Geology topics
Source-linked reports with geographic coverage including Conterminous United States.
Using 3D Elevation Program data, a modern illustration of the conterminous United States’ landscape showcases its elevation differences in vivid detail. This map has been consistently adjusted and colorized according to the highest and lowest elevations, accentuating differences across the conterminous United States. To further emphasize landforms, shaded relief has been added on top of the elevation colors.
Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.
Aim Non-native plants have the potential to harm ecosystems. Harm is classically related to their distribution and abundance, but this geographical information is often unknown. Here, we assess geographical commonness as a potential indicator of invasive status for non-native flora in the United States. Geographical commonness could inform invasion risk assessments across species and ecoregions. Location Conterminous United States. Time Period Through 2022. Major Taxa Studied Plants. Methods We compiled and standardised occurrence and abundance data from 14 spatial datasets and used this information to categorise non-native species as uncommon or common based on three dimensions of commonness: area of occupancy, habitat breadth and local abundance. To assess consistency in existing categorizations, we compared commonness to invasive status in the United States. We identified species with higher-than-expected abundance relative to their occupancy, habitat breadth or residence time. We calculated non-native plant richness within United States ecoregions and estimated unreported species based on rarefaction/extrapolation curves. Results This comprehensive database identified 1874 non-native plant species recorded in 4,844,963 locations. Of these, 1221 species were locally abundant (> 10% cover) in 797,759 unique locations. One thousand one hundred one non-native species (59%) achieved at least one dimension of commonness, including 565 species that achieved all three. Species with longer residence times tended to meet more dimensions of commonness. We identified 132 species with higher-than-expected abundance. Ecoregions in the central United States have the largest estimated numbers of unreported, abundant non-native plants. Main Conclusions A high proportion of non-native species have become common in the United States. However, existing categorizations of invasive species are not always consistent with species' abundance and distribution, even after considering residence time. Considering geographical commonness and higher-than-expected abundance revealed in this new dataset could support more consistent and proactive identification of invasive plants and lead to more efficient management practices.
Background Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments. Aims Few studies have compared BP maps with real-world fires to evaluate their suitability for near-future risk assessment. Here, we evaluated a BP map for the conterminous US based on the large fire simulation model FSim. Methods We compared BP with observed wildfires from 2016 to 2022 across 128 regions representing similar fire regimes (‘pyromes’). We evaluated the distribution of burned areas across BP values, and compared burned area distributions among fire size classes. Key results Across all pyromes, mean BP was moderately correlated with observed burned area. An average of 71% of burned area occurred in higher-BP classes, vs 79% expected. BP underpredicted burned area in the Mountain West, especially for extremely large fires. Conclusions The FSim BP map was useful for estimating subsequent wildfire hazard, but may have underestimated burned areas where input data did not reflect recent climate change, vegetation change or human ignition patterns. Implications Our evaluations indicate that caution is needed when relying on simulation-based BP maps to inform management decisions. Our results also highlight potential opportunities to improve model estimates.
Aim The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location Conterminous United States. Time Period 2010–2021. Major Taxa Studied Twenty-five species of mammals. Methods We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.
No abstract available.
This report explains the steps and specific methods used to predict fluvial fish occurrences in their native ranges for the conterminous United States. In this study, boosted regression tree models predict distributions of 271 ecologically important fluvial fish species using relations between fish presence/absence and 22 natural and anthropogenic landscape variables. Models developed for the freshwater portions of the ranges for species represented 28 families. Cyprinidae was the family with the most species (87 of 271) modeled for this study, followed by Percidae (34) and Ictaluridae (17). Model predictive performance was evaluated using four metrics: area under the receiver operating characteristic curve, sensitivity, specificity, and True Skill Statistic, which are all from tenfold cross-validation results. The relative importance of the predictor variables in the boosted regression tree models was calculated and ranked for each species. The three strongest natural predictors of fish distributions were network catchment area, the mean annual air temperature of the local catchment, and the maximum elevation of the local catchment, while the three strongest anthropogenic predictors were downstream main stem dam density, distance to downstream main stem dam, and the percentage of pasture/hay land use area within network catchment boundaries. Study results showed 61 fish species were sensitive to climate variables, and 40 fish species were sensitive to anthropogenic stressors. The models developed in this study can be used to derive critical information regarding habitat protection priorities, anthropogenic threats, and potential effects of climate change on habitat suitability, aiding in efforts to conserve fluvial fishes now and into the future.
