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At least 55 records · Page 3Linked to original sources
USGS2005

Applying petrophysical models to radar travel time and electrical resistivity tomograms: Resolution-dependent limitations

[1] Geophysical imaging has traditionally provided qualitative information about geologic structure; however, there is increasing interest in using petrophysical models to convert tomograms to quantitative estimates of hydrogeologic, mechanical, or geochemical parameters of interest (e.g., permeability, porosity, water content, and salinity). Unfortunately, petrophysical estimation based on tomograms is complicated by limited and variable image resolution, which depends on (1) measurement physics (e.g., electrical conduction or electromagnetic wave propagation), (2) parameterization and regularization, (3) measurement error, and (4) spatial variability. We present a framework to predict how core‐scale relations between geophysical properties and hydrologic parameters are altered by the inversion, which produces smoothly varying pixel‐scale estimates. We refer to this loss of information as “correlation loss.” Our approach upscales the core‐scale relation to the pixel scale using the model resolution matrix from the inversion, random field averaging, and spatial statistics of the geophysical property. Synthetic examples evaluate the utility of radar travel time tomography (RTT) and electrical‐resistivity tomography (ERT) for estimating water content. This work provides (1) a framework to assess tomograms for geologic parameter estimation and (2) insights into the different patterns of correlation loss for ERT and RTT. Whereas ERT generally performs better near boreholes, RTT performs better in the interwell region. Application of petrophysical models to the tomograms in our examples would yield misleading estimates of water content. Although the examples presented illustrate the problem of correlation loss in the context of near‐surface geophysical imaging, our results have clear implications for quantitative analysis of tomograms for diverse geoscience applications.

Journal of Geophysical Research B: Solid Earth↗
USGS2017

Bias correction of bounded location errors in presence-only data

Location error occurs when the true location is different than the reported location. Because habitat characteristics at the true location may be different than those at the reported location, ignoring location error may lead to unreliable inference concerning species–habitat relationships. We explain how a transformation known in the spatial statistics literature as a change of support (COS) can be used to correct for location errors when the true locations are points with unknown coordinates contained within arbitrary shaped polygons. We illustrate the flexibility of the COS by modelling the resource selection of Whooping Cranes ( Grus americana ) using citizen contributed records with locations that were reported with error. We also illustrate the COS with a simulation experiment. In our analysis of Whooping Crane resource selection, we found that location error can result in up to a five-fold change in coefficient estimates. Our simulation study shows that location error can result in coefficient estimates that have the wrong sign, but a COS can efficiently correct for the bias.

Methods in Ecology and Evolution↗
USGS2008

Analysis of a spatial point pattern: Examining the damage to pavement and pipes in Santa Clara Valley resulting from the Loma Prieta earthquake

This report describes some simple spatial statistical methods to explore the relationships of scattered points to geologic or other features, represented by points, lines, or areas. It also describes statistical methods to search for linear trends and clustered patterns within the scattered point data. Scattered points are often contained within irregularly shaped study areas, necessitating the use of methods largely unexplored in the point pattern literature. The methods take advantage of the power of modern GIS toolkits to numerically approximate the null hypothesis of randomly located data within an irregular study area. Observed distributions can then be compared with the null distribution of a set of randomly located points. The methods are non-parametric and are applicable to irregularly shaped study areas. Patterns within the point data are examined by comparing the distribution of the orientation of the set of vectors defined by each pair of points within the data with the equivalent distribution for a random set of points within the study area. A simple model is proposed to describe linear or clustered structure within scattered data. A scattered data set of damage to pavement and pipes, recorded after the 1989 Loma Prieta earthquake, is used as an example to demonstrate the analytical techniques. The damage is found to be preferentially located nearer a set of mapped lineaments than randomly scattered damage, suggesting range-front faulting along the base of the Santa Cruz Mountains is related to both the earthquake damage and the mapped lineaments. The damage also exhibit two non-random patterns: a single cluster of damage centered in the town of Los Gatos, California, and a linear alignment of damage along the range front of the Santa Cruz Mountains, California. The linear alignment of damage is strongest between 45° and 50° northwest. This agrees well with the mean trend of the mapped lineaments, measured as 49? northwest.

