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USGS2009

Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration

Land use change is critical in determining the distribution, magnitude and mechanisms of terrestrial carbon budgets at the local to global scales. To date, almost all regional to global carbon cycle studies are driven by a static land use map or land use change statistics with decadal time intervals. The biases in quantifying carbon exchange between the terrestrial ecosystems and the atmosphere caused by using such land use change information have not been investigated. Here, we used the General Ensemble biogeochemical Modeling System (GEMS), along with consistent and spatially explicit land use change scenarios with different intervals (1 yr, 5 yrs, 10 yrs and static, respectively), to evaluate the impacts of land use change data frequency on estimating regional carbon sequestration in the southeastern United States. Our results indicate that ignoring the detailed fast-changing dynamics of land use can lead to a significant overestimation of carbon uptake by the terrestrial ecosystem. Regional carbon sequestration increased from 0.27 to 0.69, 0.80 and 0.97 Mg C ha −1 yr −1 when land use change data frequency shifting from 1 year to 5 years, 10 years interval and static land use information, respectively. Carbon removal by forest harvesting and prolonged cumulative impacts of historical land use change on carbon cycle accounted for the differences in carbon sequestration between static and dynamic land use change scenarios. The results suggest that it is critical to incorporate the detailed dynamics of land use change into local to global carbon cycle studies. Otherwise, it is impossible to accurately quantify the geographic distributions, magnitudes, and mechanisms of terrestrial carbon sequestration at the local to global scales.

Alabama, Georgia↗

USGS2021

General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017

The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, collects data pertaining to the surface-water resources of Missouri. Established in 1964, the Ambient Water-Quality Monitoring Network (AWQMN) consisted of 69 sites in 2017. Two additional sites from the National Water-Quality Program are included with the AWQMN sites for the analyses in this report. The sites are sampled typically from 2 to 12 times per year for physical properties, total suspended solids, nutrients, fecal indicator bacteria, and trace elements. The period of analysis for this study was from 1993 through 2017 and data analysis included 71 sites and 15 water-quality constituents plus discharge. Data analysis involved retrieving the data, conditioning the data for analysis, analyzing the data for trends, and analyzing the monitoring network to determine if potential data gaps or data redundancies exist in the network. Results from these analyses can be used to help manage the monitoring network into the future. Water-quality data were analyzed using several software packages to provide graphical and statistical information for interpretation of trends in the data at selected sites. Discharge data at selected sites were analyzed to determine the general trends during the analysis period and how the water-quality samples represented the range of daily mean discharges at each site. Water-quality data also were analyzed at selected sites to determine the relative sensitivity of selected sites and constituents to changes in data collection frequency. Trend analysis at selected sites using a simulated reduction in sampling frequency was completed to compare to trends obtained using monthly data to determine the potential degradation in the ability of determining trends from a reduced sampling frequency. The viability of using estimated discharge to evaluate long-term trends for sites with no continuous discharge was investigated. Data from sites were statistically compared in groups to determine the relative similarity (or difference) between sites for each water-quality constituent to identify potentially redundant sites in the monitoring network. Discharge-weighted long-term trends during 1993 through 2017 were analyzed for 15 water-quality constituents at 58 sites and results indicated there were significant single- or two-period trends in about 17 percent of the analyses. Some trends indicated improvement and some trends indicated deterioration of the general water quality at some sites in the AWQMN. No trend was indicated in about 31 percent of the analyses. The constituents pH, specific conductance, and total phosphorus showed the most frequent significant trends, and each of the 15 constituents examined had a significant trend at one or more sites. A total of 42 sites indicated at least 1 constituent with a significant single- or two-period trend, and 10 sites indicated 6 or more significant trends. Potential data gaps identified for computing discharge-weighted long-term trends in the monitoring network included the lack of collection of continuous discharge at 23 sites, insufficient sampling frequency for some constituents (dissolved chloride and total and dissolved lead and zinc) at some sites, insufficient temporal sample distribution (lack of at least one sample in each season per year) at some sites, and insufficient sampling frequency for some highly censored constituents (nutrients and total and dissolved lead and zinc) at some sites. Potential data gaps based on site spatial distribution were identified in 7 basins greater than 800 square miles. Potential site redundancies were identified in 4 basins that had an area greater than 500 square miles with a site density greater than 2 sites per 1,000 square miles. Potential site redundancies also were identified for nine site pairs by observing statistical similarities in the constituent data distributions. Sampling frequency was investigated to determine if reducing the sampling frequency of select constituents could provide a statistically similar data distribution. At 28 of 71 sites, 11 constituents had sufficient data collection frequency (approximately monthly) to allow for the creation of simulated datasets of various reduced data collection frequency. For the selected monitoring network sites analyzed, the data distribution of a simulated sampling frequency of four times per year or greater, roughly evenly distributed over the year, was not significantly different than the data distribution of the original monthly sampling frequency. Sites analyzed using varying simulated sampling frequencies tended to be more sensitive to sampling frequency changes if they were in basins classified as large or very large size and tended to be least sensitive in basins classified as small and medium size in the Ozark Plateaus Province. Simulated reduced frequency sampling analysis indicated that the constituents and measurements most sensitive to changes in sampling frequencies were water temperature, dissolved oxygen, discharge, and dissolved nitrate, and least sensitive were pH, total suspended solids, dissolved phosphorus, and total phosphorus. Discharge-weighted long-term trend analysis was repeated at 22 sites for 11 constituents using a simulated quarterly sampling frequency, and matched about 46 percent of the significant single-period trends identified using monthly data and about 65 percent of the analyses that indicated no trend using the monthly data.

