood. Here, we use two types of models to identify potential impacts of pumping on stream temperature across the conterminous United States (CONUS) to determine which aspects of a stream's annual thermograph (thermal signatures) can be used to monitor and manage streamflow depletion impacts on stream temperature. We used long-term streamflow and stream temperature data from 30 streamgages across CONUS and surrogate models of streamflow depletion to analyse potential stream temperature impacts at each site. We compared two different stream temperature modelling approaches: (i) a process-based energy balance model and (ii) statistical regression models based on air temperature and stream discharge. We calculated a suite of thermal signatures under depleted and non-depleted conditions for each stream and found that maximum annual 7-day temperature and annual temperature range are potentially the most sensitive to streamflow depletion, with potential changes of at least 2°C at > 70% of the sites when using the process-based model. We also found that the regression-based models predicted much less sensitivity of stream temperature to streamflow depletion than the process-based model. This work provides an initial evaluation and sensitivity analysis of the potential impacts of streamflow depletion on stream temperature. We demonstrate that stream temperature may be most sensitive to pumping in streams with a high proportion of flow sourced from relatively cold groundwater inputs, and that regression-based stream temperature models may underpredict stream temperature changes caused by streamflow depletion.

conterminous United States↗
USGS2026

Patterns and drivers of cliff erosion in Big Sur, California, USA using repeat photogrammetry, 2017–2023

Seacliff erosion in steep terrain poses major risks to transportation and critical infrastructure. In Big Sur, California, USA, seacliff erosion threatens the sustainability of the central coast stretch of California State Route 1, a transportation corridor that is critical to the region's economy. Published cliff retreat rates for the region range from 1 to 40 cm yr −1 , highlighting that high-resolution, process-based studies could enhance understanding of the causes of spatial and temporal variability. We quantified cliff erosion and investigated its drivers along ∼13 km of the Big Sur coastline at week–month timescales during the late fall to early spring wet seasons between January 2017 and June 2023 by analyzing 3D point clouds developed from aerial imagery using four-dimensional structure-from-motion (4D SfM) photogrammetry techniques. We calculated cliff face retreat rates of 2.23 ± 3.06 cm yr −1 (mean ±1 σ ), an order of magnitude lower than long-term estimated rates for the region (which included large deep-seated landslides), but in line with short-term rates reported across California. Change detection imagery comparison, cliff profiles through time, and statistical analysis reveal a cyclical cliff evolution process in which erosion by wave action at the cliff base destabilizes the cliff and primes it for subsequent failure during precipitation events. Although more erosion by volume could be attributed to precipitation-induced increases in soil moisture (784 m 3 km −1 yr −1 ) compared with erosion attributed to wave power (282 m 3 km −1 yr −1 ), our observations underscore the coupled nature of these processes in driving cliff evolution, consistent with established theory and observations.

California↗
USGS2025

Relations of groundwater quality to long-term surface disposal of produced water near the Midway-Sunset and Buena Vista Oil Fields, California, USA

Contamination of groundwater by oil-field fluids in proximity to oil and gas development has been an issue of concern to water users and regulators given long histories of development and legacy disposal practices. A robust set of geochemical tracers including petroleum hydrocarbon compounds, thermogenic gases, inorganic ion concentrations, stable isotopes, radioactive isotopes, and noble gases were used to assess if oil-field fluids mixed with groundwater near the Midway-Sunset and Buena Vista Oil Fields in California, USA. Results show evidence of mixing of oil-field fluids with groundwater within the study area from either anthropogenic or natural processes. Produced water plumes associated with modern surface disposal facilities, used since the late 1950s, extend up to 1.5 km and currently remain within the boundaries of the oil fields. Plumes associated with earlier routing of produced water down natural drainages and in large retention structures (Midway Basin and Sunset Basin) near the Buena Vista Lake Bed are present in groundwater east of the oil fields. Based on geochemical tracer evidence, aerial imagery, and aerial electromagnetic surveys, these legacy plumes reach the western portion of the Central Valley aquifer system, an important groundwater resource for agricultural and domestic supply. The legacy plume associated with Sunset Basin may further be detected downgradient in deeper groundwater beneath the southern extent of the Buena Vista Lake Bed based on the presence of thermogenic gases and petroleum hydrocarbon compounds.

California↗
USGS2025

Scoping decision-maker needs and science availability to support regional natural capital accounting in the U.S. Colorado River Basin

Natural capital accounting has the potential to yield important policy insights at multiple scales, but there remains a disconnect between regional-scale natural capital accounts and their use for informing policy. In this paper, we propose a roadmap that could lead to the creation of policy-relevant regional accounts, with steps split across an initial scoping phase and a subsequent development phase. We demonstrate the scoping steps in action with an application to the Colorado River Basin (“Basin”), a large watershed in the southwestern United States (U.S.) that has faced aridification and substantial high-profile tradeoffs around the use of its water and other natural resources. Drawing on prior U.S. Geological Survey science co-production efforts, we conducted a series of eight discussion sessions with 41 scientists and science representatives whose work is relevant to Basin water, riparian and riverine ecosystems, upland ecosystems and energy and minerals. We summarise participants' thoughts on key topics and economic linkages, their insights and questions of interest and their recommendations on existing scientific data sources and gaps. We evaluate the suitability of the available data for construction of System of Environmental-Economic Accounting (SEEA) Central Framework and SEEA Ecosystem Accounting accounts, including those for land, water, forests, energy and minerals and ecosystems (covering extent, condition and ecosystem services). We present a series of lessons learned during the scoping phase, as well as lessons that could be relevant for future practitioners engaging in the development phase. The information can help guide the development of timely and relevant regional-scale environmental-economic accounts in the U.S. and beyond.

