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USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗

USGS2010

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

Journal of Applied Ecology↗

USGS2017

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Advances in Statistical Climatology, Meteorology a↗

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↗

USGS2009

Relative spatial soil geochemical variability along two transects across the United States and Canada

To support the development of protocols for the proposed North American Soil Geochemical Landscapes project, whose objective is to establish baselines for the geochemistry of North American soils, two continental-scale transects across the United States and Canada were sampled in 2004. The sampling employed a spatially stratified random sampling design in order to estimate the variability between 40-km linear sampling units, within them, at sample sites, and due to sample preparation and analytical chemical procedures. The 40-km scale was chosen to be consistent with the density proposed for the continental-scale project. The two transects, north–south (N–S) from northern Manitoba to the USA–Mexico border near El Paso, Texas, and east–west (E–W) from the Virginia shore north of Washington, DC, to north of San Francisco, California, closely following the 38th parallel, have been studied individually. The purpose of this study was to determine if statistically significant systematic spatial variation occurred along the transects. Data for 38 major, minor and trace elements in A- and C-horizon soils where less than 5% of the data were below the detection limit were investigated by Analysis of Variance (ANOVA). A total of 15 elements (K, Na, As, Ba, Be, Ce, La, Mn, Nb, P, Rb, Sb, Th, Tl and W) demonstrated statistically significant ( p <0.05) variability at the between-40-km scale for both horizons along both transects. Only Cu failed to demonstrate significant variability at the between-40-km scale for both soil horizons along both transects. The patterns of relative variability differ by transect and horizon. The N–S transect A-horizon soils show significant between-40-km scale variability for 29 elements, with only 4 elements (Ca, Mg, Pb and Sr) showing in excess of 50% of their variability at the within-40-km and ‘at-site’ scales. In contrast, the C-horizon data demonstrate significant between-40-km scale variability for 26 elements, with 21 having in excess of 50% of their variability at the within-40-km and ‘at-site’ scales. In 36 instances, the ‘at-site’ variability is statistically significant in terms of the sample preparation and analysis variability. It is postulated that this contrast between the A- and C- horizons along the N–S transect, that is dominated by agricultural land uses, is due to the local homogenization of Ap-horizon soils by tillage reducing the ‘at-site’ variability. The spatial variability is distributed similarly between scales for the A- and C-horizon soils of the E–W transect. For all elements, there is significant variability at the within-40-km scale. Notwithstanding this, there is significant between-40-km variability for 28 and 20 of the elements in the A- and C-horizon data, respectively. The differences between the two transects are attributed to (1) geology, the N–S transect runs generally parallel to regional strikes, whereas the E–W transect runs across regional structures and lithologies; and (2) land use, with agricultural tillage dominating along the N–S transect. The spatial analysis of the transect data indicates that continental-scale maps demonstrating statistically significant patterns of geochemical variability may be prepared for many elements from data on soil samples collected on a 40 x 40 km grid or similar sampling designs resulting in a sample density of 1 site per 1600 km 2 .

Applied Geochemistry↗

USGS2015

Evapotranspiration in the Nile Basin: Identifying dynamics and drivers, 2002–2011

Analysis of the relationship between evapotranspiration (ET) and its natural and anthropogenic drivers is critical in water-limited basins such as the Nile. The spatiotemporal relationships of ET with rainfall and vegetation dynamics in the Nile Basin during 2002–2011 were analyzed using satellite-derived data. Non-parametric statistics were used to quantify ET-rainfall interactions and trends across land cover types and subbasins. We found that 65% of the study area (2.5 million km2) showed significant (p < 0.05) positive correlations between monthly ET and rainfall, whereas 7% showed significant negative correlations. As expected, positive ET-rainfall correlations were observed over natural vegetation, mixed croplands/natural vegetation, and croplands, with a few subbasin-specific exceptions. In particular, irrigated croplands, wetlands and some forests exhibited negative correlations. Trend tests revealed spatial clusters of statistically significant trends in ET (6% of study area was negative; 12% positive), vegetation greenness (24% negative; 12% positive) and rainfall (11% negative; 1% positive) during 2002–2011. The Nile Delta, Ethiopian highlands and central Uganda regions showed decline in ET while central parts of Sudan, South Sudan, southwestern Ethiopia and northeastern Uganda showed increases. Except for a decline in ET in central Uganda, the detected changes in ET (both positive and negative) were not associated with corresponding changes in rainfall. Detected declines in ET in the Nile delta and Ethiopian highlands were found to be attributable to anthropogenic land degradation, while the ET decline in central Uganda is likely caused by rainfall reduction.