Missouri↗

USGS2013

Computed statistics at streamgages, and methods for estimating low-flow frequency statistics and development of regional regression equations for estimating low-flow frequency statistics at ungaged locations in Missouri

The weather and precipitation patterns in Missouri vary considerably from year to year. In 2008, the statewide average rainfall was 57.34 inches and in 2012, the statewide average rainfall was 30.64 inches. This variability in precipitation and resulting streamflow in Missouri underlies the necessity for water managers and users to have reliable streamflow statistics and a means to compute select statistics at ungaged locations for a better understanding of water availability. Knowledge of surface-water availability is dependent on the streamflow data that have been collected and analyzed by the U.S. Geological Survey for more than 100 years at approximately 350 streamgages throughout Missouri. The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, computed streamflow statistics at streamgages through the 2010 water year, defined periods of drought and defined methods to estimate streamflow statistics at ungaged locations, and developed regional regression equations to compute selected streamflow statistics at ungaged locations. Streamflow statistics and flow durations were computed for 532 streamgages in Missouri and in neighboring States of Missouri. For streamgages with more than 10 years of record, Kendall’s tau was computed to evaluate for trends in streamflow data. If trends were detected, the variable length method was used to define the period of no trend. Water years were removed from the dataset from the beginning of the record for a streamgage until no trend was detected. Low-flow frequency statistics were then computed for the entire period of record and for the period of no trend if 10 or more years of record were available for each analysis. Three methods are presented for computing selected streamflow statistics at ungaged locations. The first method uses power curve equations developed for 28 selected streams in Missouri and neighboring States that have multiple streamgages on the same streams. Statistical estimates on one of these streams can be calculated at an ungaged location that has a drainage area that is between 40 percent of the drainage area of the farthest upstream streamgage and within 150 percent of the drainage area of the farthest downstream streamgage along the stream of interest. The second method may be used on any stream with a streamgage that has operated for 10 years or longer and for which anthropogenic effects have not changed the low-flow characteristics at the ungaged location since collection of the streamflow data. A ratio of drainage area of the stream at the ungaged location to the drainage area of the stream at the streamgage was computed to estimate the statistic at the ungaged location. The range of applicability is between 40- and 150-percent of the drainage area of the streamgage, and the ungaged location must be located on the same stream as the streamgage. The third method uses regional regression equations to estimate selected low-flow frequency statistics for unregulated streams in Missouri. This report presents regression equations to estimate frequency statistics for the 10-year recurrence interval and for the N-day durations of 1, 2, 3, 7, 10, 30, and 60 days. Basin and climatic characteristics were computed using geographic information system software and digital geospatial data. A total of 35 characteristics were computed for use in preliminary statewide and regional regression analyses based on existing digital geospatial data and previous studies. Spatial analyses for geographical bias in the predictive accuracy of the regional regression equations defined three low-flow regions with the State representing the three major physiographic provinces in Missouri. Region 1 includes the Central Lowlands, Region 2 includes the Ozark Plateaus, and Region 3 includes the Mississippi Alluvial Plain. A total of 207 streamgages were used in the regression analyses for the regional equations. Of the 207 U.S. Geological Survey streamgages, 77 were located in Region 1, 120 were located in Region 2, and 10 were located in Region 3. Streamgages located outside of Missouri were selected to extend the range of data used for the independent variables in the regression analyses. Streamgages included in the regression analyses had 10 or more years of record and were considered to be affected minimally by anthropogenic activities or trends. Regional regression analyses identified three characteristics as statistically significant for the development of regional equations. For Region 1, drainage area, longest flow path, and streamflow-variability index were statistically significant. The range in the standard error of estimate for Region 1 is 79.6 to 94.2 percent. For Region 2, drainage area and streamflow variability index were statistically significant, and the range in the standard error of estimate is 48.2 to 72.1 percent. For Region 3, drainage area and streamflow-variability index also were statistically significant with a range in the standard error of estimate of 48.1 to 96.2 percent. Limitations on the use of estimating low-flow frequency statistics at ungaged locations are dependent on the method used. The first method outlined for use in Missouri, power curve equations, were developed to estimate the selected statistics for ungaged locations on 28 selected streams with multiple streamgages located on the same stream. A second method uses a drainage-area ratio to compute statistics at an ungaged location using data from a single streamgage on the same stream with 10 or more years of record. Ungaged locations on these streams may use the ratio of the drainage area at an ungaged location to the drainage area at a streamgage location to scale the selected statistic value from the streamgage location to the ungaged location. This method can be used if the drainage area of the ungaged location is within 40 to 150 percent of the streamgage drainage area. The third method is the use of the regional regression equations. The limits for the use of these equations are based on the ranges of the characteristics used as independent variables and that streams must be affected minimally by anthropogenic activities.

