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Research about conterminous United States

Source-linked reports with geographic coverage including conterminous United States.

At least 55 records · Page 3Linked to original sources
USGS2025

Rising rates of wildfire building destruction in the conterminous United States

Many regions of the world have seen an increase in highly destructive wildfires, driven by well-documented increases in burned area and growth of housing in the wildland–urban interface (WUI), which exposes more homes to fire. However, it is unclear whether wildfires are also becoming more destructive due to changes in wildfire behavior or in the development patterns of exposed communities. Here, we assessed trends in wildfire building exposure and destruction rates in the conterminous United States from 2002 to 2022. We mapped destroyed and surviving buildings within 100 m of all wildfires that destroyed 10 or more buildings (n = 362) and assessed trends relative to major ecoregions and vegetation types. We used logistic regression to assess relationships between destruction rates and landscape factors. We found that 10% of exposed buildings were destroyed in 2002–2012, but this percentage increased to 32% in 2013–2022. This increase was largely due to greater building exposure in evergreen forests in the northwestern United States, where exposed buildings were more than 3.4 times as likely to be destroyed as those in grass and shrublands. However, annual destruction rates also significantly increased in all other vegetation types and were correlated with development type, weather, and burn severity. These results indicate that increasing wildfire destruction in the United States has resulted not only from increased exposure but from rising rates of building destruction, potentially indicating more extreme wildfire behavior. This finding underscores the need to better understand how fuel management, community planning, and hardening buildings can reduce vulnerability.

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USGS2025

Offsetting the noise: A framework for applying phenological offset corrections in remotely sensed burn severity assessments

Background Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs; however, efficacy across diverse scenarios remains underexplored. Aims We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments. Methods We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous US. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds and potential bias. Key results Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence with observed burn severity categories. Conclusions Offset corrections offer benefits but also present trade-offs to accurately representing remotely sensed burn severity. Implications The utility of offset corrections depends on the environment, methods and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.

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USGS2025

Harmonization of a water withdrawal dataset for the conterminous United States

The U.S. Geological Survey is developing nationally consistent water-use modeling approaches to replace previous methods relying on locally specific reported and estimated data. These national assessments require datasets that incorporate water withdrawal variability across the United States and over long periods. However, source data often have unclear definitions, missing or varied units, differing temporal resolutions, varied data quality, and inconsistent formats, which hinder automation and require individualized processing. The public-supply datasets described in this paper were used in machine learning models to estimate annual and monthly public-supply water use for 2000–2020 for the conterminous United States (CONUS) and in a model to estimate public-supply deliveries. Public-supply withdrawal data were acquired for the CONUS and the District of Columbia; however, 11 states had annual data for only 1 year, and 10 states had no monthly data. Annual withdrawal data were acquired for 81% of public-supply water service areas, and monthly withdrawal data were acquired for 47% for at least 1 year from 2000 to 2020. These datasets and methods provide the most comprehensive collection of reported public-supply withdrawals to date and can be used by water-use managers, the scientific community, and the broader public. The extensive data processing described herein can be applicable to datasets representing other categories of water use.

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USGS2025

The rise of U.S. photovoltaics: An exploration of land use density trends in large-scale solar facility attributes, 2012-2021

An energy transition is underway in the United States; renewable energy generation is now on par with coal and nuclear generation. The number of large-scale solar photovoltaic facilities increased approximately tenfold between 2012 and 2021, with an associated 25-fold increase in cumulative installed capacity. With ambitious decarbonization and renewable energy deployment goals at both the federal and state levels, deployments of large-scale solar photovoltaic facilities will continue apace. This growth is likely to be complex with ripples of impacts felt throughout different aspects of society, and thus accurate solar land use metrics allowing more accurate predictions are of value to policymakers, planners, and other stakeholders in the future photovoltaic build-out. In this paper, we leverage data from the newly released US Large-Scale Solar Photovoltaic Database to examine recent trends in large-scale solar photovoltaic land use. We analyze the relationships between solar array capacity density (W/acre) and a range of facility attributes to better understand the future land requirements of solar capacity expansion over the coming years. Installed capacity was the single strongest determinant of solar array area. However, we found substantial variation in capacity density across facility attributes, including mount type, latitude, urbanicity, time, and prior land use.

