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At least 163 records · Page 9Linked to original sources
USGS2025

Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020

The U.S. Geological Survey (USGS) and the Washington State Department of Ecology (Ecology) have developed watershed models of seasonal load estimates of total nitrogen (TN) and total phosphorus (TP) discharging into the Washington waters of the Salish Sea from 2005 through 2020. The modeling approach used was dynamic SPARROW (SPAtially Referenced Regressions On Watershed attributes), a statistical-physical watershed modeling technique, initially applied at large spatial scales to represent long-term average stream loads throughout a stream network, refined here to estimate seasonal TN and TP loads across watersheds to clarify upstream contributions from discernable point and nonpoint sources delivered to marine waters at surface water confluences along the shoreline and quantify when, where, and why they were high or low. Upstream contributing sources included permitted treated wastewater facilities, crop fertilizer, animal feeding operations, septic systems, urban land and stormwater, atmospheric deposition (TN only), nitrogen fixation by Red Alder Alnus rubra trees (TN only), and background geologic material (TP only). Instream load magnitudes and their source compositions varied widely across watersheds, and even within each watershed, yet the largest loads typically occurred in the large rivers during winter and fall when streamflow was highest. Likewise, instream loads were typically lowest in summer during low streamflow, yet the relative instream aquatic decay was highest. The seasonal storage lag component of those nonpoint sources was estimated to contribute a quarter of the seasonal instream load during winter and fall high streamflow and sometimes half of the instream load during summer low streamflow. A key aspect of Ecology’s current Puget Sound Nutrient Source Reduction Project is consideration of upstream watershed contributions of nutrients to their marine-water discharge points. Simulated seasonal loads carried by streams to 63 river mouth marine discharge points 9 ranged by several orders-of-magnitude for both TN and TP due to the spatial and seasonal differences in hydrologic flows, magnitude and timing of contributing sources, and in-stream decay. The Snohomish and Skagit Rivers discharged the largest TN and TP loads, yet the Samish River was shown to have some of the highest TN and TP yields and concentrations. Additionally, a reference scenario was developed to provide an estimate of the pre-industrial local and regional loads.

ESS Open Archive↗
USGS2026

Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020

The U.S. Geological Survey and the Washington State Department of Ecology (Ecology) have developed watershed models of seasonal load estimates of total nitrogen (TN) and total phosphorus (TP) discharging into the Washington State waters of the Salish Sea from 2005 through 2020. The modeling approach used was dynamic SPARROW (SPAtially Referenced Regressions On Watershed attributes), a statistical-physical watershed modeling technique, initially applied at large spatial scales to represent long-term average stream loads throughout a stream network, refined here to estimate seasonal TN and TP loads across watersheds. Upstream contributing sources included permitted treated wastewater facilities, crop fertilizer, animal feeding operations, septic systems, urban land and stormwater, atmospheric deposition (TN only), nitrogen fixation by Alnus rubra Bong. (red alder) trees (TN only), and background geologic material (TP only). Instream load magnitudes and their source compositions varied across watersheds, and even within each watershed, yet the largest loads typically occurred in the large rivers during winter and fall when streamflow was highest. Likewise, instream loads were typically lowest in summer during low streamflow, yet the relative instream aquatic decay was highest. The seasonal storage lag component of all nonpoint sources was estimated to contribute a quarter of the seasonal instream load during winter and fall high streamflow and sometimes half of the instream load during summer low streamflow. Simulated seasonal loads carried by streams to a few hundred river mouth marine discharge points ranged by several orders-of-magnitude for TN and TP due to the spatial and seasonal differences in hydrologic flows, magnitude and timing of contributing sources, and instream decay. The Snohomish and Skagit Rivers discharged the largest TN and TP loads, yet the Samish River was shown to have some of the highest TN and TP yields and concentrations. Additionally, a reference scenario estimate developed of the pre-industrial local and regional TN loads suggests that red alder tree density has increased in lower riparian areas and that treated wastewater is the dominant source in some watersheds that has led to increases in TN loading to marine waters.