Water↗

USGS1992

Experiments with central-limit properties of spatial samples from locally covariant random fields

When spatial samples are statistically dependent, the classical estimator of sample-mean standard deviation is well known to be inconsistent. For locally dependent samples, however, consistent estimators of sample-mean standard deviation can be constructed. The present paper investigates the sampling properties of one such estimator, designated as the tau estimator of sample-mean standard deviation. In particular, the asymptotic normality properties of standardized sample means based on tau estimators are studied in terms of computer experiments with simulated sample-mean distributions. The effects of both sample size and dependency levels among samples are examined for various value of tau (denoting the size of the spatial kernel for the estimator). The results suggest that even for small degrees of spatial dependency, the tau estimator exhibits significantly stronger normality properties than does the classical estimator of standardized sample means.

Regional Science and Urban Economics↗

USGS2016

Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects

Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus , collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m 2 ) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m 2 . Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.

New York↗

USGS2022

Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources

The Williston Basin has been a leading oil and gas producing area for more than 50 years. While oil production initially peaked within the Williston Basin in the mid-1980s, production rapidly increased in the mid-2000s, largely because of improved horizontal (directional) drilling and hydraulic fracturing methods. In 2012, energy development associated with the Bakken Formation was identified as a priority requiring collaboration toward improved timeliness of issuing permits for new wells combined with reasonable measures to maintain environmental quality. Shortly thereafter, the Bakken Federal Executive Group was created to address common challenges associated with energy development. The Bakken Federal Executive Group partner agencies identified a gap in current understanding of the cumulative environmental challenges attributed to energy development throughout the area, resulting in an effort to aggregate scientific data and identify additional research and information needs related to natural resources within areas of energy development in the Williston Basin. As part of this effort, water resources in the area (including groundwater; streams and rivers; and lakes, reservoirs, and wetlands) were characterized and described in terms of physical occurrence, flow characteristics, recharge, water quality, and water use. Similarly, waters produced during energy-development activities also were characterized even though these waters are not considered usable resources within the area. Groundwater resources were characterized by the major hydrogeologic units, or aquifers, identifying the units that supply most groundwater used for domestic, stock, agricultural, and industrial purposes. The groundwater characterization included other deeper hydrogeologic units in the Williston Basin that may be a useable source of water with treatment, have utility as a reservoir for reinjection of produced waters, or be a source of minerals and energy resources. A generalized groundwater budget and flow system identifying the sources of recharge (stream infiltration, precipitation, and movement [leakage] from other aquifers) and the general groundwater flow direction is included for each of the major hydrogeologic units. Rivers and streams within the Williston Basin with 10 or more years of continuous streamflow data were identified. For a subset of these sites, streamflow characteristics, including the monthly and annual mean flow, were generated to identify seasonal and interannual changes in streamflow and thus provide information on the drivers and reliability of streamflow at the seasonal or multiyear scale. Daily streamflow and annual extreme flows (peak and low flow) also were estimated for the subset of sites. The daily streamflow and annual extreme flow values provide information on short-term or extreme events that are relevant to infrastructure design and evaluating spills, leaks, or accidental discharges of water or petroleum products. Surface-water features (lakes, ponds, and wetlands) were classified using the Cowardin system and identified on the National Wetlands Inventory maps generated by the U.S. Fish and Wildlife Service. The spatial distribution of the surface-water features was analyzed by State, county, and specifically in comparison to the Prairie Pothole Region. The proximity of the surface-water features to energy development infrastructure (specifically oil or gas well pads) was evaluated. It was determined that, although oil or gas wells are often near a surface-water feature, most surface-water features do not have wells nearby, with the exception of wells in the Prairie Pothole Region. Water-quality data were aggregated from two data sources: (1) the Water-Quality Portal, sponsored by the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), and National Water Quality Monitoring Council; and (2) a data compilation completed as part of the USGS National Water-Quality Assessment project. The Water-Quality Portal integrates publicly available water-quality data from databases maintained by the USGS, EPA, and U.S. Department of Agriculture, including water-quality data from Tribal, State, and local databases. Water-quality data for 15 commonly measured water-quality constituents were aggregated for groundwater, rivers and streams, and lakes and reservoirs. For each aggregated dataset (groundwater, rivers and streams, and lakes and reservoirs), analyses of