Missouri↗

USGS2020

Assessment of methods for soil monitoring in the Adirondack region of New York

Repeated sampling to detect changes in forest soils was rarely used before 1990, but the value of soil monitoring in understanding environmental change is becoming well established. The growing number of resampling studies has shown that sampling designs and procedures must be adapted to the objectives of the monitoring program and the soils being monitored. In the Adirondack region, current priorities include the response of soils to large increases, and more recently, large decreases in acidic deposition, and changes driven by trending climate such as altered pools of soil organic carbon, as well as other unforeseen factors that will occur in the future. To improve methods and assess the feasibility of long-term soil monitoring in the Adirondack region, the United States Geological Survey (USGS) conducted a pilot project to evaluate a new sampling method for characterizing soils on a watershed basis. Results obtained with this new approach, referred to as the ADK sampling method, was compared to methods used in previous sampling conducted in 2004 as part of the Western Adirondack Stream Survey (WASS), and also to previous high-replication pit sampling in the North and South Tributary watersheds of Buck Creek (North Buck and South Buck). The number of sampling locations and spatial distribution of sampling points within watersheds differed among the methods, although pit excavation was used to obtain samples in all cases. In addition, this investigation evaluated the use of small diameter corers as a means to measure forest floor mass with greater accuracy and precision than commonly used methods such as pit excavation. Sufficient statistical power to detect ecologically relevant changes in upper profile horizons (Oe, Oa and upper 10 cm of the B) were achieved with the ADK sampling method that utilized 18 pit excavations per watershed. The sampling locations were organized within each watershed into three study areas (six sampling locations per study area) that represented the primary types of landscape within the watershed. Sampling at 18 locations per watershed was found to be nearly as effective at detecting changes as sampling at 28 locations per watershed. Numerous significant changes (P < 0.10) were detected with both 18 and 28 sampling locations at sampling intervals of 12 to 16 years. The relationship between soil data obtained with the ADK method and stream chemistry at the base of the watershed suggested that this approach adequately characterized soil variability within the watershed for the purpose of studying soil-stream linkages. Significant changes in upper B horizon calcium (P < 0.10) and Oa horizon aluminum (P < 0.01) were detected when the data from the four WASS watersheds were combined with the two Buck Creek watersheds, which suggested that there would be value in resampling other WASS watersheds previously sampled in 2004 to support a regional assessment. Study results support small diameter cores as a useful method to monitor changes in the organic matter mass of the forest floor. This method showed high reproducibility in repeated sampling tests and lower spatial variability in sample data than traditional approaches when compared on a watershed basis. Soil coring is also faster and requires less equipment than pit excavation methods, which makes it more conducive to sampling over large areas. However, organic matter mass of the forest floor determined by coring was consistently less than the values obtained by the ADK sampling method that used pit sampling and vertical horizon measurements, and also literature values of a previous Adirondack study that utilized pit sampling in which the entire horizon was collected over a measured area. However, a high correlation (R2 = 0.87) occurred between organic matter content (expressed as Mg ha-1) determined by coring and the ADK sampling method. Differing methods with regard to where sample could be collected, and how organic matter was collected for chemical analysis were the likely reasons for differences in quantification of forest floor organic mass. Collection of forest floor cores in conjunction with the ADK method is recommended to provide improved sensitivity in detecting changes in the forest floor in proximity of where full analyses of the soil profile are being done. This duel sampling approach represents an optimized method for measuring and understanding how Adirondack soils will change in the future.

