SEARCH · Search USGS

Results for “Statistical Methods & Applications”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20Linked to original sources

USGS2018

Statistical detection of flow regime changes in horizontal hydraulically fractured Bakken oil wells

The application of horizontal and hydraulically fractured wells for producing oil from low permeability formations has changed the face of the North American oil industry. One feature of the production profile of many such wells is a transition from transient linear oil flow to boundary-dominated flow. The identification of the time of this transition is important for the calibration of models that forecast the well’s future production and the expected ultimate recovery. It is preferable that such models generally use data from the boundary-dominated flow regime for parameter calibration. Accurate well production forecasts are needed for operational decisions, long-term planning, commercial transactions, regulatory proceedings, and asset valuation. Petroleum engineers frequently make the call on the transition point based on subjective visual interpretations of log–log plots for individual wells. This is time-consuming and is generally not repeatable by other analysts. This note evaluates statistical approaches that can serve as alternatives to the subjective visual interpretations. Specifically, the predictive performance of production models calibrated with boundary-dominated data based on transition dates calculated with constrained nonlinear least squares and Bayesian regressions was very close to that obtained using the visual method, suggesting that statistical approaches may indeed be constructed to replace less objective visual approaches without loss of accuracy.

Natural Resources Research↗

USGS2010

A solute flux approach to transport in heterogeneous formations: 1. The general framework

It is common to represent solute tranport in heterogeneous formations in terms of the resident concentration C (x, t), regarded as a random space function. The present study investigates the alternative representation by q , the solute mass flux at a point of a control plane normal to the mean flow. This representation is appropriate for many field applications in which the variable of interest is the mass of solute discharged through a control surface. A general framework to compute the statistical moments of q and of the associated total solute discharge Q and mass M is established. With x the direction of the mean flow, a solute particle is crossing the control plane at y = η, z = ζ and at the travel (arrival) time τ. The associated expected solute flux value is proportional to the joint probability density function (pdf) g 1 of η, ζ and τ, whereas the variance of q is shown to depend on the joint pdf g 2 of the same variables for two particles. In turn, the statistical moments of η, ζ and τ depend on those of the velocity components through a system of stochastic ordinary differential equations. For a steady velocity field and neglecting the effect of pore‐scale dispersion, a major simplification of the problem results in the independence of the random variables η, ζ and τ. As a consequence, the pdf of η and ζ can be derived independently of τ. A few approximate approaches to derive the statistical moments of η, ζ and τ are outlined. These methods will be explored in paper 2 in order to effectively derive the variances of the total solute discharge and mass, while paper 3 will deal with the nonlinear effect of the velocity variance upon the moments of η, ζ and τ

Water Resources Research↗

USGS2020

Economic valuation of health benefits from using geologic data to communicate radon risk potential

Background: Radon exposure is the second leading cause of lung cancer worldwide and represents a major health concern within and outside the United States. Mitigating exposure to radon is especially critical in places with high rates of tobacco smoking (e.g., Kentucky, USA), as radon-induced lung cancer is markedly greater among people exposed to tobacco smoke. Despite homes being a common source of radon exposure, convincing homeowners to test and mitigate for radon remains a challenge. A new communication strategy to increase radon testing among Kentucky homeowners utilizes fine-scale geologic map data to create detailed radon risk potential maps. We assessed the health benefits of this strategy via avoided lung cancer and associated premature mortality and quantified the economic value of these benefits to indicate the potential utility of using geologic map data in radon communication strategies. Methods: We estimated the change in radon testing among all 120 counties in Kentucky following a new communication strategy reliant on geologic maps. We approximated the resultant potential change in radon mitigation rates and subsequent expected lung cancer cases and mortality avoided among smokers and non-smokers exposed to ≥4 pCi/L of radon in the home. We then applied the value of a statistical life to derive the economic value of the expected avoided mortality. Results: The new communication strategy is estimated to help 75 Kentucky residents in one year avoid exposure to harmful radon levels via increased testing and mitigation rates. This equated to the potential avoidance of approximately one premature death due to lung cancer, with a net present value of \$3.4 to \$8.5 million (2016 USD). Conclusions: Our analysis illustrates the potential economic value of health benefits associated with geologic map data used as part of a communication strategy conveying radon risk to the public. Geologic map data are freely available in varying resolutions throughout the United States, suggesting Kentucky’s radon communication strategy using geologic maps can be employed in other states to educate the public about radon. As this is only a single application, in a single state, the economic and health benefits of geologic map data in educating the public about radon are likely to exceed our estimates.