British Columbia, Washington↗
USGS2019

Catchment-level estimates of nitrogen and phosphorus agricultural use from commercial fertilizer sales for the conterminous United States, 2012

Nutrient inputs from commercial agricultural fertilizer, particularly nitrogen and phosphorus, are important factors contributing to the degradation of surface-water quality and the alteration of aquatic ecosystems. Despite this importance, information about the application of fertilizer to agricultural land is not available in a consistent manner across the United States at a scale useful for regional water-quality assessment. To address this need, an approach is developed to relate commercial fertilizer sales to a set of explanatory variables using spatially referenced modeling methods. Spatially referenced modeling in this study refers to statistically relating fertilizer use, estimated from commercial fertilizer sales data, to spatially referenced data on watershed attributes. Separate models for nitrogen and phosphorus are developed to estimate elemental fertilizer use on agricultural lands for the conterminous United States at the National Hydrography Dataset Plus (NHDPlus) catchment scale for the year 2012. The approach builds on earlier efforts that use Association of American Plant Food Control Officials data on fertilizer sales to provide county-level estimates of nitrogen and phosphorus fertilizer use. The spatially referenced method improves on these efforts by allowing for varying nitrogen to phosphorus ratios at the catchment scale and expanding the set of variables used to allocate county-level sales data to the catchment scale. The models include catchment-level factors that are either primary determinants of fertilizer use, such as the acreage of different crop types, or measures reflecting the intensity of use, such as climate. Explanatory variables available only at the county scale, such as U.S. Department of Agriculture Census of Agriculture estimates of fertilizer expenditures, are included to improve the model predictions of elemental use. The nitrogen and phosphorus models explain more than 90 percent of the variation in elemental use at the state level, and the statistical approach allows for the estimation of uncertainty of predicted use in each catchment. The spatial patterns of model predictions reflect known agricultural cropping practices across the United States that transcend political boundaries, despite the county/state orientation of the fertilizer sales information. The results are expected to be useful for a variety of water-quality assessments that are intended to estimate nitrogen and phosphorus loads to streams.

Scientific Investigations Report↗
USGS2020

Are the stress drops of small earthquakes good predictors of the stress drops of moderate-to-large earthquakes?

The stress drops of small earthquakes often exhibit spatial patterns of variability. If moderate and large earthquakes follow the same spatial patterns, the stress drops of possible future damaging earthquakes could be better predicted by considering the stress drops of nearby small events. Better stress drop predictability could reduce ground-motion uncertainty in Probabilistic Seismic Hazard Assessment (PSHA) and Earthquake Early Warning (EEW). I find that for an internally consistent stress drop catalog of M1.8-3.1 events in southern California, the stress drops of the bigger earthquakes are predictable from the nearby smaller events. However, this catalog only weakly spatially correlates with another catalog of M3.0-5.8 earthquakes, and is spatially uncorrelated with five other stress drop catalogs of M≥3.4 earthquakes. For southern California events M5.5-7.5, stress drops compiled from the literature are weakly spatially correlated with the stress drops of the M1.8-3.1 events, although the correlations are not statistically significant. The lack of strong spatial correlation may be due to actual differences in the controlling factors of stress drop, for example dynamic weakening in moderate-to-large earthquakes. Alternatively, a stronger spatial correlation may exist that is obscured by methodological heterogeneity and large errors in the stress drop estimates. Either way, the stress drops of small earthquakes do not appear to be good predictors of the stress drops of nearby moderate-to-large earthquakes, at least for current techniques of stress drop estimation. If these results are representative, small-earthquake stress drops are not currently useful for substantially reducing uncertainty in PSHA and EEW.

California↗
USGS2019

A statistical forecasting approach to metapopulation viability analysis

Conservation of at‐risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two‐step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently developed statistical PVAs differ because forecasts are made conditional on models fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 yr of data on the federally threatened Chiricahua leopard frog ( Lithobates chiricahuensis ) to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50‐yr time horizon indicated that metapopulation extinction risk was <4% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 3.9% (95% CI 0–37%) by year 2066. Restoring six ponds by increasing their hydroperiod reduced extinction risk to <1% and greatly reduced uncertainty (95% CI 0–2%). Our results suggest that managers can mitigate the impacts of drought and environmental stochasticity on metapopulation viability by maintaining ponds that hold water throughout the year and keeping them free of invasive predators. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.

Ecological Applications↗
USGS2014

Status and understanding of groundwater quality in the South Coast Interior groundwater basins, 2008: California GAMA Priority Basin Project