New York↗

USGS1989

Statistical analysis of factors affecting landslide distribution in the new Madrid seismic zone, Tennessee and Kentucky

More than 220 large landslides along the bluffs bordering the Mississippi alluvial plain between Cairo, Ill., and Memphis, Tenn., are analyzed by discriminant analysis and multiple linear regression to determine the relative effects of slope height and steepness, stratigraphic variation, slope aspect, and proximity to the hypocenters of the 1811-12 New Madrid, Mo., earthquakes on the distribution of these landslides. Three types of landslides are analyzed: (1) old, coherent slumps and block slides, which have eroded and revegetated features and no active analogs in the area; (2) old earth flows, which are also eroded and revegetated; and (3) young rotational slumps, which are present only along near-river bluffs, and which are the only young, active landslides in the area. Discriminant analysis shows that only one characteristic differs significantly between bluffs with and without young rotational slumps: failed bluffs tend to have sand and clay at their base, which may render them more susceptible to fluvial erosion. Bluffs having old coherent slides are significantly higher, steeper, and closer to the hypocenters of the 1811-12 earthquakes than bluffs without these slides. Bluffs having old earth flows are likewise higher and closer to the earthquake hypocenters. Multiple regression analysis indicates that the distribution of young rotational slumps is affected most strongly by slope steepness: about one-third of the variation in the distribution is explained by variations in slope steepness. The distribution of old coherent slides and earth flows is affected most strongly by slope height, but the proximity to the hypocenters of the 1811-12 earthquakes also significantly affects the distribution. The results of the statistical analyses indicate that the only recently active landsliding in the area is along actively eroding river banks, where rotational slumps formed as bluffs are undercut by the river. The analyses further indicate that the old coherent slides and earth flows in the area are spatially related to the 1811-12 earthquake hypocenters and were thus probably triggered by those earthquakes. These results are consistent with findings of other recent investigations of landslides in the area that presented field, historical, and analytical evidence to demonstrate that old landslides in the area formed during the 1811-12 New Madrid earthquakes. Results of the multiple linear regression can also be used to approximate the relative susceptibility of the bluffs in the study area to seismically induced landsliding.

Kentucky, Tennessee↗

USGS2024

A ‘how-to’ guide for estimating animal diel activity using hierarchical models

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.

Journal of Animal Ecology↗

USGS2021

Nitrogen and phosphorus sources and delivery from the Mississippi/Atchafalaya River Basin: An update using 2012 SPARROW models

Nitrogen (N) and phosphorus (P) inputs throughout the Mississippi/Atchafalaya River Basin (MARB) have been linked to the Gulf of Mexico hypoxia and water‐quality problems throughout the MARB. To describe N and P loading throughout the MARB, SPAtially Referenced Regression On Watershed attributes (SPARROW) models were previously developed based on nutrient inputs and management similar to 1992 and 2002. In this study, refined SPARROW models were developed with higher resolution basin delineation, updated (2012) source inputs, improved calibration (load) targets, and additional statistical techniques than used in the previous SPARROW models. Based on the refined models, consistent with past models, N and P loads/yields were the highest from the central part of the MARB (Corn Belt) and along the Mississippi River. Agricultural activities remained the most important N and P source, but more so for N because its input, which could now be distinguished from atmospheric deposition, could be estimated. Natural loss of P from geologic material throughout the MARB was an important source, contributing about 23% of the total P from the MARB, and resulted in specific areas, such as Kentucky and Tennessee, being larger sources of P than previously estimated. This information can help managers decide where efforts will have the largest effects (highest ranked areas) on reducing nutrient loading to the Gulf hypoxia and what are the most important sources of N and P in these areas.