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USGS2025

Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud

Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed and irrigated area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel based supervised random forest classifier, and pixel based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer's accuracies of 90.2% and 95.7%, respectively, and user's accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (68%) was classified as rainfed and 44.5 Mha (32%) was classified as irrigated. State level summaries highlight regional differences and their implications for national and global food and water security.

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USGS2025

An automated compositing method for producing annual clear images from Landsat Collection 2 for annual NLCD production

Quality image input is fundamental to the quality of derived land cover products. Substantial time and effort are usually required to prepare images. Here, we present a novel and streamlined compositing algorithm that ingests Landsat Collection 2 Analysis Ready Data (ARD) and outputs cloud-free and gap-free composite imagery, which can be directly used for classification. This method leverages and improves the previous National Land Cover Database (NLCD) Virtual Median Value Point (VMVP) compositing method, the first part of the image preparation for NLCD 2019 operational production. The NLCD 2019 image preparation approach includes a second part, a residual cloud and cloud shadow detection and gap-filling method, to produce final cloud-free and gap-free composite imagery. The second part requires one clear reference image for each target year. Additional reference images are needed for producing reasonable observations for perennial ice/snow areas because Pixel QA (Quality Assessment) from ARD has difficulties differentiating ice/snow areas from clouds. Unlike the NLCD 2019 image preparation approach, our new compositing method, which is referred to as Automated VMVP (AVMVP), uses Landsat ARD as the only input and does not require reference images and extra steps. In this method, we developed new spectral filter criteria coupled with counts of clear observations using Pixel QA to identify potential cloud and cloud shadow observations on initially selected observations from the NLCD VMVP compositing algorithm. We also automate “gap-filling” using clear observations retrieved from a maximum of ±2 years around the target year when needed. Finally, a percentile-filtered compositing method was developed for the perennial ice/snow areas. All these steps are streamlined, pixel-based, and directly run on Landsat Collection 2 ARD. We have run successful tests on the conterminous United States (CONUS). Composite images derived from our innovative method were used to produce the CONUS Annual NLCD Collection 1 product suite that covers the period from 1985 to 2023.

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USGS2025

Water withdrawal and consumption trends for thermoelectric-power plants in the conterminous United States, 2008-2020

Freshwater-using utility-scale thermoelectric (TE) plant water-use estimates were evaluated for annual trends from 2008 to 2020 across the conterminous United States (CONUS) and within hydrologic regions. Overall, TE water withdrawal and consumption trends declined across CONUS by 14,335 and 278 million liters/day, respectively. Decreasing water withdrawal and consumption trends for TE plants are driven largely by switching from coal-fired plants to other generation technologies. TE plant cooling system technology has also changed, with large declining trends for TE plants using once-through cooling systems and small increasing consumption trends for TE plants using recirculating tower cooling systems. Fifteen hydrologic regions have decreasing trends in withdrawals and consumption. The largest decreases are for coal-fired plants using once-through freshwater cooling systems in the Great Lakes and Ohio hydrologic regions. Natural gas combined cycle plants with recirculating tower cooling systems have increased water consumption trends across most of the CONUS hydrologic regions. Some TE plants with recirculating tower or once-through cooling systems withdraw water volumes that on average are close to or exceed average simulated streamflows. Most of these situations occur in the central and eastern U.S., potentially leading to water availability issues among competing water needs, ecosystem impacts from thermal pollution, and power generation constraints.