Arizona, California, Colorado, Nevada, New Mexico,↗
USGS2024

Beyond the wedge: Impact of tidal streams on salinization of groundwater in a coastal aquifer stressed by pumping and sea-level rise

Saltwater intrusion (SWI) is a well-studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field-collected data, we developed a regional, variable-density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea-level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater-saltwater interface. We found the presence of the marsh inverts the typical freshwater-saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.

Delaware↗
USGS2026

Riverscape heterogeneity shapes population diversity for a migratory fish

Habitat patch dynamics can scale up to influence population demography and diversity with implications for resilience to environmental stochasticity. But how the spatial arrangement and size of habitat patches interact with other components of habitat heterogeneity to shape population diversity at larger spatial scales is not well understood. For riverine fishes, there is increasing evidence that tributary streams provide critical demographic support to main stem rivers. However, the extent to which main stem rivers rely on demographic contributions from tributaries, and the factors underlying this dependence, have not been assessed. Here, we used genetic stock identification to evaluate the effect of tributaries on population diversity of Yellowstone cutthroat trout ( Oncorhynchus virginalis bouvieri ) occupying the main stem Snake River, Wyoming, USA. We found that the main stem relied almost entirely on tributaries for demographic support, but main stem composition varied spatially among river sections. Distance between habitat patches, catchment area, and groundwater availability acted in concert to determine the contribution of specific tributaries to the main stem, but contributions were ultimately modulated by habitat connectivity. We also found evidence for multi-scale spatial structure in tributary contributions, providing insight into untested drivers of main stem river population diversity. Our results demonstrate how spatially discrete and distributed riverscape attributes influence population diversity at broader spatial scales, illustrating how ecosystem resilience emerges from the dynamic, two-way exchange of individuals and energy across habitat networks. Management plans for large rivers that address the ecological contributions of tributaries may be needed to achieve optimal outcomes. Similarly, conservation strategies that exclusively focus on headwater streams may fail to capture the broader habitat requirements necessary to maintain robust cold-water fish populations and associated recreational fisheries, particularly under global environmental change.

Wyoming↗
USGS2025

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↗
USGS2026

Legacy of the fumigant 1,2-dibromo-3-chloropropane (DBCP) in California groundwater

The fumigant pesticide 1,2-dibromo-3-chloropropane (DBCP) was widely used in California agriculture during the 1960s and 1970s before being banned in 1979. Despite this ban, DBCP continues to contaminate groundwater due to its persistence and mobility. This study evaluates the distribution, historical trends, and projected persistence of DBCP in California using data from over 13,000 public supply wells and additional domestic, irrigation, and observation wells (1980-2022). Since 2010, DBCP has been detected in 9% of public supply wells statewide, with higher frequencies in the San Joaquin Valley (21%) and upper Santa Ana River watershed (13%), where DBCP use was most prevalent. Approximately 70% of wells had decreasing concentration trends, whereas increases were more common in deeper wells, indicating downward vertical migration of the DBCP front. Groundwater age estimates show that recharge timing aligns with the 1960s–1970s loading period, enabling reconstruction of peak inputs and providing a basis for age based modeling. To estimate future persistence, we applied a one dimensional advection–dispersion model that simulates long term declines in peak concentrations based on groundwater age, historical loading, and a 38 year degradation half life. Model projections suggest that concentrations above the maximum contaminant level may persist in a declining number of wells until approximately 2080 (range: 2048–2109), with longer persistence in the San Joaquin Valley. The simplified modeling framework, based on age distributions typical of wells capturing peak concentrations, can provide practical regional scale assessment of non-point source contaminants where long-term monitoring exists. This study highlights how the legacy of DBCP contamination will likely affect California's groundwater resources throughout the 21st century.

California↗
USGS1989

Techniques for simulating flood hydrographs and estimating flood volumes for ungaged basins in east and west Tennessee

A dimensionless hydrograph developed for a variety of basin conditions in Georgia was tested for its applicability to streams in East and West Tennessee by comparing it to a similar dimensionless hydrograph developed for streams in East and West Tennessee. Hydrographs of observed discharge at 83 streams in East Tennessee and 38 in West Tennessee were used in the study. Statistical analyses were performed by comparing simulated (or computed) hydrographs, derived by application of the Georgia dimensionless hydrograph, and dimensionless hydrographs developed from Tennessee data, with the observed hydrographs at 50 and 75% of their peak-flow widths. Results of the tests indicate that the Georgia dimensionless hydrography is virtually the same as the one developed for streams in East Tennessee, but that it is different from the dimensionless hydrograph developed for streams in West Tennessee. Because of the extensive testing of the Georgia dimensionless hydrograph, it was determined to be applicable for East Tennessee, whereas the dimensionless hydrograph developed from data on streams in West Tennessee was determined to be applicable in West Tennessee. As part of the dimensionless hydrograph development, an average lagtime in hours for each study basin, and the volume in inches of flood runoff for each flood event were computed. By use of multiple-regression analysis, equations were developed that relate basin lagtime to drainage area size, basin length, and percent impervious area. Similarly, flood volumes were related to drainage area size, peak discharge, and basin lagtime. These equations, along with the appropriate dimensionless hydrograph, can be used to estimate a typical (average) flood hydrograph and volume for recurrence-intervals up to 100 years at any ungaged site draining less than 50 sq mi in East and West Tennessee.

Tennessee↗