Mississippi/Atchafalaya River Basin↗

USGS2008

Recent sedimentation patterns within the central Atchafalaya Basin, Louisiana

Sediment deposition and storage are important functions of forested bottomlands, yet documentation and interpretation of sedimentation processes in these systems remain incomplete. Our study was located in the central Atchafalaya Basin, Louisiana, a distributary of the Mississippi River and contains the largest contiguously forested riparian wetland in North America, which suffers from high sedimentation in some areas and hypoxia in others. We established 20 floodplain transects reflecting the distribution of depositional environments within the central Basin and monitored general and local sediment deposition patterns over a three-year period (2000-2003). Deposition rate, sediment texture, bulk density, and loss on ignition (LOI, percent organic material) were determined near or just above artificial markers (clay pads) located at each station per transect. Transect mean sedimentation rates ranged from about 2 to 42 mm/yr, mean percent organic material ranged from about 7% to 28%, mean percent sand (> 63 ??) ranged from about 5% to 44%, and bulk density varied from about 0.4 to 1.3. The sites were categorized into five statistically different clusters based on sedimentation rate; most of these could be characterized by a suite of parameters that included hydroperiod, source(s) of sediment-laden water, hydraulic connectivity, flow stagnation, and local geomorphic setting along transect (levee versus backswamp), which lead to distinct spatial sedimentation patterns. Sites with low elevation (long hydroperiod), high hydraulic connectivity to multiple sources of sediment-laden water, and hydraulic damming (flow stagnation) featured the highest amounts of sediment trapping; the converse in any of these factors typically diminished sediment trapping. Based on aerial extent of clusters, the study area potentially traps 6,720,000 Mg of sediment annually, of which, 820,000 Mg represent organic materials. Thus, the Atchafalaya Basin plays a substantial role in lowland sediment (and associated contaminant) storage, including the sequestration of carbon. Findings on local sedimentation patterns may aid in management of flow to control sediment deposition and reduce hypoxia. ?? 2008, The Society of Wetland Scientists.

Wetlands↗

USGS2006

Vulnerability of recently recharged ground water in the High Plains aquifer to nitrate contamination

Nitrate concentrations greater than background levels have been detected in ground water of the High Plains aquifer. Empirically based models and corresponding maps were developed that predict the vulnerability of the aquifer to nonpoint-source nitrate contamination. The models predict the probability of detecting nitrate concentrations larger than 4 milligrams per liter in ground water of the High Plains aquifer that was recharged during the last 50 years. The models were calibrated by correlating concentrations of nitrate in ground water from 336 wells that intercept recently recharged (less than 50 years) water with anthropogenic and hydrogeologic explanatory variables. Particle-tracking simulations delineated well-contributing areas and determined well-screen depths that intercept recently recharged ground water. The models were developed using multivariate logistic regression, and a map was generated from these models using a geographic information system. Two multivariate logistic regression models of vulnerability were found to have the most statistical significance and the best model fit and predictive ability. The two models represent the Northern High Plains and the combined Central and Southern High Plains, and they indicate that ground-water vulnerability of the entire High Plains aquifer is best explained by the spatial distribution of nonirrigated and irrigated agricultural lands, organic matter of the soil, depth to the regional water table, and clay content of the unsaturated zone. Vulnerability of the Northern High Plains is greater in areas that have more nonirrigated and irrigated agricultural lands and less organic matter in the soil. The vulnerability of the Central and Southern High Plains also is greater in areas that have more nonirrigated and irrigated agricultural lands and also in areas with shallow depths to water table and less clay in the unsaturated zone. The majority (53.3 percent) of the High Plains aquifer has less than a 40-percent predicted probability of nitrate concentrations larger than 4 milligrams per liter. Approximately 21.1 percent of the High Plains aquifer has a relatively high (greater than 60 percent) predicted probability of nitrate concentrations greater than or equal to 4 milligrams per liter. Areas with relatively high predicted probability are located in the southwestern, southern, and eastern areas of the Northern High Plains, in the eastern arm of the Central High Plains, and in southern areas of the Southern High Plains. Areas of the aquifer with relatively low (less than 40 percent) predicted vulnerability to nitrate concentrations greater than or equal to 4 milligrams per liter are located in the northwestern and north-central areas of the Northern High Plains, the central and southern areas of the Central High Plains, and a band across the north-central part of the Southern High Plains. Uncertainty of these vulnerability predictions was estimated by Latin hypercube sampling to address propagation of model and data errors inherently associated with estimates of model coefficients and explanatory variables. Results of the Latin hypercube sampling simulations are presented as uncertainty maps of the lower 5th and upper 95th percentile of the output probability distribution, which represents the 90-percent prediction interval that contains the true probability of detecting nitrate greater than or equal to 4 milligrams per liter. Generally, these uncertainty maps show greater prediction uncertainty in areas with relatively higher predicted vulnerability and lower uncertainty in areas with relatively lower predicted vulnerability.