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USGS2025

Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning

Large-scale hydrologic models are increasingly being developed for operational use in the forecasting and planning of water resources. However, the predictive strength of such models depends on how well they resolve various functions of catchment hydrology, which are influenced by gradients in climate, topography, soils, and land use. Most assessments of hydrologic model uncertainty have been limited to traditional statistical methods. Here, we present a proof-of-concept approach that uses interpretable machine learning techniques to provide post hoc assessment of model sensitivity and process deficiency in hydrologic models. We train a random forest model to predict the Kling–Gupta efficiency (KGE) of National Water Model (NWM) and National Hydrologic Model (NHM) streamflow predictions for 4383 stream gauges in the conterminous United States. Thereafter, we explain the local and global controls that 48 catchment attributes exert on KGE prediction using interpretable Shapley values. Overall, we find that soil water content is the most impactful feature controlling successful model performance, suggesting that soil water storage is difficult for hydrologic models to resolve, particularly for arid locations. We identify nonlinear thresholds beyond which predictive performance decreases for NWM and NHM. For example, soil water content less than 210 mm, precipitation less than 900 mm yr −1 , road density greater than 5 km km −2 , and lake area percent greater than 10 % contributed to lower KGE values. These results suggest that improvements in how these influential processes are represented could result in the largest increases in NWM and NHM predictive performance. This study demonstrates the utility of interrogating process-based models using data-driven techniques, which has broad applicability and potential for improving the next generation of large-scale hydrologic models.

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USGS2025

A synthesis engine for constructing geologic maps of the United States

The geologic history of the United States is cataloged in thousands of geologic maps produced during many decades. However, the disparate nature of these individual maps makes it challenging to assess resources, research geologic histories, or characterize natural hazards holistically across the Nation. The U.S. House of Representatives 2020 appropriations bill for the U.S. Department of the Interior (H.R. 116-100) requires the U.S. Geological Survey to “bring together detailed national and continental-resolution [two-dimensional] and [three-dimensional] information produced throughout the Survey and by [F]ederal and [S]tate partners.” In response to this directive, this report presents a compilation and synthesis of geologic maps across the United States in the form of a relational database. The synthesis database includes thematic maps that synthesize the Nation’s geology, and retains the original input maps as well as linkages to standardized vocabularies to aid the discoverability of geologic information. Specifically, the synthesis database is targeted toward producing four National-resolution maps for the conterminous United States: Quaternary geology, the geology at the Earth’s surface, pre-Quaternary geology, and Precambrian geology. In addition, the synthesis database includes the infrastructure necessary to expand to additional resolutions in the future.

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USGS2025

Economic costs of invasive carps in the United States: Case study and management implications

Biological invasions can have far-reaching impacts and incur enormous monetary costs. Economic considerations play an important role in management decision-making. We used the invasion of U.S. waterways by silver ( Hypophthalmichthys molitrix ) and bighead ( H. nobilis ) carp as a case study of the costs of aquatic invasive species. Although these carps are well-known invaders, published reports on their economic costs are lacking. Our study included market values for commercial fisheries, non-market values for recreational fisheries, and management costs. Our results showed that by 2020, U.S. federal and state agencies had spent nearly $592 million in cumulative management costs. A difference-in-difference model testing for the effect of invasive carp on commercial harvest in invaded versus uninvaded reaches of the Mississippi and Illinois Rivers showed no statistical significance. A benefit transfer analysis of invasion effects on total economic value of recreational fishing, an important ecosystem service, in a heavily invaded section of the Illinois River estimated a total loss of more than $10 million over 10 years. While there are other known impacts on ecosystem services, including alteration of aquatic food webs, plankton communities, and native fish communities, these could not be quantified in economic terms in our analysis.