Kentucky↗

USGS1994

Estimating breeding proportions and testing hypotheses about costs of reproduction with capture-recapture data

The proportion of animals in a population that breeds is an important determinant of population growth rate. Usual estimates of this quantity from field sampling data assume that the probability of appearing in the capture or count statistic is the same for animals that do and do not breed. A similar assumption is required by most existing methods used to test ecologically interesting hypotheses about reproductive costs using field sampling data. However, in many field sampling situations breeding and nonbreeding animals are likely to exhibit different probabilities of being seen or caught. In this paper, we propose the use of multistate capture-recapture models for these estimation and testing problems. This methodology permits a formal test of the hypothesis of equal capture/sighting probabilities for breeding and nonbreeding individuals. Two estimators of breeding proportion (and associated standard errors) are presented, one for the case of equal capture probabilities and one for the case of unequal capture probabilities. The multistate modeling framework also yields formal tests of hypotheses about reproductive costs to future reproduction or survival or both fitness components. The general methodology is illustrated using capture-recapture data on female meadow voles, Microtus pennsylvanicus. Resulting estimates of the proportion of reproductively active females showed strong seasonal variation, as expected, with low breeding proportions in midwinter. We found no evidence of reproductive costs extracted in subsequent survival or reproduction. We believe that this methodological framework has wide application to problems in animal ecology concerning breeding proportions and phenotypic reproductive costs.

Ecology↗

USGS2016

Establishing a pre-mining geochemical baseline at a uranium mine near Grand Canyon National Park, USA

During 2012, approximately 404,000 ha of Federal Land in northern Arizona was withdrawn from consideration of mineral extraction for a 20-year period to protect the Grand Canyon watershed from potentially adverse effects of U mineral exploration and development. The development, operation, and reclamation of the Canyon Mine during the withdrawal period provide an excellent field site to understand and document off-site migration of radionuclides within the withdrawal area. As part of the Department of Interior's (DOI's) study plan for the exclusion area, the objective of our study is to utilize pre-defined decision units (DUs) in areas within and surrounding the Canyon Mine to demonstrate how newly established incremental sampling methodologies (ISM) combined with multivariate statistical methods can be used to document a repeatable and statistically defensible measure of pre-mining baseline conditions in surface soils and stream sediment samples prior to ore extraction. During the survey in June 2013, the highest pre-mining 95% upper confidence level (UCL) concentrations with respect to As, Mo, U, and V were found in the triplicate samples collected from surface soils in the mine site DU designated as M1. Gamma activities were slightly elevated in soils within the M1 DU (up to 28 μR/h); however, off-site gamma activities in soil and stream-sediment samples were lower (< 6 to 12 μR/h). Hierarchical cluster analysis (HCA) was applied to 33 chemical constituents contained in the multivariate data generated from the analysis of triplicate samples collected in the soil and stream sediment DUs within and surrounding Canyon Mine. Most of the triplicate samples from individual DUs were grouped in the same dendrogram cluster when using a similarity value (SV) of 0.70 (unitless). Different group membership of triplicate samples from two of the four haul road DUs was likely the result of heterogeneity induced by non-native soil material introduced from the gravel road base or from vehicular traffic. Application of HCA and ISM will provide critical metrics to meet DOI's long-term goals for assessing off-site migration of radionuclides resulting from mining and reclamation in the current (2015) exclusion area associated within the Grand Canyon watershed and the associated national park.

Arizona↗

USGS2026

Spatio-temporal modeling for assessing geoenergy resources: A workflow applied to gas in place variation in coal beds