California↗
USGS2011

Description and testing of the Geo Data Portal: Data integration framework and Web processing services for environmental science collaboration

Interest in sharing interdisciplinary environmental modeling results and related data is increasing among scientists. The U.S. Geological Survey Geo Data Portal project enables data sharing by assembling open-standard Web services into an integrated data retrieval and analysis Web application design methodology that streamlines time-consuming and resource-intensive data management tasks. Data-serving Web services allow Web-based processing services to access Internet-available data sources. The Web processing services developed for the project create commonly needed derivatives of data in numerous formats. Coordinate reference system manipulation and spatial statistics calculation components implemented for the Web processing services were confirmed using ArcGIS 9.3.1, a geographic information science software package. Outcomes of the Geo Data Portal project support the rapid development of user interfaces for accessing and manipulating environmental data.

Open-File Report↗
USGS1996

Potential effects of climate change on streamflow, eastern and western slopes of the Sierra Nevada, California and Nevada

Precipitation-runoff models of the East Fork Carson and North Fork American Rivers were developed and calibrated for use in evaluating the sensitivity of streamflow in the north-central Sierra Nevada to climate change. The East Fork Carson River drains part of the rain-shadowed, eastern slope of the Sierra Nevada and is generally higher than the North Fork American River, which drains the wetter, western slope. First, a geographic information system was developed to describe the spatial variability of basin characteristics and to help estimate model parameters. The result was a partitioning of each basin into noncontiguous, but hydrologically uniform, land units. Hydrologic descriptions of these units were developed and the Precipitation- Runoff Modeling System (PRMS) was used to simulate water and energy balances for each unit in response to daily weather conditions. The models were calibrated and verified using historical streamflows over 22-year (Carson River) and 42-year (American River) periods. Simulated annual streamflow errors average plus 10 percent of the observed flow for the East Fork Carson River basin and plus 15 percent for the North Fork American River basin. Interannual variability is well simulated overall, but, at daily scales, wet periods are simulated more accurately than drier periods. The simulated water budgets for the two basins are significantly different in seasonality of streamflow, sublimation, evapotranspiration, and snowmelt. The simulations indicate that differences in snowpack and snowmelt timing can play pervasive roles in determining the sensitivity of water resources to climate change, in terms of both resource availability and amount. The calibrated models were driven by more than 25 hypothetical climate-change scenarios, each 100 years long. The scenarios were synthesized and spatially disaggregated by methods designed to preserve realistic daily, monthly, annual, and spatial statistics. Simulated streamflow timing was not very sensitive to changes in mean precipitation, but was sensitive to changes in mean temperatures. Changes in annual streamflow amounts were amplified reflections of imposed mean precipitation changes, with especially large responses to wetter climates. In contrast, streamflow amount was surprisingly insensitive to mean temperature changes as a result of temporal links between peak snowmelt and the beginning of warm-season evapotranspiration. Comparisons of simulations driven by temporally detailed climate-model changes in which mean temperature changes vary from month to month and simulations in which uniform climate changes were imposed throughout the year indicate that the snowpack accumulates the influences of short-term conditions so that season average climate changes were more important than shorter term changes.