the water-quality data included summary statistics, maps of spatial distribution of constituent values, boxplots of constituent values by timeframe or hydrogeologic unit, spatial comparisons of site locations and constituent values to petroleum well density, and comparisons of the constituent values measured to EPA drinking-water standards/guidelines. Produced water includes all fluids brought to the surface along with the targeted hydrocarbons as part of the oil and gas exploration and extraction processes. These fluids may include formation water (waters that co-exist with rock/oil/gas), hydraulic fracturing fluids, and other combinations of water and chemicals used during oil and gas well drilling, development, treatments, recompletions, and workovers. Produced water datasets were aggregated from two sources: the USGS National Produced Waters Geochemical database (ver. 2.1) and a series of projects focused specifically on sampling produced water in the Williston Basin from 2010 to 2014. The National Produced Waters Geochemical database was useful for a general understanding of produced-water chemistry. Produced waters are characterized by extreme salinity and contain elevated concentrations of other constituents (including arsenic, barium, cadmium, lead, zinc, radium-226/radium-228, and ammonium) that could negatively affect water and aquatic resources if released. Produced waters also have a generally unique chemical (isotopic) signature that may be useful in tracking water from different geologic units; for example, the oxygen/deuterium and strontium ratio values measured in brine waters from the Bakken Formation are distinct from brines collected from other geologic units in the Williston Basin. Water-use information related to energy production in the area also was aggregated and summarized. The summary of water use is not limited to oil and gas production but includes water used to produce all types of energy resources in the Williston Basin, including coal/lignite, thermoelectric power, oil and gas, hydropower, biomass and biofuels, wind, geothermal, and solar. Each State has its own methods for regulating and reporting water usage within its jurisdiction. These methods can introduce problems when examining water use from sources, such as the Missouri River or Fox Hills aquifer, that are shared across political boundaries. Without the one-to-one match for usage types and amounts used from a water source, it is difficult to develop a comprehensive water budget for the water source being evaluated. A large amount of freshwater is required to prepare a well for oil and gas well production; in some cases, 3 to 7 million gallons of water are needed per well. The EPA estimates that hydraulic fracturing in the Williston Basin uses between 70 to 140 billion gallons per year. Water also is used for myriad other purposes related to ancillary oil and gas extraction. In addition to water used for immediate energy development, the expanded human workforce migrating into the area and other support staff who have moved into the area during the development also use water. Research and information needs were identified that could be relevant in the evaluation of the effects of energy development on water resources. Information needs related to the evaluation of groundwater resources include the following: improved potentiometric-surface maps for glacial units; availability of a uniform stream network digital geographic coverage that spans the international boundary with Canada; enhanced surface-water use information with regards to the gain and loss of streamflow to shallow groundwater, which would increase understanding groundwater and surface-water interactions; and expanded geophysical assessments. Gaps in the availability of streamflow data include the lack of information on ice-jam flooding despite potential for effects to infrastructure (pipelines, roads, and facilities) and an understanding of the cumulative effects of largely undocumented stock and diversion dams. Although this study resulted in the aggregation of a large quantity of water-quality data, the availability of consistently collected, systematically processed and reported data over large parts of the Williston Basin is sparse. Few samples have been analyzed for constituents that may indicate the effect of energy development on water resources. Constituents that could be considered include boron, chloride, bromide, iodine, fluoride, manganese, lithium, radium, strontium isotopes, volatile organic compounds, and isotopes of inorganic ions (such as hydrogen and carbon). Collaboration between Tribal, Federal, State, and local entities to identify a common study design, common monitoring constituents, and consistent sampling locations would generate datasets with broad utility and would likely result in overall cost savings for monitoring over time. Similarly, there is a need for standardized sample collection, processing, laboratory analytical methods, and the collection of ancillary data for produced waters sampling. Additional characterization of the range of chemical, microbial, and isotopic compositions and quantities of “end-member” produced waters, and the collection of time-series datasets to document the changes in produced waters during and after well development also were needs identified during this study. Water-use estimates would be improved through the implementation of comprehensive studies of water use from groundwater and surface-water sources using consistent methodologies across the Williston Basin. The submission of chemical and water data related to hydraulic fracturing collected by the oil and gas industry would add to the quantity of available data. Consistent implementation of regulations and monitoring controls across political boundaries (State, county, and international) would further improve the consistency of data available for the estimates of water use.