The ability to estimate spatio-temporal changes in hydrocarbon reservoir properties and energy resources within pore volumes is essential for optimizing production, reservoir management, geologic energy storage, and safety in underground mining operations. In coal seams, predicting remaining methane gas-in-place (GIP) is critical for quantifying producible gas and improving mine safety and productivity through effective ventilation planning. Although such changes are commonly evaluated using physics-based numerical simulation models, these approaches often require extensive data, calibration effort, and time. This study presents a spatio-temporal geostatistical modeling approach that bridges the gap between purely spatial models and full numerical simulations. The method is applied to a case study of coal seam degasification in the Mary Lee coal group, Black Warrior Basin, Alabama, USA, to estimate GIP evolution over time within a selected mining district. The analysis uses published data from prior natural gas production history-matching of degasification using vertical wells. Empirical spatial and temporal statistics were calculated for reservoir pressure and water saturation, and spatio-temporal variogram models were fitted to experimental variograms. These models provided the structural basis for spatio-temporal kriging, integrated with spatial estimates of time-invariant parameters (porosity, density, and thickness) to estimate GIP. This approach enabled estimation of GIP changes over time, including periods without data. Boxplots of GIP estimates indicated systematic depletion and decreasing spatial variability, reflecting the impacts of degasification. Comparison with cumulative gas production from empirical well records showed approximately 85% agreement based on a relative similarity metric. Spatio-temporal GIP estimates were also used to estimate methane emissions to longwall ventilation systems and compared with reported emissions from the U.S. EPA Greenhouse Gas Reporting Program, showing similar distributions (≈80%) given data limitations. Overall, this integrated modeling approach provides time-dependent GIP estimates with broader implications for resource assessment applications.

Alabama↗

USGS2023

Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling

Background Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods. Methods We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment. Results Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.

PeerJ↗

USGS2018

Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts

Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads ( Rhinella marina ) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

Ecosphere↗

USGS2007

Concerns regarding a call for pluralism of information theory and hypothesis testing

1. Stephens et al. (2005) argue for 'pluralism' in statistical analysis, combining null hypothesis testing and information-theoretic (I-T) methods. We show that I-T methods are more informative even in single variable problems and we provide an ecological example. 2. I-T methods allow inferences to be made from multiple models simultaneously. We believe multimodel inference is the future of data analysis, which cannot be achieved with null hypothesis-testing approaches. 3. We argue for a stronger emphasis on critical thinking in science in general and less reliance on exploratory data analysis and data dredging. Deriving alternative hypotheses is central to science; deriving a single interesting science hypothesis and then comparing it to a default null hypothesis (e.g. 'no difference') is not an efficient strategy for gaining knowledge. We think this single-hypothesis strategy has been relied upon too often in the past. 4. We clarify misconceptions presented by Stephens et al. (2005) . 5. We think inference should be made about models, directly linked to scientific hypotheses, and their parameters conditioned on data, Prob(Hj| data). I-T methods provide a basis for this inference. Null hypothesis testing merely provides a probability statement about the data conditioned on a null model, Prob(data |H0). 6. Synthesis and applications . I-T methods provide a more informative approach to inference. I-T methods provide a direct measure of evidence for or against hypotheses and a means to consider simultaneously multiple hypotheses as a basis for rigorous inference. Progress in our science can be accelerated if modern methods can be used intelligently; this includes various I-T and Bayesian methods.

Journal of Applied Ecology↗

USGS2012

An economic value of remote-sensing information—Application to agricultural production and maintaining groundwater quality

Does remote-sensing information provide economic benefits to society, and can a value be assigned to those benefits? Can resource management and policy decisions be better informed by coupling past and present Earth observations with groundwater nitrate measurements? Using an integrated assessment approach, the U.S. Geological Survey (USGS) applied an established conceptual framework to answer these questions, as well as to estimate the value of information (VOI) for remote-sensing imagery. The approach uses moderate-resolution land-imagery (MRLI) data from the Landsat and Advanced Wide Field Sensor satellites that has been classified by the National Agricultural Statistics Service into the Cropland Data Layer (CDL). Within the constraint of the U.S. Environmental Protection Agency's public health threshold for potable groundwater resources, the USGS modeled the relation between a population of the CDL's land uses and dynamic nitrate (NO3-) contamination of aquifers in a case study region in northeastern Iowa. Employing various multiscaled, multitemporal geospatial datasets with MRLI to maximize the value of agricultural production, the approach develops and uses multiple environmental science models to address dynamic nitrogen loading and transport at specified distances from specific sites (wells) and at landscape scales (for example, across 35 counties and two aquifers). In addition to the ecosystem service of potable groundwater, this effort focuses on the use of MRLI for the management of the major land uses in the study region-the production of corn and soybeans, which can impact groundwater quality. Derived methods and results include (1) economic and dynamic nitrate-pollution models, (2) probabilities of the survival of groundwater, and (3) a VOI for remote sensing. For the northeastern Iowa study region, the marginal benefit of the MRLI VOI (in 2010 dollars) is $858 million ±$197 million annualized, which corresponds to a net present value of $38.1 billion ±$8.8 billion for that flow of benefits in perpetuity. Given that these economic estimates are derived from one case study in a part of only one State, the estimates provide a lower estimate related to the potential value of the Landsat Data Continuity Mission.