Water-Resources Investigations Report↗
USGS2010

Bringing modeling to the masses: A web based system to predict potential species distributions

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Future Internet↗
USGS2012

Fine-scale habitat use of reintroduced black-footed ferrets on prairie dog colonies in New Mexico

Black-footed ferrets ( Mustela nigripes ) are among the most endangered animals in North America. Reintroductions of captive-born ferrets onto prairie dog ( Cynomys spp.) colonies are crucial to the conservation of the species. In September 2007, captive-born ferrets were released on a black-tailed prairie dog ( Cynomys ludovicianus ) colony at the Vermejo Park Ranch, New Mexico. Ferret kits experimentally released in areas of comparatively low and high prairie dog burrow densities were located via spotlight surveys. Some maturing ferret kits were subsequently translocated to areas of low and high burrow densities on nearby prairie dog colonies. For 2 months, fine-scale habitat use was quantified by mapping all burrow openings within a 30-m radius of each ferret location. Spatial statistics accounted for autocorrelation in the burrow densities in areas used by ferrets. It was hypothesized that ferrets would select areas of high burrow densities within colonies; however, burrow densities in areas used by ferrets were generally similar to the available burrow densities. Because ferrets used areas with burrow densities similar to densities available at the colony level and because of the potential energetic benefits for ferrets using areas with high burrow densities, releasing ferrets on colonies with high burrow densities might increase reintroduction success.

New Mexico↗
USGS2013

Spatial heterogeneity in statistical power to detect changes in lake area in Alaskan National Wildlife Refuges

Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.

Alaska↗
USGS2012

A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

Background: Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns. Methods: A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns. Results: The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001). Conclusions: This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

Kentucky↗
USGS2008

Spatial and temporal statistical analysis of bycatch data: Patterns of sea turtle bycatch in the North Atlantic

Loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) sea turtle distributions and movements in offshore waters of the western North Atlantic are not well understood despite continued efforts to monitor, survey, and observe them. Loggerhead and leatherback sea turtles are listed as endangered by the World Conservation Union, and thus anthropogenic mortality of these species, including fishing, is of elevated interest. This study quantifies spatial and temporal patterns of sea turtle bycatch distributions to identify potential processes influencing their locations. A Ripley's K function analysis was employed on the NOAA Fisheries Atlantic Pelagic Longline Observer Program data to determine spatial, temporal, and spatio-temporal patterns of sea turtle bycatch distributions within the pattern of the pelagic fishery distribution. Results indicate that loggerhead and leatherback sea turtle catch distributions change seasonally, with patterns of spatial clustering appearing from July through October. The results from the space-time analysis indicate that sea turtle catch distributions are related on a relatively fine scale (30-200 km and 1-5 days). The use of spatial and temporal point pattern analysis, particularly K function analysis, is a novel way to examine bycatch data and can be used to inform fishing practices such that fishing could still occur while minimizing sea turtle bycatch. ?? 2008 NRC.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2018

Spatial autoregressive models for statistical inference from ecological data

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals ( Phoca vitulina ) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.

Ecological Monographs↗
USGS2005

A statistical estimator of the spatial distribution of the water-table altitude

An algorithm was designed to statistically estimate the areal distribution of water-table altitude. The altitude of the water table was bounded below by the minimum water-table surface and above by the land surface. Using lake elevations and stream stages, and interpolating between lakes and streams, the minimum water-table surface was generated. A multiple linear regression among the minimum water-table altitude, the difference between land-surface and minimum water-table altitudes, and the water-level measurements from surficial aquifer system wells resulted in a consistently high correlation for all groups of physiographic regions in Florida. A simple linear regression between land-surface and water-level measurements resulted in a root-mean-square residual of 4.23 m, with residuals ranging from -8.78 to 41.54 m. A simple linear regression between the minimum water table and the water-level measurements resulted in a root-mean-square residual of 1.45 m, with residuals ranging from -7.39 to 4.10 m. The application of the multiple linear regression presented herein resulted in a root-mean-square residual of 1.05 m, with residuals ranging from -5.24 to 5.63 m. Results from complete and partial F tests rejected the hypothesis of eliminating any of the regressors in the multiple linear regression presented in this study.