Scientific Investigations Report↗

USGS2020

Trends in concentration, loads, and sources of trace metals and nutrients in the Spokane River Watershed, northern Idaho, water years 1990–2018

A long history of mining and widespread metals contamination in the Coeur d’Alene River watershed and downstream into the Spokane River has led to the area’s designation as a Superfund site and to extensive, ongoing (as of 2020) remedial actions. Long-term water-quality and streamflow data, collected by the U.S. Geological Survey for up to 29 years at 20 sampling sites in the Coeur d’Alene, Spokane and St. Joe River watersheds, were analyzed to evaluate the impact of remedial actions on metals in surface water. Analyses focused on total and dissolved cadmium, zinc and lead. Trends in total phosphorus, total nitrogen and dissolved orthophosphate were also evaluated; although these nutrients are not constituents of concern for the Superfund site, they are important to the health of Coeur d’Alene Lake. Dissolved cadmium, zinc and lead concentrations were compared to ambient water-quality criteria at 20 sample sites. For the 12 sites with the most extensive data records, Weighted Regressions on Time, Discharge and Season (WRTDS) models were developed to estimate flow-normalized annual mean concentrations and flow-normalized annual total loads; these results were used to evaluate trends because flow-normalization dampens the impact of interannual streamflow variability on concentrations and loads. WRTDS models with Kalman filtering (WRTDS_K) were developed to estimate annual mean concentrations and annual total loads; these results were used to evaluate spatial patterns in constituent sources. Models were developed for total and dissolved cadmium, lead, and zinc; total phosphorus and nitrogen; and dissolved orthophosphate, although not all constituents were modeled for all sites due to limited sample sizes. Bootstrapped confidence intervals were constructed to determine the statistical likelihood of trends and the slope of trends in flow-normalized concentrations and loads during the period of record (13–29 years, depending on the site), water years 1999–2009, and water years 2009–18.

Idaho↗

USGS2013

Digital-image processing and image analysis of glacier ice

This document provides a methodology for extracting grain statistics from 8-bit color and grayscale images of thin sections of glacier ice—a subset of physical properties measurements typically performed on ice cores. This type of analysis is most commonly used to characterize the evolution of ice-crystal size, shape, and intercrystalline spatial relations within a large body of ice sampled by deep ice-coring projects from which paleoclimate records will be developed. However, such information is equally useful for investigating the stress state and physical responses of ice to stresses within a glacier. The methods of analysis presented here go hand-in-hand with the analysis of ice fabrics (aggregate crystal orientations) and, when combined with fabric analysis, provide a powerful method for investigating the dynamic recrystallization and deformation behaviors of bodies of ice in motion. The procedures described in this document compose a step-by-step handbook for a specific image acquisition and data reduction system built in support of U.S. Geological Survey ice analysis projects, but the general methodology can be used with any combination of image processing and analysis software. The specific approaches in this document use the FoveaPro 4 plug-in toolset to Adobe Photoshop CS5 Extended but it can be carried out equally well, though somewhat less conveniently, with software such as the image processing toolbox in MATLAB, Image-Pro Plus, or ImageJ.