Professional Paper↗

USGS2022

Bayesian inverse reinforcement learning for collective animal movement

Agent-based methods allow for defining simple rules that generate complex group behaviors. The governing rules of such models are typically set a priori, and parameters are tuned from observed behavior trajectories. Instead of making simplifying assumptions across all anticipated scenarios, inverse reinforcement learning provides inference on the short-term (local) rules governing long-term behavior policies by using properties of a Markov decision process. We use the computationally efficient linearly-solvable Markov decision process to learn the local rules governing collective movement for a simulation of the selfpropelled-particle (SPP) model and a data application for a captive guppy population. The estimation of the behavioral decision costs is done in a Bayesian framework with basis function smoothing. We recover the true costs in the SPP simulation and find the guppies value collective movement more than targeted movement toward shelter.

Annals of Applied Statistics↗

USGS2013

Identification of metrics to monitor salt marsh integrity on National Wildlife Refuges in relation to conservation and management objectives

Executive Summary Most salt marshes in the US have been degraded by human activities, and threats from physical alterations, surrounding land-use, species invasions, and global climate change persist. Salt marshes are unique and highly productive ecosystems with high intrinsic value to wildlife, and many National Wildlife Refuges (NWRs) have been established in coastal areas to protect large tracts of salt marsh and wetland-dependent species. Various management practices are employed routinely on coastal NWRs to restore and enhance marsh integrity and ensure ecosystem sustainability. Prioritizing NWR salt marshes for application of management actions and choosing among multiple management options requires scientifically-based methods for assessing marsh condition. Monitoring is integral to structured decision-making (SDM), a formal process for decomposing a decision into its essential elements. Within a natural resource context, SDM involves identifying management objectives, alternative management actions, and expected management outcomes. The core of SDM is a set of criteria for measuring system performance and evaluating management responses. Therefore, use of SDM to frame natural resource decisions leads to logical selection of monitoring attributes that are linked explicitly to management needs. We used SDM to guide selection of variables for monitoring the ecological integrity of salt marshes within the National Wildlife Refuge System (NWRS). Our objectives were to identify indicators of salt marsh integrity that are effective across large geographic regions, responsive to a wide range of threats, and feasible to implement within funding and staffing constraints of the NWRS. In April, 2008, we engaged interdisciplinary experts in a week-long rapid prototyping SDM workshop to define the essential elements of salt marsh management decisions on refuges throughout the northeastern, southwestern, and northwestern US, corresponding to respective Regions 5, 2, and 1 of the US Fish and Wildlife Service (FWS). Through this process we identified measurable attributes for monitoring salt marsh ecosystems that are integrated into conservation practice and target management objectives. The following salt marsh attributes were identified through the SDM process either for describing state condition to determine management needs or for evaluating the achievement of management objectives: historical condition and geomorphic setting; ditch density; surrounding land use; ratio of open water area to vegetation area; rate of pesticide application; environmental contaminant concentration; change in marsh surface elevation relative to sea level rise; tidal range and groundwater level; surface topography; salinity; and species composition and abundance of vegetation, invasive species, invertebrates, nekton, and breeding and wintering birds. The identified attributes were too broadly defined to serve as operational monitoring variables. Therefore, we tested specific metrics for quantifying most of these attributes in summers of 2008 and 2009. The first four attributes in the above list can be characterized by office-based analysis of existing GIS data layers. The remaining attributes require field-based methods for assessment. We were forced to exclude a small number of attributes from field tests due to inconsistent data (pesticide application rate, environmental contaminant concentrations) or requirements that exceeded the scope of this project (change in marsh surface elevation; surface topography; benthic invertebrates; wintering birds). We evaluated potential metrics for evaluating all remaining field attributes. In partnership with NWRS biologists, we tested rapid versus intensive metrics for monitoring field attributes (tidal range and groundwater level; marsh surface