Ground Water↗
USGS2014

Status and understanding of groundwater quality in the Klamath Mountains study unit, 2010: California GAMA Priority Basin Project

Groundwater quality in the Klamath Mountains (KLAM) study unit was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in Del Norte, Humboldt, Shasta, Siskiyou, Tehama, and Trinity Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The GAMA Priority Basin Project was designed to provide a spatially unbiased, statistically robust assessment of the quality of untreated (raw) groundwater in the primary aquifer system. The assessment is based on water-quality data and explanatory factors for groundwater samples collected in 2010 by the USGS from 39 sites and on water-quality data from the California Department of Public Health (CDPH) water-quality database. The primary aquifer system was defined by the depth intervals of the wells listed in the CDPH water-quality database for the KLAM study unit. The quality of groundwater in the primary aquifer system may be different from that in the shallower or deeper water-bearing zones; shallow groundwater may be more vulnerable to surficial contamination. This study included two types of assessments: (1) a status assessment , which characterized the status of the current quality of the groundwater resource by using data from samples analyzed for volatile organic compounds, pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements, and (2) an understanding assessment , which evaluated the natural and human factors potentially affecting the groundwater quality. The assessments were intended to characterize the quality of groundwater resources in the primary aquifer system of the KLAM study unit, not the quality of treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentrations divided by the health- or aesthetic-based benchmark concentrations) were used for evaluating groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. A relative-concentration greater than (>) 1.0 indicates a concentration greater than a benchmark, and a relative-concentration less than or equal to (≤) 1.0 indicates a concentration less than or equal to a benchmark. Relative-concentrations of organic constituents were classified as “high” (relative-concentration > 1.0), “moderate” (0.1 < relative-concentration ≤ 1.0), or “low” (relative-concentration ≤ 0.1). For inorganic constituents, the boundary between low and moderate relative-concentration was set at 0.5. Aquifer-scale proportion was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the area of the primary aquifer system with a relative-concentration greater than 1.0 for a particular constituent or class of constituents; percentage is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentages of the primary aquifer system with moderate and low relative-concentrations, respectively. The KLAM study unit includes more than 8,800 square miles (mi 2 ), but only those areas near the sampling sites, about 920 mi 2 , are included in the areal assessment of the study unit. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. To confirm this methodology, 90 percent confidence intervals were calculated for the grid-based high aquifer-scale proportions and were compared to the spatially weighted results, which were found to be within these confidence intervals in all cases. Grid-based results were selected for use in the status assessment unless, as was observed in a few cases, a grid-based result was zero and the spatially weighted result was not zero, in which case, the spatially weighted result was used. The status assessment showed that inorganic constituents with human-health benchmarks were detected at high relative-concentrations in 2.6 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The high aquifer-scale proportion for inorganic constituents mainly reflected the high aquifer-scale proportions of boron. Inorganic constituents with secondary maximum contaminant levels were detected at high relative-concentrations in 13 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The constituents present at high relative-concentrations included iron and manganese. Organic constituents with human-health benchmarks were not detected at high relative-concentrations, but were detected at moderate relative-concentrations in 1.9 percent of the primary aquifer system. The 1.9 percent reflected a spatially weighted moderate aquifer-scale proportion for the gasoline additive methyl tert-butyl ether. Of the 148 organic constituents analyzed, 14 constituents were detected. Only one organic constituent had a detection frequency of greater than 10 percent—the trihalomethane, chloroform. The second component of this study, the understanding assessment , identified the natural and human factors that may have affected the groundwater quality in the KLAM study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were aquifer lithology, land use, hydrologic conditions, depth, groundwater age, and geochemical conditions. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the KLAM study unit. Groundwater age distribution (modern, mixed, or pre-modern), redox class (oxic, mixed, or anoxic), and dissolved oxygen concentration were the explanatory factors that best explained occurrence patterns of the inorganic constituents. High concentrations of boron were found to be associated with groundwater classified as mixed or pre-modern with respect to groundwater age. Boron was also negatively correlated to dissolved oxygen and positively correlated to specific conductance. Iron and manganese concentrations were strongly associated with low dissolved oxygen concentrations, anoxic and mixed redox classifications, and pre-modern groundwater. Specific conductance concentrations were found to be related to pre-modern groundwater, low dissolved oxygen concentrations, and high pH. Chloroform was selected for additional evaluation in the understanding assessment because it was detected in more than 10 percent of wells sampled in the KLAM study unit. Septic tank density was the only explanatory factor that was found to relate to chloroform concentrations.