Groundwater quality in the approximately 653-square-mile (1,691-square-kilometer) South Coast Interior Basins (SCI) study unit was investigated as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The South Coast Interior Basins study unit contains eight priority groundwater basins grouped into three study areas, Livermore, Gilroy, and Cuyama, in the Southern Coast Ranges hydrogeologic province. 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 South Coast Interior Basins study was designed to provide a spatially unbiased assessment of untreated (raw) groundwater quality within the primary aquifer system, as well as a statistically consistent basis for comparing water quality between basins. The assessment was based on water-quality and ancillary data collected by the USGS from 50 wells in 2008 and on water-quality data from the California Department of Public Health (CDPH) database. The primary aquifer system was defined by the depth intervals of the wells listed in the CDPH database for the SCI 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. The first component of this study, the status of the current quality of the groundwater resource, was assessed by using data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally occurring inorganic constituents, such as trace elements and minor ions. This status assessment is intended to characterize the quality of groundwater resources within the primary aquifer system of the SCI study unit, not the treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentration divided by the health- or aesthetic-based benchmark concentration) 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 equal to or less than a benchmark. Relative-concentrations of organic constituents and special-interest constituents were classified as “high” (relative-concentration greater than 1.0), “moderate” (relative-concentration greater than 0.1 and less than or equal to 1.0), or “low” (relative-concentration less than or equal to 0.1). Relative-concentrations of inorganic constituents were classified as “high” (relative-concentration greater than 1.0), “moderate” (relative-concentration greater than 0.5 and less than or equal to 1.0), or “low” (relative-concentration less than or equal to 0.5). Aquifer-scale proportion was used as the primary metric in the status assessment 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 areal percentage of the primary aquifer system with moderate and low relative-concentrations, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable in the SCI study unit (within 90-percent confidence intervals). Inorganic constituents (one or more) with health-based benchmarks were detected at high relative-concentrations in 29 percent of the primary aquifer system, at moderate relative-concentrations in 37 percent, and at low relative-concentrations in 34 percent. High aquifer-scale proportions of inorganic constituents primarily reflected high aquifer-scale proportions of nitrate (14 percent), boron (8.6 percent), molybdenum (8.6 percent), and arsenic (5.7 percent). In contrast, the relative-concentrations of organic constituents (one or more) were high in 1.6 percent, moderate in 2.0 percent, and low or not detected in 96 percent of the primary aquifer system. Of the 207 organic and special-interest constituents analyzed for, 15 constituents were detected. Perchlorate was found at moderate relative-concentrations in 34 percent of the aquifer. Two organic constituents were frequently detected (in greater than 10 percent of samples): the trihalomethane chloroform and the herbicide simazine. The second component of this study, the understanding assessment, identified natural and human factors that may have affected groundwater quality by evaluating land use, physical characteristics of the wells, and geochemical conditions of the aquifer. This evaluation was done by using statistical tests of correlations between these potential explanatory factors and water-quality data. Concentrations of arsenic, molybdenum, and manganese were generally greater in anoxic and pre-modern groundwater than other groundwater. In contrast, concentrations of nitrate and perchlorate were significantly higher in oxic and modern groundwater. Concentrations of simazine were greater in modern than pre-modern groundwater. Chloroform detections were positively correlated with greater urban land use. Boron concentrations and chloroform detections were higher in the Livermore study area than in the other study areas of the SCI; total dissolved solids and sulfate concentrations were greater in the Cuyama study area.

California↗
USGS2019

Using the beta distribution to analyze plant cover data

Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data. Commonly, plant abundance is quantified by measuring cover within multiple areal plots or along multiple lines randomly placed within a study area. A common practice for analyzing plant cover is to use statistical methods that rely on the normal distribution for quantifying uncertainty. This is problematic because plant cover data tend to be left‐skewed (J‐shaped), right skewed (L‐shaped) or U‐shaped and, therefore, commonly violate classic statistical assumptions, such as normality. We outline statistical analyses that explicitly account for spatial aggregation by assuming that plant cover is beta‐distributed. The beta distribution is a flexible choice because within the open unit interval it can take on a wide range of shapes (L, J, U, or a bell‐shaped). We discuss and introduce extensions to the beta distribution that address common analysis issues encountered in plant cover datasets, such as i) the treatment of zero and one cover values, ii) hierarchical data structures, and iii) observations errors. For heuristic purposes, we focus on single species analyses, but we demonstrate how the outlined methods can be generalized to more species. The assumption that plant cover is beta‐distributed allows us to estimate the degree of spatial aggregation, and the ecological significance of this new knowledge is discussed. We provide a summary of available software for analyses (emphasizing standard R packages) and include worked examples and a simulation study comparing analysis options as supplemental information. Synthesis . Previously, the state of the statistical software made it practically difficult for empirical plant ecologists to analyze their cover data correctly, but new theory and R‐packages have been developed, and this difficulty no longer exists. We recommend that empirical plant ecologists embrace the new statistical possibilities for exploring the exciting ecological features in spatial variation of plant cover.