Montana, North Dakota, South Dakota↗

USGS2018

Occupancy modeling species–environment relationships with non‐ignorable survey designs

Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non‐probability surveys are combined or unequal selection probabilities are used, the design may be non‐ignorable. We outline the use of pseudo‐maximum likelihood estimation for site‐occupancy models to account for such non‐ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo‐likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non‐detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudo‐maximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species–environment relationships with non‐ignorable sampling designs was biased, whereas the pseudo‐likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non‐probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. Our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model‐based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence‐based conservation decision‐making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided.

Ecological Applications↗

USGS2024

Peak streamflow trends in Missouri and their relation to changes in climate, water years 1921–2020

This report characterizes changes in peak streamflow in Missouri and the relation of these changes to climatic variability, and provides a foundation for future studies that can address nonstationarity in peak-streamflow frequency analysis in Missouri. Records of annual peak and daily streamflow at streamgages and gridded monthly climatic data (observed and modeled) were examined across four trend periods (100 years, water years 1921–2020; 75 years, 1946–2020; 50 years, 1971–2020; and 30 years, 1991–2020) for trends, change points (abrupt changes in the streamflow time series), and other statistical properties indicative of changing conditions. Peak streamflow magnitudes generally exhibit upward trends across the State for the 100-, 75-, and 50-year trend periods and only in southern Missouri for the 30-year trend period. The medians of the trend magnitudes (normalized by median peak streamflow) range from a 10-percent increase during the 30-year trend period to a 40-percent increase during the 100-year trend period. Changes in the 90-percent quantile of peak streamflow, which correspond to the 10-percent exceedance probability often used for the design of drainage structures, are not as substantial or widespread, showing consistent increases mainly in the southern part of the State in the 50- and 30-year trend periods. Streamgages with trends in peak streamflow often also have change points, or abrupt changes, in streamflow magnitude. Change points in peak streamflows generally follow that of the peak streamflow trends, with upward change points throughout most of the State at the 100- and 75-year trend periods and in southern Missouri at the 30-year trend period. Temporally, clusters upward of change points are observed in the 1970s through 1980s for the 100-, 75-, and 50-year trend periods and around 2006 and 2007 for the 50- and 30-year trend periods. A peaks-over-threshold analysis, which evaluates changes in the frequency of peak streamflows over a certain threshold, indicates that high flows have increased in frequency at 50 to 64 percent of streamgages in the 100- and 75-year trend periods. Most streamgages in the 50- and 30-year trend periods exhibit no change. Although the frequency of high flows has increased at some streamgages and trend periods in Missouri, these increases are not as widespread as the increases in the magnitude of peak streamflow. Upward trends in observed temperature and observed annual precipitation dominate in all trend periods, with no downward trends in precipitation and only two somewhat likely downward trends in temperature for the 100-year trend period. Increases in annual precipitation mostly are limited to southern Missouri for the 30-year trend period. The proportion of precipitation falling as snow has largely decreased in the study basins across the State, which is expected in response to increasing temperature. Upward trends in modeled annual runoff, which in this study incorporates only the effects of climatic variation, are observed in the same geographic areas where there are increases in observed annual precipitation. When peak streamflow and climatic trends are considered together, widespread upward trends in peak streamflows for the 100-, 75-, and 50-year trend periods and for the 30-year trend period mainly in southern Missouri (encompassing both trends and abrupt change) appear to be driven largely by increases in precipitation based on spatial patterns and statistical relations. The prevalence of nonstationarity in peak streamflow in Missouri has important implications for peak-flow frequency analysis. Winter and spring precipitation and the occurrence of extreme precipitation events are expected to increase across the State. If precipitation continues to increase as expected, peak-flow frequency estimates based on older records may no longer represent the hydrologic regime of today, and methods for nonstationary peak-flow frequency analysis may be needed.