California↗
USGS2020

Operational Bayesian GLS regression for regional hydrologic analyses

This paper develops the quasi-analytic Bayesian analysis of the generalized least squares (GLS) (B-GLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445 ) into an operational and statistically comprehensive GLS regional hydrologic regression methodology to estimate flood quantiles, regional shape parameters, low flows, and other statistics with spatially correlated flow records. New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R 2 , pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage and influence are extended to identify rogue observations, address lack of fit, and support gauge network design and region-of-influence regression. Formulas are derived for the Bayesian computation of estimators, standard errors, and diagnostic statistics. The use of B-GLS and the new diagnostic statistics are illustrated with a regional log-space skew regression analysis for the Piedmont region in the Southeastern U.S. A comparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimator over the method-of-moment estimator of model error variance introduced by Stedinger and Tasker (1985, https://doi.org/10.1029/WR021i009p01421 ). Of the three types of analyses, only GLS considers the covariance among concurrent flows. The example demonstrates that GLS regional skewness models can be highly accurate when correctly analyzed: The B-GLS average variance of prediction is 0.090 for South Carolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S. Geological Survey yielded 0.193 (Feaster & Tasker, 2002, https://doi.org/10.3133/wri024140 ). B-GLS provides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data, allowing for the estimation of parameters and their actual precision and computation of several diagnostic statistics, as well as correctly attributing variability to the three key sources: time sampling error, model error, and signal.

Water Resources Research↗
USGS2010

A methodology for the assessment of unconventional (continuous) resources with an application to the Greater Natural Buttes gas field, Utah

The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.

Utah↗
USGS2025

Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington

Water temperature is a primary control on the occurrence and distribution of fish and other ectothermic aquatic species. In the Pacific Northwest, cold-water species such as Pacific salmon ( Oncorhynchus spp.) and bull trout ( Salvelinus confluentus ) have specific temperature requirements during different life stages that must be met to ensure the viability of their populations. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support a number of cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer baseflow during August and September, and the climatic, hydrologic, and physical processes regulating it are not well constrained. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and 7-day average daily maximum water temperature for August and September for the White River Basin. The SSN models were calibrated using water temperature measurements collected in 2010 through 2020. The extent of the models included the White River and its tributaries upstream from its confluence with Silver Creek in Mount Rainier National Park, Washington. SSN models incorporated covariates hypothesized to represent the climatic, hydrologic, and physical processes that influence water temperature. SSN models were fit to the measured data and compared to generalized linear models that lacked spatial autocovariance structures. Statistically significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow-water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. Additionally, models of mean August and September water temperature had better predictive performance than those of 7-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of the mainstem White River compared to cooler water temperatures in the high-elevation headwater streams. The proportion of ice cover emerged as an inversely related significant covariate to both mean August and September water temperature because streams that receive glacial meltwater are colder than non-glaciated streams. Water temperatures of the upper White River increased downstream and are attributed to warming of water temperature from accumulated solar radiation and inflow of non-glaciated tributaries. Estimated water temperatures for the upper White River model are 3–4 degrees Celsius (°C) warmer for tributaries, but 1–2 °C cooler for the mainstem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale NorWeST model are attributed to the fact that the upper White River SSN included water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream from the Mount Rainer National Park boundary were used in the regional scale model.

Washington↗
USGS2012

Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species&mdash;juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)&mdash;in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69&ndash;95%, 51&ndash;87%, and 86&ndash;100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.

Oregon↗