This report documents a three-part continental-scale calibration procedure and a new streamflow routing algorithm using the U.S. Geological Survey National Hydrologic Model (NHM) infrastructure along with an application of the Precipitation-Runoff Modeling System (PRMS). The traditional approach to hydrologic model calibration and evaluation, which relies on comparing observed and simulated streamflow, is not sufficient for accurately representing the non-streamflow parts of the water budget. If intermediate process variables computed by the hydrologic model are not examined, the variables could be characterized by parameter values that do not replicate those hydrological processes present in the physical system. In answer to this potential problem, alternative hydrologic process variables from the model (in addition to streamflow) are included in a calibration procedure applied to the conterminous United States (CONUS) domain. The three-part calibration procedure presented in this report considers volume (calibration by hydrologic response unit [byHRU]), timing (calibration by headwater watershed [byHW]), and measured streamflow [byHWobs]). The first part, byHRU, is considered a water-balance volume calibration that uses five alternative (non-streamflow) hydrologic quantities (runoff, actual evapotranspiration, recharge, soil moisture, and snow-covered area) as calibration targets for each hydrologic response unit (HRU). These alternative data products were derived, with error bounds, from multiple sources for each of the 109,951 HRUs in the NHM on time scales varying from annual to daily. The second part of the calibration, byHW, is considered a streamflow timing calibration that uses statistically based streamflow simulations developed using ordinary kriging for 7,265 headwater watersheds that had drainage areas of less than 3,000 square kilometers (1,158 square miles) across the CONUS. Two streamflow routing algorithms were tested in this byHW calibration: (1) continuity without attenuation of the flood pulse and (2) a new formulation of the Muskingum routing method, which was added to the PRMS as part of this study. The third part of the calibration, byHWobs, refines the model parameters using available measured streamflow using 1,417 streamgage locations. A multiple-objective, stepwise, automated calibration procedure was used to identify the optimal set of parameters for each calibration procedure. Using a variety of alternative datasets for calibration of the water budget provides users of the NHM-PRMS with improved initial parameters and helps alleviate the equifinality problem (getting the right answer for the wrong reason). Through a community effort, these alternative data products, with error bounds, can be used to improve and expand our understanding of hydrologic-process representation in models. The broader modeling community can use these data products, with error bounds, to calibrate and evaluate hydrologic models using more than streamflow.
Changes in groundwater quality have been evaluated for more than 2,200 wells in 25 Principal Aquifers in the United States based on repeated decadal sampling (once every 10 years) from 1988 to 2021. The purpose of this study is to identify contaminants with changing concentrations, the locations and magnitude of those changes, the factors driving those changes, the obstacles to interpreting the changes, and approaches to ameliorate those obstacles. Sampling was conducted in 89 networks of 20–30 wells each that represent various geographic regions, aquifer types and land use types. Each network, and the wells that comprise them, are sampled on a rotating basis once every 10 years. Of the 28 constituents evaluated for trends, concentrations of Na, Cl, dissolved solids, SO 4 , and NO 3 exhibited statistically significant increases at the network level more frequently than other constituents. Factors affecting trends in Cl and NO 3 are emphasized in this study. Regional patterns show large increases of Cl in urban areas in the Northeast and Midcontinent, where road-deicing salt application rates are 10 to 100 times greater than in other regions of the country, and in semiarid and arid regions of the western United States, where evaporation concentrates solutes in recharge. The largest increases in NO 3 were in agricultural areas of the semiarid west, arid west and Pacific regions which are characterized by oxic groundwater, long-term increases in nitrogen fertilizer usage, and high rates of irrigation. However, finding a direct relation between increasing contaminant sources and corresponding groundwater quality response, particularly when sampling once every 10 years, can be complicated by factors such as uncertainty in the timing, mass, and location of contaminant sources, groundwater residence time (recharge date), geochemical conditions in the aquifer that affect contaminant transport, and variability in water quality due to climatic factors such as seasonality and hydrologic conditions. Understanding groundwater residence time allows the changes in groundwater quality to be evaluated in the context of recharge date rather than the sample date. Likewise, information on geochemical characteristics of the aquifer can be helpful for understanding relations between contaminant source inputs and groundwater concentrations. For example, oxic geochemical conditions in the aquifer may allow for conservative transport and accumulation of NO 3 in groundwater, whereas reducing environments could favor NO 3 degradation. Differences in hydrologic conditions (wetter or drier than average) on the date of sampling could impact the statistical results of sampling at decadal intervals and obscure long-term patterns. Samples collected under substantially different hydrologic conditions can be identified, and high-frequency sampling can improve interpretation of measured results in these cases. Although decadal sampling and associated water-quality interpretations have limitations, repeated, scheduled sampling of thousands of wells over multiple decades has great value for identifying and understanding long-term, regional groundwater-quality trends. Despite these limitations, the concepts presented herein provide options that could be used to interpret trends or changes when sampling over longer timespans, which is less common than trend networks with higher frequency sampling intervals.
Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Urban areas usually exhibit relatively warmer air and surface temperatures than surrounding non-urban lands, a phenomenon recognized as Surface Urban Heat Island (SUHI). As urban areas continue to develop and the climate continues to warm, it has become increasingly important to quantify and map the SUHI effect and learn how to mitigate it. To help meet the expanding need of analysis ready data for SUHI based studies, a methodology was developed to evaluate Land Surface Temperature (LST) using the Landsat Collection 1 Provisional Surface Temperature Science Product. The Landsat derived LST products were processed for 50 major cities throughout the Conterminous U.S. The SUHI product package includes per-pixel annual surface temperature, annual intensity, annual hotspot, and hotspot probability bands from 1985 to 2020.
Current time-invariant groundwater vulnerability assessments may not capture intermittent contamination events in landscape areas that experience rapid infiltration following precipitation or snowmelt. Occurrences of rapid infiltration and intermittent degradation of groundwater quality are frequently reported in fractured-rock aquifers. This investigation identifies landscape areas underlain by fractured rock within the conterminous United States (CONUS) that may be susceptible to rapid infiltration and where groundwater is a principal source of water supply to the population. Our analysis shows that approximately 27 percent of the CONUS, corresponding to a population of approximately 150 million people, is both underlain by fractured rock and denoted as an area of significant groundwater use. The results of this survey identified shallow fractured-rock aquifers underlying glacial sediments in the upper Midwest and northeastern United States as areas that may be subject to rapid infiltration events. Additionally, aquifers associated with the early Mesozoic basins located in the northeastern and mid-Atlantic United States and bands of carbonate aquifers in the southeastern United States show high susceptibility to rapid infiltration. Index values used in this investigation indicate isolated areas in the western half of the United States also show high susceptibility to rapid infiltration. The isolated areas in Oklahoma, Texas, Arkansas, and southwestern Missouri correspond to karst regions of carbonate aquifers. The isolated areas showing high susceptibility to rapid infiltration and contamination from agricultural sources are locations where more detailed investigations of transient contamination events are warranted. This survey also addresses the potential for contaminant longevity in fractured-rock aquifers stemming from intermittent contamination events. Contaminants that can dissolve into the groundwater following infiltration may be introduced into fractures, and the dissolved constituents can diffuse from fractures into the porosity of the adjacent rock matrix. These constituents can then diffuse back into permeable fractures and adversely affect groundwater quality at downgradient locations over an extended time frame. Rock types with larger matrix porosities have the capacity to retain and then release larger quantities of dissolved constituents, resulting in longer residence times for dissolved groundwater contaminants. The magnitude of the dissolved contaminant concentration infiltrating to the water table will also dictate whether the contaminant concentration in the groundwater exceeds limits for human consumption over the duration of a contamination event. In general, sedimentary- and carbonate-rock aquifers have larger matrix porosities in comparison to igneous- and metamorphic-rock aquifers, and thus, they are more susceptible to longer contaminant residence times. Aquifers composed of sedimentary or carbonate rock constitute approximately 51 percent of the CONUS, and 19 percent of the CONUS is associated with sedimentary- or carbonate-rock aquifers that are of significance for groundwater use. Depending on the contaminants of concern and the concentration of the contaminants introduced into the groundwater from infiltrating water, it would be beneficial for investigations of susceptibility to rapid infiltration to also consider the potential for contaminant longevity. This investigation identifies areas of rapid infiltration into fractured rock using index values applied to the attributes (1) depth to the water table, (2) depth to bedrock, and (3) percentage of sand in soil, where larger index values indicate a greater susceptibility to rapid infiltration. These attributes are selected as the most likely factors that affect rapid infiltration to the water table. The combination of depth to water table and depth to bedrock highlight those aquifer settings that are characterized as shallow fractured-rock aquifers, where the water table may reside either in the bedrock or in overlying unconsolidated geologic materials. In addition, we consider the percentage of agricultural use as a land-use attribute when formulating an index of susceptibility to rapid infiltration and contamination. Agricultural areas are well recognized as nonpoint sources of contaminants that can affect groundwater quality because of seasonal amendments applied to the land surface. Rural agricultural areas are also characterized by septic tanks and leach fields for onsite treatment of wastewater, which may also be a source of contamination that may be introduced into the groundwater following precipitation or snowmelt events.