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USGS2025

Why are non-native plants successful? Consistently fast economic traits and novel origin jointly explain abundance across US ecoregions

Are non-native plants abundant because they are non-native, and have advantages over native plants, or because they possess ‘fast’ resource strategies, and have advantages in disturbed environments? This question is central to invasion biology but remains unanswered. We quantified the relative importance of resource strategy and biogeographic origin in 69 441 plots across the conterminous United States containing 11 280 plant species. Non-native species had faster economic traits than native species in most plant communities (77%, 86% and 82% of plots for leaf nitrogen concentration, specific leaf area, and leaf dry matter content). Non-native species also had distinct patterns of abundance, but these were not explained by their fast traits. Compared with functionally similar native species, non-native species were (1) more abundant in plains and deserts, indicating the importance of biogeographic origin, and less abundant in forested ecoregions, (2) were more abundant where co-occurring species had fast traits, for example due to disturbance, and (3) showed weaker signals of local environmental filtering. These results clarify the nature of plant invasion: Although non-native plants have consistently fast economic traits, other novel characteristics and processes likely explain their abundance and, therefore, impacts.

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USGS2025

Public supply water delivery analysis and estimation for the conterminous United States

Public supply water withdrawals represent 14% of all withdrawals in the conterminous United States (CONUS), supplying approximately 87% of the population with fresh water. Deliveries for public water supply are crucial for associating water use amounts with populations because they often differ from total withdrawals due to wholesales, transfers, losses, and other factors. Understanding these differences helps identify the drivers for each type of delivery. The goal of this study was to compile all available public water supply delivery data for the CONUS and develop a data-driven model to estimate deliveries for all water service areas within the CONUS. Annual deliveries were estimated between 2010 and 2020, encompassing total water deliveries; combined commercial, industrial, and institutional deliveries (CII); and domestic deliveries. Data were compiled for 2,744 water service areas to produce the most comprehensive public water supply delivery data set for the CONUS to date. Three ensemble modeling approaches were developed to estimate total, CII, and domestic per capita (DPC) deliveries using a gradient boosted regression tree modeling approach. Estimates of daily domestic and CII per capita deliveries were generated from these models for approximately 18,800 water service areas, covering most public water systems in the CONUS. Domestic delivery was found to be lowest in the midwestern region and higher in the southern and southwest regions of the United States. Results indicate that climate and land use can be associated with regional differences in DPC delivery. Population metrics and land use were identified as significant contributors to CII delivery estimates.

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USGS2025

Drought and deluge— Opportunities for climate-change adaptation in US national parks

In a changing climate, resource management depends on anticipating changes and considering uncertainties. To facilitate effective decision making on public lands, we regionally summarized the magnitude and uncertainty of projected change in management-relevant climate variables for 332 national park units across the contiguous US. Temperature, frequency of extreme precipitation events, and drought exposure are all projected to increase within seven regions delineated in the US National Climate Assessment. In particular, the anticipated collective impacts of droughts and flooding events will lead to unique management challenges, including combinations of management actions that may seem inconsistent. Furthermore, uncertainty in the magnitude of change varied by region and climate variable considered, pointing to specific opportunities for prioritization, transferability, and innovation of climate adaptation regionally and at the park-unit scale.

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USGS2025

Leveraging detection uncertainty to estimate Renibacterium salmoninarum infection status among multiple tissues and assays

Effective disease surveillance relies on accurate pathogen testing and robust prevalence estimates. Diagnostic specificity (DSp), the probability that an uninfected animal tests negative, is high when false positives are low. Diagnostic sensitivity (DSe) is the probability an infected animal tests positive; higher DSe means fewer false negatives. However, sensitivity and false negatives are harder to estimate without a "gold standard", an assay that can detect between 90 - 100% of true positive infections. Occupancy estimation of infection prevalence offers one solution by allowing for imperfect detection of the pathogen. Testing potentially infected tissues multiple times allows for the use of a Bayesian multistate occupancy model to estimate the probability of pathogen infection in tissues [Formula: see text] and detection probabilities [Formula: see text] for different assays. Using [Formula: see text] and [Formula: see text] from the posterior distribution, the conditional probability of detecting the pathogen can be modeled, allowing for the calculation of DSe. Renibacterium salmoninarum is a bacterial pathogen causing bacterial kidney disease among salmonid species and was the model pathogen we used to train our model. The current testing standard for salmonids combines initial screening for antibodies using direct fluorescent antibody test (DFAT) with polymerase chain reaction (PCR) confirmation to detect R. salmoninarum. However, detection of R. salmoninarum still varies between species, tissues, and assays. Here, a multi-state occupancy model was used to estimate detection probability among individual and dual kidney/liver infections with DFAT and qPCR in fish with an unknown infection status. Both assays produced false negatives, but qPCR had fewer than DFAT and a higher DSe. Infection state was often misclassified, but multiple surveys per individual or combining tissues for testing improved DSe for both assays.