elevation; salinity; and species composition and abundance of vegetation, invasive species, nekton, and breeding birds) at coastal refuges throughout FWS Region 5. Seven refuges participated in metric testing in 2008: Rachel Carson (ME), Parker River (MA), Wertheim (NY), E. B. Forsythe (NJ), Bombay Hook (DE), Prime Hook (DE), and Eastern Shore of Virginia Complex (VA). These seven and two additional refuges participated in metric testing in 2009: Rhode Island Complex (RI) and Stewart B. McKinney (CT). We based all field metrics on existing protocols for salt marsh assessment. Sampling locations were determined randomly within delineated marsh study units (MSUs) at each refuge. Detailed field methods are provided in appendices to this report. Measurements for individual metrics were averaged across samples within MSUs during each year of sampling. Each year, correlation or regression analysis was conducted on average measurements across MSUs within each attribute set to identify redundant metrics. Statistical redundancy between a pair of metrics within an attribute set (i.e., correlation or regression slopes with p-values < 0.05) was considered justification for eliminating one of the pair from the regional set of monitoring metrics. Decisions regarding metric elimination versus retention were based on feasibility of monitoring, considering such factors as sampling time, resources required, and potential for regional standardization in implementation. The result of these tests is a reduced suite of monitoring metrics that targets NWRS management decisions and is practicable for implementing on a regional scale. Based on these tests, we recommend the following list of metrics for monitoring integrity of NWRS salt marshes (marsh attribute category is in parentheses): (historical condition and geomorphic setting) position of marsh in the landscape, marsh shape, degree of fill and/or fragmentation, degree of tidal flushing, amount of aquatic edge; (ditch density) ranking of ditch density from none to severe; (surrounding land use) relative proportion of agricultural land in a 150-m buffer around the marsh, relative proportion of natural land in a 150-m buffer around the marsh, relative proportion of natural land in a 1-km buffer around the marsh; (ratio of open water area to vegetation area) ratio of open water to emergent herbaceous wetlands within the marsh; (marsh surface elevation) elevation referenced to NAVD88 in a representative area of the marsh; (tidal range and groundwater level) percent of time the marsh surface is flooded during deployment of a continuous water-level monitor at a representative marsh location, mean depth of surface flooding as measured by a continuous water-level monitor at a representative location; (salinity) salinity measured in surface water; (vegetation community) vegetation species richness using the point-intercept method in 100-m diameter survey plots, percent cover of various marsh community types within 100-m diameter survey plots; (invasive species abundance) percent cover of invasive plant species measured using the point-intercept method in 100-m diameter survey plots; (nekton community) nekton density, nekton species richness, length of Fundulus heteroclitus ; (breeding bird community) abundance of Willets counted per point during standard call-broadcast surveys, summed abundance of tidal marsh obligate species (Clapper Rail, Willet, Saltmarsh Sparrow, Seaside Sparrow) counted per point during standard call-broadcast surveys. Metrics describing the historical condition, geomorphic setting, and broad landscape features can be assessed using existing GIS databases. Our results support use of rapid methods to assess the majority of field metrics; only those used to describe the nekton community must be measured using intensive methods (throw traps or ditch nets, dependant on habitat configuration). Implementation of these metrics for quantitative assessment of NWRS salt marsh integrity in FWS Region 5 requires developing sampling designs for each refuge. Additionally, it is important to determine how the monitoring information will be used within a management context. SDM should be used to complete the analysis of salt marsh management decisions. The next steps would involve 1) prioritizing and weighting the management objectives; 2) predicting responses to individual management actions in terms of objectives and metrics; 3) using multiattribute utility theory to convert all measurable attributes to a common utility scale; 4) determining the total management benefit of each action by summing utilities across objectives; and 5) maximizing the total management benefits within cost constraints for each refuge. This process would allow the optimum management decisions for NWRS salt marshes to be selected and implemented based directly on monitoring data and current understanding of marsh responses to management actions. Monitoring the outcome of management actions would then allow new monitoring data to be incorporated into subsequent decisions.