The U.S. Geological Survey has a long history of leading flood-frequency analysis studies. These studies play a critical role in the assessment of risk, protection of lives, and planning and design of flood protection infrastructure. Standard flood-frequency analysis is based on the assumption of stationarity—that is, that the distribution of floods at a given site varies around a particular mean within a particular envelope of variance (and skew) and that these parameters of the underlying statistical distribution representative of the floods do not vary over time. Gradual or abrupt changes in one or more of the distributional parameters are called nonstationarities and violate the underlying assumptions of current U.S. Federal Government guidelines for flood-frequency analysis. Uncertainty exists as to what degree of violations calls for the use of a modified method for flood-frequency analysis and what the modified method(s) should be. When deciding whether to perform nonstationary flood-frequency analysis and choosing a method for such analysis, it is important to understand the causes of the nonstationarity. Gradual or abrupt changes in distributional properties of floods may be the result of numerous factors, such as regulation, diversion, land-use change, or climate change. In the interest of developing a cohesive national approach for better understanding the causes of nonstationarities and incorporating potential or observed changes into flood-frequency estimates, subject-matter experts from the U.S. Geological Survey and cooperators worked together to develop a multiple working hypotheses framework for making attributions and a common vocabulary for making provisions of confidence. Seven regional teams of these experts used ancillary datasets and institutional knowledge to evaluate plausible causes for monotonic trends and change points in annual peak-streamflow data for the conterminous United States that had been identified in an earlier phase of the project. The first chapter of this professional paper describes the development of a list of the potential attributions, presents a literature review of the potential attributions, describes the regional approach, summarizes insights obtained from the attribution process, and suggests future research. The other chapters provide the methods used for attribution in the seven regions—Pacific Northwest, Upper Plains, Midwest, Northeast, Southwest, South-Central, and Southeast—and summarize the regional patterns of nonstationarities.
Waste isolation is a key strategy for mitigating risk from municipal solid waste (MSW) and hazardous waste streams. Conventional covers at MSW facilities are designed for a 30-yr post-closure period where compacted soils and geosynthetics are used to minimize percolation into buried waste. Recently, evapotranspiration (ET) covers have shown beneficial use for MSW management. Evapotranspiration covers encourage infiltration, storage, and transpiration of precipitation to minimize percolation. Such covers may also have beneficial use for long-term waste issues, such as at Uranium Mill Tailings Radiation Control Act (UMTRCA) sites. These sites were covered by a clay radon barrier to create tortuous flow paths that allow radioactive decay and attenuation of short-lived, radon-222 gas. For long-term waste isolation, an ET-radon cover may provide greater resilience by exploiting natural processes instead of resisting them. This update presents a review of the current state-of-the-science regarding ET covers and considerations for long-term applications.
Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities, has increased the demand for information on forest regrowth and recovery following natural and anthropogenic disturbances (e.g., fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human interpreted annual reference data that were collected independently. The validation results show one-year of underestimation and 6-year standard deviation of error between the reference data and regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.
The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous U.S. based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint dataset from Microsoft®. We classified WUI across the conterminous U.S. at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6% – 18.8% of the conterminous U.S. as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail, and can be widely applied to take advantage of the growing availability of high-resolution building footprint datasets and classification methods.
Extensive and severe droughts have substantial effects on water supplies, agriculture, and aquatic ecosystems. To better understand these droughts, we used tree-ring-based reconstructions of the Palmer drought severity index (PDSI) for the period 1475–2017 to examine droughts that covered at least 33% of the conterminous United States (CONUS). We identified 37 spatially extensive drought events for the CONUS and examined their spatial and temporal patterns. The duration of the extensive drought events ranged from 3 to 12 yr and on average affected 43% of the CONUS. The recent (2000–08) drought in the southwestern CONUS, often referred to as the turn-of-the-century drought, is likely one of the longest droughts in the CONUS during the past 500 years. A principal components analysis of the PDSI data from 1475 through 2017 resulted in three principal components (PCs) that explain about 48% of the variability of PDSI and are helpful to understand the temporal and spatial variability of the 37 extensive droughts in the CONUS. Analyses of the relations between the three PCs and well-known climate indices, such as indices of El Niño–Southern Oscillation, indicate statistically significant correlations; however, the correlations do not appear to be large enough (all with an absolute value less than 0.45) to be useful for the development of drought prediction models.
The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.
This report and associated dataset provide tabular county-level estimates of kilograms of nitrogen and phosphorus generated from two sources: (a) fertilizer from commercial sources and (b) livestock-based manure, for the period 1950 through 2017 for the conterminous United States. Datasets collected during this time span are for intervals of approximately 5 years that coincide with the U.S. Department of Agriculture’s census years. Nutrients from fertilizer for 1950–2012 are exactly those previously described in U.S. Geological Survey (USGS) reports and datasets; however, estimates of nutrient masses from fertilizer applied in 2017 are described here as a new product modeled from 11 predictor variables for 2017 including county-level fertilizer expenditures, land use, and acres of fertilized land. Fertilizer-based estimates are provided for both farm and nonfarm (urban) usage. The estimates of nutrients from manure for all years were generated anew by using methods and formulas described in previous USGS reports but adjusted to account for historical changes in the annual averages of animal weights. The tabular data are available as an accompanying data release.