Missouri↗

USGS2026

Bottom-up characterization of geologic methane emissions in the San Juan Basin in the southwestern USA

Methane is a potent greenhouse gas that plays an important role in atmospheric chemistry and global warming. The current global methane budget has large uncertainties, and a better understanding of the budget would help to guide strategies for reducing anthropogenic emissions to fight climate change. Natural geologic methane emissions are a particularly poorly constrained source, with top-down estimates from 14 C in ice cores suggesting much lower geologic emissions than bottom-up scaling of direct flux measurements. Our study aims to contribute to resolving this discrepancy through improved bottom-up characterization of geologic methane seepage in the San Juan Basin in southwestern Colorado and northwestern New Mexico, USA. We performed 983 new flux chamber measurements in this basin during summer 2022 and winter 2023 field campaigns. Our results, in combination with prior measurements, suggest that natural seepage in the San Juan Basin only occurs on or near the Fruitland coal outcrop. Specifically, our new measurements confirm previous measurements of seepage along the northwestern exposure of the Fruitland outcrop in Colorado (a known hydrodynamic overpressure region) and for the first time, identified seepage locations along the southernmost Fruitland outcrop exposure in New Mexico, in association with a coal cleat and a fault. Overall, seepage along the Fruitland coal outcrop is heterogeneously distributed, with both positive and negative (interpreted as microbial soil sink) methane fluxes. Features that are hypothesized to be predictive of seepage (e.g., faults) were not associated with positive methane fluxes in areas outside of the Fruitland outcrop. Our best estimate for total geologic methane seepage in the San Juan Basin from spatial interpolation and statistical upscaling is approximately 0.14 Tg CH 4 /yr, with a range from 0.029 to 0.48 Tg CH 4 /yr. This best-estimate value is lower than a previous bottom-up estimate from a gridded seepage inventory, but higher than a previous top-down estimate.

Colorado, New Mexico↗

USGS2021

Characterization of water-resource threats and needs for U.S. Fish and Wildlife Service National Wildlife Refuges in the Legacy Mountain-Prairie Region, 2020

The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service (FWS), began a study in 2019 to complete the compilation and quality assurance of water-resource threats and needs data for the 117 National Wildlife Refuges (NWRs) in the FWS Legacy Mountain-Prairie Region (LMPR) and to characterize the water-resource threats and needs of each refuge and of the LMPR itself. The LMPR encompasses the states of Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming. This report includes the compilation and quality assurance of current (April 2020) water-resource threats and needs data for the refuges in the LMPR and a statistical, graphical, and spatial characterization, including the ranking and prioritization of threat types, threat causes, and needs by the number of occurrences in the LMPR as a whole and by refuges, states, and select U.S. Environmental Protection Agency Level III Ecoregions. A total of 540 unique threat occurrences were identified for 109 refuges in the LMPR. No threats were identified for eight refuges. About 43 percent of the threat occurrences, for 59 refuges, had a high-severity threat rating. Of the 10 most common threat types, 8 were also among the most common high-severity threat types. Water-resource threats had 72 different causes. About 83 percent of the overall common causes for threats and for high-severity threats were the same. The most common threat types overall and the most common high-severity threat types were compromised water management capability, habitat shifting/alteration, and altered flow regimes. The 20 water-resource threat types for Long Lake NWR were the most for refuges in the LMPR. Other refuges with the greatest number of threat types included Marais des Cygnes NWR (18) and Arapaho and Lee Metcalf NWRs (16 each). About 54 percent of refuges with threats had high-severity threats. Arapaho and Quivira NWRs each had 10 high-severity threat types, the maximum number of high-severity threat types for LMPR refuges. A total of 637 unique need occurrences were identified for 114 refuges. No needs were reported for three refuges. The most common need type, a Water Resource Inventory and Assessment, was reported for 78 refuges. Two of the most common need types, repair and replace water management infrastructure and water supply/quantity monitoring, were the most common high-priority need types. Bear River Migratory Bird Refuge had the most (39) unique water-resource need types for refuges in the LMPR. Other refuges with the greatest number of need types were Baca (38), Alamosa (36), and Monte Vista (36) NWRs. The most high-priority need types for a refuge was 23, at Monte Vista NWR. Alamosa (22), Baca (22), and Lake Andes (19) NWRs were also among the top 4 refuges with the greatest number of high-priority need types. An overall ranking scheme was developed to identify refuges that have the highest-ranking priority for conservation efforts to fulfill refuges’ statutory purposes. The count of occurrences of high-severity threats and high-priority needs were summed to determine the overall ranking value for a refuge. The 10 refuges with the highest overall ranking values, in order of ranking from higher to lower, were Alamosa, Baca, and Monte Vista NWRs (tied for highest); Lake Andes NWR, Ouray and Quivira NWRs, Bear River Migratory Bird Refuge and Flint Hills NWR, Cokeville Meadows NWR, and Arapaho NWR. About 33 percent of overall threat occurrences were reported as under the control of the FWS to mitigate, as were 37 percent of all threat occurrences with a high-severity rating. The most common overall threat types and high-severity threat types under FWS control were compromised water management capability; habitat shifting/alteration; altered flow regimes; loss/alteration of wetland habitat; and legal challenges or fines for non-compliance with water policy, law, or regulation. A total of 68 percent of overall need occurrences and 67 percent of all high-priority need occurrences were under the control of the FWS. The most common overall need types and high-priority needs types under control were repair or replace water management infrastructure, water supply/quantity monitoring, water quality baseline monitoring, and protect habitat from invasive species. A Water Resource Inventory and Assessment was also a common overall need under FWS control, as was the high-priority need of water level monitoring.