Connecticut, Delaware, Maine, Massachusetts, New J↗

USGS2002

Data-quality measures for stakeholder-implemented watershed-monitoring programs

Community-based watershed groups, many of which collect environmental data, have steadily increased in number over the last decade. The data generated by these programs are often underutilized due to uncertainty in the quality of data produced. The incorporation of data-quality measures into stakeholder monitoring programs lends statistical validity to data. Data-quality measures are divided into three steps: quality assurance, quality control, and quality assessment. The quality-assurance step attempts to control sources of error that cannot be directly quantified. This step is part of the design phase of a monitoring program and includes clearly defined, quantifiable objectives, sampling sites that meet the objectives, standardized protocols for sample collection, and standardized laboratory methods. Quality control (QC) is the collection of samples to assess the magnitude of error in a data set due to sampling, processing, transport, and analysis. In order to design a QC sampling program, a series of issues needs to be considered: (1) potential sources of error, (2) the type of QC samples, (3) inference space, (4) the number of QC samples, and (5) the distribution of the QC samples. Quality assessment is the process of evaluating quality-assurance measures and analyzing the QC data in order to interpret the environmental data. Quality assessment has two parts: one that is conducted on an ongoing basis as the monitoring program is running, and one that is conducted during the analysis of environmental data. The discussion of the data-quality measures is followed by an example of their application to a monitoring program in the Big Thompson River watershed of northern Colorado.

Open-File Report↗

USGS2021

Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming

Executive Summary The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.

Wyoming↗

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.

conterminous United States↗

USGS1980

A review and evaluation of alternatives for updating U.S. Geological Survey land use and land cover maps

Since 1974, the U.S. Geological Survey has been engaged in a nationwide program of baseline mapping of land use and land cover and associated data at a scale of 1:250,000. As l:100,000-scale bases have become available, they have been used for mapping certain areas and for special applications. These two scales are appropriate for mapping land use and land cover data on a nationwide basis within a practical time frame, and with an acceptable degree of standardization, accuracy, and level of detail. An essential requisite to better use of the land is current information on land use and land cover conditions and on the rates and trends of changes with time. Thus, plans are underway to update these maps and data. The major considerations in planning a nationwide program for updating U.S. Geological Survey land use and land cover maps are as follows: (1) How often should maps be updated? (2) What remotely sensed source materials should be used for detecting and compiling changes in land use and land cover? (3) What base maps should be used for presenting data on land use and land cover changes? (4) What maps or portions of a map should be updated? (5) What methods should be used for identifying and mapping changes? (6) What procedures should be followed for updating maps and what formats should be used? These factors must be considered in developing a map update program that portrays an appropriate level of information, relates to and builds upon the existing U.S. Geological Survey land use and land cover digital and statistical data base, is timely, cost-effective and standardized, and meets the varying needs of land use and land cover data users.

Circular↗

USGS2020

Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts

A method to estimate the probable high groundwater level in Massachusetts, excluding Cape Cod and the islands, was developed in 1981. The method uses a groundwater measurement from a test site, groundwater measurements from an index well, and a distribution of high groundwater levels from wells in similar geologic and topographic settings. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, conducted an update to the Frimpter method for estimating the probable high groundwater levels in Massachusetts. The study evaluated the potential changes to the method resulting from four decades of additional groundwater-level data and the expansion of the network of wells for monitoring groundwater levels. The differences and potential benefits of daily, as opposed to monthly, measurements in the application of the method were examined because of the increased availability of high-frequency (subdaily) groundwater-level data. The study also considered long-term trends in groundwater levels that may alter the accuracy of the method. Finally, the accuracy of the estimated high groundwater levels was evaluated, and improved implementation guidance was prepared. For this study, groundwater levels in 153 wells in Massachusetts and surrounding States with records with lengths of 16 to 78 years were analyzed. The highest recorded groundwater levels ranged from 1.2 feet (ft) above land surface (flooded conditions) to 45.8 ft below land surface, with a median of 4.6 ft below land surface. The maximum annual groundwater-level range was 1.4 to 17.9 ft, with a median of 5.5 ft. The within-month variation, maximum annual groundwater-level range, and highest recorded groundwater level were computed using daily mean groundwater-level values from 28 wells with continuous records. The use of daily data resulted in larger maximum annual groundwater-level ranges (0.02 to 2.94 ft larger, with a median of 0.58 ft larger) and shallower highest-recorded groundwater levels (0.0 to 1.60 ft shallower, with a median of 0.18 ft shallower) than computations based on monthly measurements in the same wells. Statistical tests showed moderate to strong evidence of trends in measurements of both high and low groundwater levels within most of the periods during which water levels were analyzed. High groundwater levels rose beneath the land surface at most sites during four of the six periods used for analysis (1966–2015, 1986–2015, 1991–2010, and 1981–2010). Low groundwater levels also increased at many sites during most of the periods evaluated, but this trend was less widespread than the similar trends in high groundwater levels, and the trend was to deeper low groundwater levels at more sites than the trend to deeper high groundwater levels. There was no clear trend in annual groundwater-level ranges at most sites during the six periods analyzed. In general, the Frimpter method predicted shallower (higher) high groundwater levels than were observed but correctly classified sites according to their suitabilities for unmounded septic systems. The mean error of the predictions (difference between the estimated and observed groundwater levels) ranged from −3.23 ft to −1.40 ft for various approaches to estimating the groundwater-level range and selecting an index well. The method correctly classified 83 to 86 percent of monitoring-well sites according to their suitability for an unmounded septic system for many approaches to estimating the annual groundwater-level range and selecting an index well. The approach selected for estimating the annual groundwater-level range and selecting an index well will depend upon the importance of an accurate estimate of the high groundwater level as compared to the importance of an estimated high groundwater level that is less likely to be exceeded.