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USGS2025

A crustal thermal model of the conterminous U.S. constrained by multiple data sets: A Monte-Carlo approach

The thermal structure of the continental crust plays a critical role in understanding its elastic and rheologic properties as well as its dynamic processes. Thermal parameter data sets on continental scales have been used to constrain the crustal thermal structure, including both the direct (e.g. temperature, heat flux and heat conductivity measured at the surface) and indirect (e.g. seismically derived Mohorovičić discontinuity (Moho) temperature, geomagnetically derived Curie depth) observations. In this study, we present a new continental scale crustal heat generation model with additional information from seismologically inferred crustal composition. Together with previous direct and indirect thermal parameter data sets in the conterminous United States, we use the new crustal heat generation model to construct a 3-D crustal temperature model under a newly developed Bayesian framework. Specifically, we first derive profiles of crustal heat generation based on an empirical geochemical relationship at 1683 locations where seismologically derived crustal composition information is available. Then for each of these locations, the average heat generation values in the upper, middle and lower crust are combined with other thermal parameters through a Markov Chain Monte-Carlo inversion for a conductive, vertically smooth temperature profile. The results, posterior distributions of temperature profiles, are used to generate a 3-D crustal thermal model with the uncertainties systematically assessed. The new temperature model overall exhibits similar patterns to that from the U.S. Geological Survey National Crustal Model, but also reduces possible biases and the model's dependence on a single thermal parameter.

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USGS2025

An enhanced national-scale urban tree canopy cover dataset for the United States

Moderate-resolution (30-m) national map products have limited capacity to represent fine-scale, heterogeneous urban forms and processes, yet improvements from incorporating higher resolution predictor data remain rare. In this study, we applied random forest models to high-resolution land cover data for 71 U.S. urban areas, moderate-resolution National Land Cover Database (NLCD) Tree Canopy Cover (TCC), and additional explanatory climatic and structural data to develop an enhanced urban TCC dataset for U.S. urban areas. With a coefficient of determination (R 2 ) of 0.747, our model estimated TCC within 3% for 62 urban areas and added 13.4% more city-level TCC on average, compared to the native NLCD TCC product. Cross validations indicated model stability suitable for building a national-scale TCC dataset (median R 2 of 0.752, 0.675, and 0.743 for 1,000-fold cross validation, urban area leave-one-out cross validation, and cross validation by Census block group median year built, respectively). Additionally, our model code can be used to improve moderate-resolution TCC in other parts of the world where high-resolution land cover data have limited spatiotemporal availability.

conterminous United States↗
USGS2025

Annual NLCD (National Land Cover Database)—The next generation of land cover mapping

Introduction The widely used National Land Cover Database (NLCD) has long been the foundational land cover source for scientists, resource managers, and decision makers across the United States. In 2024, a reinvention as Annual NLCD added the key improvement of annual time steps to show decades of change at a higher frequency than the intervals of 2–3 years used in the legacy NLCD. Annual NLCD was derived primarily from the long Landsat satellite data record, and it includes data from other sources. The first release in 2024 of Annual NLCD provides Collection 1.0 of products encompassing land cover and land change from 1985 through 2023 for the conterminous United States (CONUS). The Annual NLCD Collection 1.0 consists of six operational products that map the unique characteristics of land cover. A map created from Annual NLCD shows 16 land cover classes for the CONUS in 2023.

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