Colorado, Kansas, Montana, Nebraska, North Dakota,↗

USGS2017

Macroinvertebrate communities evaluated prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho, 2001-16

The U.S. Geological Survey, in cooperation with Blaine County and The Nature Conservancy, evaluated the status of macroinvertebrate communities prior to and following a channel restoration project in Silver Creek, Blaine County, Idaho. The objective of the evaluation was to determine whether 2014 remediation efforts to restore natural channel conditions in an impounded area of Silver Creek caused declines in local macroinvertebrate communities. Starting in 2001 and ending in 2016, macroinvertebrates were sampled every 3 years at two long-term trend sites and sampled seasonally (spring, summer, and autumn) in 2013, 2015, and 2016 at seven synoptic sites. Trend-site communities were collected from natural stream-bottom substrates to represent locally established macroinvertebrate assemblages. Synoptic site communities were sampled using artificial (multi-plate) substrates to represent recently colonized (4–6 weeks) assemblages. Statistical summaries of spatial and temporal patterns in macroinvertebrate taxonomic composition at both trend and synoptic sites were completed. The potential effect of the restoration project on resident macroinvertebrate populations was determined by comparing the following community assemblage metrics: Total taxonomic richness (taxa richness); Total macroinvertebrate abundance (total abundance); Ephemeroptera, Plecoptera, Trichoptera (EPT) richness; EPT abundance; Simpson’s diversity; and Simpson’s evenness for periods prior to and following restoration. A significant decrease in one or more metric values in the period following stream channel restoration was the basis for determining impairment to the macroinvertebrate communities in Silver Creek. Comparison of pre-restoration (2001–13) and post‑restoration (2016) macroinvertebrate community composition at trend sites determined that no significant decreases occurred in any metric parameter for communities sampled in 2016. Taxa and EPT richness of colonized assemblages at synoptic sites increased significantly from pre-restoration in 2013 to post-restoration in 2015 and 2016. Similarly, total and EPT abundances at synoptic sites showed non-significant increases from 2013 to 2015 and 2016. Significant seasonal differences in macroinvertebrate assemblages were apparent at synoptic site locations and likely reflected typical life-history patterns of increased insect emergence and development in the late spring and early summer months. Taxa and EPT richness were each significantly higher in spring and summer than in autumn, and total abundances were significantly higher in spring than in summer and autumn. No significant differences in community diversity or evenness of colonized communities were noted at synoptic site locations between pre- and post-restoration years or among seasons. Select community-metric results from the trend- and synoptic ‑ site sampling indicated that the Silver Creek restoration effort in 2014 did not result in a significant decline in resident macroinvertebrate communities.

Idaho↗

USGS2023

Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada

Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to conventional oil and natural gas accumulations. The specific provinces are the Campos Basin (Brazil), the Santos Basin (Brazil), the North Sea Graben (northwestern Europe), the Middle Magdelena Basin (Colombia), the Sirte Basin (Libya), and the Kutei Basin (Indonesia). For each province, discovery data and well data through October 2019 are reported; from these data, depth distributions of the oil in oil fields and natural gas in gas fields were computed. The concepts of delineated prospective area and explored area include elements of geographic spatial information and statistical data analytics. Graphs showing dynamic growth of discoveries that are tied to the delineated prospective area provide a means of grading prospective area. Visualizations put the results of exploration in the context of geographic and geologic features of the play or basin and can be a tool to assist geologists with the appraisal of the number and sizes of undiscovered petroleum accumulations. Visualizations of exploration drilling and discoveries can (1) assist in conceptualizing a geologic model of the basin, (2) highlight relations among discovered accumulations in different plays or assessment units within the basin, and (3) allow the geologist to identify the missing information needed to complete the geologic model of a basin. Further, if visualization attributes can be quantified, they may be used for formulating quantitative models that predict numbers and sizes of undiscovered oil and gas accumulations. Such modeling approaches include discovery process models, Bayesian network models that characterize play or assessment unit dependencies, and innovative applications of machine learning to complement standard geologic assessments. The purpose of this report is to show how visualizations can further the understanding of exploration maturity for the six selected petroleum provinces. It also shows how the geologic framework, geologic data, and drilling and discovery trends can give context to the interpretation of the visualizations that lead to appraisal of exploration maturity.