Connecticut, Massachusetts, New Hampshire, Rhode I↗

USGS2025

Evaluating hydrologic data products for scientific and management applications related to potential future streamflow conditions in the Upper Mississippi and Illinois Rivers

The hydrology of the Upper Mississippi and Illinois Rivers is a fundamental driver of ecosystem patterns and processes across a large portion of the United States. Quantitative hydrologic data for the main stems of these rivers underlie numerous scientific investigations, statistical models, and decision-making processes for local, State, and Federal agencies involved in the Upper Mississippi River Restoration program. Although historical hydrologic data exist, data representing potential future conditions of the Upper Mississippi and Illinois Rivers lack the resolution necessary to anticipate biotic and abiotic responses to altered hydrology and to determine resilient management actions. A source of future hydrologic scenarios is the readily available LOCA–VIC–mizuRoute hydrologic data products (named for the chain of models the data are produced from—localized constructed analogs, Variable Infiltration Capacity macroscale hydrological model, and the mizuRoute hydrologic routing model—that we shorten further to LVM in this report) that include simulated discharges for historic and future timeframes. The objective of this study is to assess the reliability of the hydrologic data products for their use in Upper Mississippi River Restoration program applications. Key study questions are (1) do the hydrologic data products reproduce characteristics of hydrology necessary to support ecological modeling and restoration decision-making applications within the Upper Mississippi River Restoration program? and (2) are there geographic differences in the reliability of the hydrologic data products? Seven characteristics of river hydrology were selected related to flow magnitude, seasonality, and regime for evaluation. The seven characteristics were calculated using observed and historical simulated hydrologic data at 19 U.S. Geological Survey streamgages throughout the basins of the Upper Mississippi and Illinois Rivers; two streamgages are located on the main stem of the Mississippi River and two streamgages are located on the main stem of the Illinois River. Statistical comparisons between observed and historical simulated characteristics indicated that the hydrologic data products did not reliably represent historical hydrologic conditions in the basin or main stem. The hydrologic data products we evaluated could not reliably capture the overall hydrologic regime or flow magnitudes; the latter is evidenced by substantial underestimates of discharge at most streamgages. Seasonal hydrologic characteristics were captured more reliably than flow magnitude, but overall correspondence was low for most streamgages. A weak latitudinal pattern in seasonal characteristics indicated the hydrologic data products poorly represent streamflow timing in snow-affected regions of the basin. Discrepancies in magnitude, seasonality, and regime indicate the potential for multiple sources of error. Because poor correspondence was present across all 19 streamgages, it was not possible to identify specific drivers of poor performance (that is, drainage area or geography). The modeling chain should be evaluated for biases associated with meteorologic forcing data, as well as hydrologic model formulation and calibration. We conclude that the hydrologic data products we evaluated appear unsuitable for applications tied to habitat and ecosystem restoration and management in the Upper Mississippi and Illinois Rivers. Plans to develop a future hydrology dataset for the Upper Mississippi River Restoration program would benefit from ongoing work to improve global climate model output downscaling methods, to improve hydrologic models, to make use of innovations in machine-learning approaches for projecting hydrology, and other efforts. The framework developed herein to evaluate hydrometeorological outputs generated using global climate models for a specific water resources application is a transferrable approach that could be applied to other data products and river systems.

Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