Techniques and Methods↗

USGS2010

Estimating migratory game-bird productivity by integrating age ratio and banding data

Context: Reproduction is a critical component of fitness, and understanding factors that influence temporal and spatial dynamics in reproductive output is important for effective management and conservation. Although several indices of reproductive output for wide-ranging species, such as migratory birds, exist, there has been no theoretical justification for their estimators or associated measures of variance. Aims: The aims of our research were to develop statistical justification for an estimator of reproduction and associated variances on the basis of an existing national wing-collection survey and banding data, and to demonstrate the applicability of this estimator to a migratory game bird. Methods: We used a Bayesian hierarchical modelling approach to integrate wing-collection data, which provides information on population age ratios, and band-recovery data, which provides information on recovery probabilities of various age classes, for American woodcock ( Scolopax minor ) to estimate productivity and associated measures of variance. We present two models of relative vulnerability between age classes: one model assumed that adult recovery probabilities were higher, but that annual fluctuations were synchronous between the two age classes (i.e. an additive effect of age and year). The second model assumed that adults, on average, had higher recovery probabilities than did juveniles and that annual fluctuations were asynchronous through time (i.e. an interaction between age and year). Key results: Fitting our models within a hierarchical Bayesian framework efficiently incorporates the two data types into a single estimator and derives appropriate variances for the productivity estimator. Further, use of Bayesian methods enabled us to derive credible intervals that avoid the reliance on asymptotic assumptions. When applied to American woodcock data, the additive model resulted in biologically realistic and more precise age-ratio estimates each year and is adequate when the relative vulnerability to sampling only slightly varies or does not vary among components of a population (e.g. age, sex class) among years. Therefore, we recommend using woodcock indices from our analysis based on this model. Conclusions: We provide a flexible modelling framework for estimating productivity and associated variances that can incorporate ecological covariates to explore various factors that could drive annual dynamics in productivity. Applying our model to the American woodcock data indicated that assumptions about the variability in relative recovery probabilities could greatly influence the precision of our productivity estimator. Therefore, researchers should carefully consider the assumption of temporally variable relative recovery probabilities (i.e. ratio of juvenile to adults' recovery probability) for different age classes when applying this estimator. Implications: Several national and international management strategies for migratory game birds in North America rely on measures of productivity from harvest survey parts collections, without a justification of the estimator or providing estimates of precision. We derive an estimator of productivity with realistic measures of uncertainty that can be directly incorporated into management plans or ecological studies across large spatial scales.

Wildlife Research↗

USGS1990

Radarclinometry of the Earth and Venus from space-shuttle and Venera-15 imagery

The project to develop a line-integral approach to 2-dimensional radarclinometry and to bring it to the status of producing topographic maps from real radar images has been concluded. The final developments of the theory itself have involved a trial-and-error resolution of the curvature decision process at each integration step over range as follows: (1) Locally Indeterminate Azimuth-Azimuth Curvature is invoked if the range-directed path of integration is within 1 ??? in angle of the tangent to a local characteristic curve of the partial differential equation of radarclinometry (equivalent to a lapse in the necessity for an auxiliary curvature assumption); (2) Local Cylindricity is invoked if the local image isophote has a radius-of-curvature greater than 50 pixels; (3) Least-Squared Local Sphericity is invoked if the characteristic curve trends at greater than 70 ??? to the range direction (the auxiliary curvature assumption is becoming a sufficiently strong influence as to warrant the overconstraint), and (4) the default hypothesis, which is invoked most often, is the localization through the Euler/Lagrange equation from the calculus of variations of the global principle of minimization of the surface area of the terrain. The development of the set of line integrals into a 2-dimensional topographic surface is not practically achieved by branching the line integral at the range threshold, because the radarclinometry equations are too frequently coupled but weakly to the slope component in the direction of radar-azimuth, and under circumstances for which the powerfully influential auxiliary curvature assumption is too unrealistic. In other words, a line integration in radar-azimuth is far more frequently directed orthogonally to the local characteristic curve than is one carried out over range. Such orthogonality results in stepping the strike under the exclusive control of the curvature assumption. Instead, a quasi-surface-integration step is taken by modeling the dependence on initial strike of the gravitational potential energy of the vertical slab of terrain under the range-profile. The adopted starting strike for the range integral is the one which minimizes the gravitational potential energy. This radarclinometric method, in combination with my recently published method for determining an effective radar back-scattering function from one-dimensional slope statistics and image pixel-signal statistics, was applied to three images. First, to separate theoretical difficulties from experimental impediments, an artificial radar image was generated from a topographic map of the Lake Champlain West quadrangle in the Adirondack Mountains. Except for the regional trend in elevation, to which radarclinometry is insensitive by design, the agreement between the original and derived topography appears good. The morphologies agree and the range of relief is the same to within 4%. As an example of data of the highest quality available from space-borne radar at the present time, a SIR-B image of very rugged terrain in the coastal mountains of Oregon was similarly processed. The result, after filtering to redistribute photoclinometric errors about the two-dimensional spatial spectrum, agrees with ground truth almost as well. As an example of the worst possible data, in terms of signal-to-noise ratio and radar incidence angle (no detraction from the praise due the first high resolution space-borne radar-imaging of Venus intended), a Venera-15 image segment in Sedna Planitia just north-east of Sapho was processed, using Venera altimetry and Pioneer roughness data for slope statistics, in spite of the resolution mis-match. Considerably more trial-and-error filtering was required. The result appears plausible, but an error check is, of course, impossible. ?? 1990 Kluwer Academic Publishers.