Campos Basin, Kutei Basin, Middle Magdelena Basin,↗

USGS1997

Trace elements and synthetic organic compounds in biota and streambed sediment of the Western Lake Michigan Drainages, 1992-1995

Sampling was conducted in 1992,1994, and 1995 to determine the occurrence of a broad suite of trace elements and synthetic organic compounds in biota and streambed sediment in selected streams in the Western Lake Michigan Drainages a study unit of the National Water- Quality Assessment (NAWQA) Program of the U.S. Geological Survey. Sediment was sampled at 31 sites for trace elements and 23 sites for synthetic organic compounds; biota were collected at a subset of sites. Some of the variability in trace elements and synthetic organic compounds was related to land use, and many differences in trace element concentration by land use category were statistically significant. The spatial distribution of some trace elements was related to a combination of land use and surficial deposits and (or) bedrock type. Urban land use was likely the dominant factor influencing high sediment concentrations of Cd, Cu, Hg, and Pb. Forested land use was related to high concentrations of As in sediment and biota and high Se in sediment; however, bedrock type was an additional factor for As and surficial deposits type was a factor for Se. Land use, surficial deposits, and bedrock type appeared to influence Ni and Zn concentrations, and Cr concentrations did not directly reflect any of these factors. Most occurrences of organochlorine compounds in sediment and biota were related to urban land use. DDT and related compounds were detected at sites spanning all land uses. PCBs were detected in sediment at large-river sites and at one urban site in the Milwaukee area; the highest concentrations of PCBs in fish were found at two of four large river sites. A larger number of SVOCs were detected at urban sites and large river sites compared to agricultural and forested sites.

Michigan, Wisconsin↗

USGS2016

Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis

This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000–2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m −2 ·year −1 ) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.

Great Plains↗

USGS1999

Application of spatially referenced regression modeling for the evaluation of total nitrogen loading in the Chesapeake Bay watershed

The reduction of stream nutrient loads is an important part of current efforts to improve water quality in the Chesapeake Bay. To design programs that will effectively reduce stream nutrient loading, resource managers need spatially detailed information that describes the location of nutrient sources and the watershed factors that affect delivery of nutrients to the Bay. To address this need, the U.S. Geological Survey has developed a set of spatially referenced regression models for the evaluation of nutrient loading in the watershed. The technique applied for this purpose is referred to as ?SPARROW? (SPAtially Referenced Regressions On Watershed attributes), which is a statistical modeling approach that retains spatial referencing for illustrating predictions, and for relating upstream nutrient sources to downstream nutrient loads. SPARROW is based on a digital stream-network data set that is composed of stream segments (reaches) that are attributed with traveltime and connectivity information. Drainage-basin boundaries are defined for each stream reach in the network data set through the use of a digital elevation model. For the Chesapeake Bay watershed, the spatial network was developed using the U.S. Environmental Protection Agency?s River Reach File 1 digital stream network, and is composed of 1,408 stream reaches and watershed segments. To develop a SPARROW model for total nitrogen in the Chesapeake Bay watershed, data sets for sources and basin characteristics were incorporated into the spatial network and related to stream-loading information by using a nonlinear regression model approach. Total nitrogen source variables that were statistically significant in the model include point sources, urban area, fertilizer application, manure generation and atmospheric deposition. Total nitrogen loss variables that were significant in the model include soil permeability and instream-loss rates for four stream-reach classes. Applications of SPARROW for evaluating total nitrogen loading in the Chesapeake Bay watershed include the illustration of the spatial distributions of total nitrogen yields and of the potential for delivery of those yields to the Bay. This information is being used by the Chesapeake Bay Program to target nutrient-reduction areas (Priority Nutrient Reduction Areas) and to design nutrient-load reduction plans that are specific to each tributary (Tributary Strategies).

Chesapeake Bay watershed↗