Earth, Moon and Planets↗

USGS2012

Projections and downscaling of 21st century temperatures, precipitation, radiative fluxes and winds for the southwestern US, with focus on the Lake Tahoe basin

Recent projections of global climate changes in response to increasing greenhouse-gas concentrations in the atmosphere include warming in the Southwestern US and, especially, in the vicinity of Lake Tahoe of from about +3°C to +6°C by end of century and changes in precipitation on the order of 5-10 % increases or (more commonly) decreases, depending on the climate model considered. Along with these basic changes, other climate variables like solar insolation, downwelling (longwave) radiant heat, and winds may change. Together these climate changes may result in changes in the hydrology of the Tahoe basin and potential changes in lake overturning and ecological regimes. Current climate projections, however, are generally spatially too coarse (with grid cells separated by 1 to 2° latitude and longitude) for direct use in assessments of the vulnerabilities of the much smaller Tahoe basin. Thus, daily temperatures, precipitation, winds, and downward radiation fluxes from selected global projections have been downscaled by a statistical method called the constructed-analogues method onto 10 to 12 km grids over the Southwest and especially over Lake Tahoe. Precipitation, solar insolation and winds over the Tahoe basin change only moderately (and with indeterminate signs) in the downscaled projections, whereas temperatures and downward longwave fluxes increase along with imposed increases in global greenhouse-gas concentrations.

Arizona;California;Idaho;Nevada;Oregon;Utah↗

USGS2024

Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better

Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.

Colorado↗

USGS2007

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

Journal of Land Use Science↗

USGS2015

Spring plant phenology and false springs in the conterminous US during the 21st century

The onset of spring plant growth has shifted earlier in the year over the past several decades due to rising global temperatures. Earlier spring onset may cause phenological mismatches between the availability of plant resources and dependent animals, and potentially lead to more false springs, when subsequent freezing temperatures damage new plant growth. We used the extended spring indices to project changes in spring onset, defined by leaf out and by first bloom, and predicted false springs until 2100 in the conterminous United States (US) using statistically-downscaled climate projections from the Coupled Model Intercomparison Project 5 ensemble. Averaged over our study region, the median shift in spring onset was 23 days earlier in the Representative Concentration Pathway 8.5 scenario with particularly large shifts in the Western US and the Great Plains. Spatial variation in phenology was due to the influence of short-term temperature changes around the time of spring onset versus season long accumulation of warm temperatures. False spring risk increased in the Great Plains and portions of the Midwest, but remained constant or decreased elsewhere. We conclude that global climate change may have complex and spatially variable effects on spring onset and false springs, making local predictions of change difficult.

Geophysical Research Letters↗

USGS2013

Integrating resource selection information with spatial capture--recapture

1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. 2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. 3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. 4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

Methods in Ecology and Evolution↗