Journal of Ecology↗
USGS2013

Status and understanding of groundwater quality in the San Francisco Bay groundwater basins, 2007—California GAMA Priority Basin Project

Groundwater quality in the approximately 620-square-mile (1,600-square-kilometer) San Francisco Bay study unit was investigated as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in the Southern Coast Ranges of California, in San Francisco, San Mateo, Santa Clara, Alameda, and Contra Costa 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 San Francisco Bay study was designed to provide a spatially unbiased assessment of the quality of untreated groundwater within the primary aquifer system, as well as a statistically consistent basis for comparing water quality throughout the State. The assessment is based on water-quality and ancillary data collected by the USGS from 79 wells in 2007 and is supplemented with water-quality data from the California Department of Public Health (CDPH) database. The primary aquifer system is defined by the depth interval of the wells listed in the CDPH database for the San Francisco Bay study unit. The quality of groundwater in shallower or deeper water-bearing zones may differ from that in the primary aquifer system; shallower groundwater may be more vulnerable to surficial contamination. The first component of this study, the status of the current quality of the groundwater resource, was assessed by using data from samples analyzed for volatile organic compounds (VOCs), pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements. Water- quality data from the CDPH database also were incorporated for this assessment. This status assessment is intended to characterize the quality of groundwater resources within the primary aquifer system of the San Francisco Bay study unit, not the treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentration divided by the benchmark concentration) were used for evaluating groundwater quality for those constituents that have Federal and (or) California benchmarks. 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 equal to or less than a benchmark. Relative-concentrations of organic and special-interest constituents were classified as low (relative- concentration ≤ 0.1), moderate (0.1 < relative- concentration ≤ 1.0), or high (relative-concentration > 1.0). Inorganic constituent relative- concentrations were classified as low (relative-concentration ≤ 0.5), moderate (0.5 < relative-concentration ≤ 1.0), or high (relative- concentration > 1.0). A lower threshold value of relative-concentration was used to distinguish between low and moderate values of organic constituents because organic constituents are generally less prevalent and have smaller relative-concentrations than naturally occurring inorganic constituents. Aquifer-scale proportion was used as the metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the primary aquifer system that has relative-concentration greater than 1.0 for a particular constituent or class of constituents; proportion 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 that have moderate and low relative-concentrations, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportion for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable in the San Francisco Bay study unit (90-percent confidence intervals). Inorganic constituents with health-based benchmarks were present at high relative-concentrations in 5.1 percent of the primary aquifer system, and at moderate relative-concentrations in 25 percent. The high aquifer-scale proportion of inorganic constituents primarily reflected high aquifer-scale proportions of barium (3.0 percent) and nitrate (2.1 percent). Inorganic constituents with secondary maximum contaminant levels were present at high relative-concentrations in 14 percent of the primary aquifer system and at moderate relative-concentrations in 33 percent. The constituents present at high relative-concentrations included total dissolved solids (7.0 percent), chloride (6.1 percent), manganese (12 percent), and iron (3.0 percent). Organic constituents with health-based benchmarks were present at high relative-concentrations in 0.6 percent and at moderate relative-concentrations in 12 percent of the primary aquifer system. Of the 202 organic constituents analyzed for, 32 were detected. Three organic constituents were frequently detected (in 10 percent or more of samples): the trihalomethane chloroform, the solvent 1,1,1-trichloroethane and the refrigerant 1,1,2-trichlorotrifluoroethane. One special-interest constituent, perchlorate, was detected at moderate relative-concentrations in 42 percent of the primary aquifer system. The second component of this work, the understanding assessment, identified some of the primary natural and human factors that may affect groundwater quality by evaluating land use, physical characteristics of the wells, and geochemical conditions of the aquifer. Results from these evaluations were used to explain the occurrence and distribution of constituents in the study unit.

California↗
USGS2018

Consequences of ignoring spatial variation in population trend when conducting a power analysis

Long-term, large-scale monitoring programs are becoming increasingly common to document status and trends of wild populations. A successful program for monitoring population trend hinges on the ability to detect the trend of interest. Power analyses are useful for quantifying the sample size needed for trend detection, given expected variation in the population. Four components of variation (within-year variation at a given site, interannual variation within a site, variation among sites in the interannual variation, and variation among sites in mean abundance or density) are commonly considered in power analyses for population trend, but a fifth is not: variation among sites in the local trend. Spatial variation in trend is expected to reduce statistical power, but the magnitude of this reduction has not been fully explored. We used computer simulations to evaluate the consequences of ignoring spatial variation in trend under a variety of sampling designs and wide ranges of other components of variation. The effect of spatial variation in trend on power was minor when other input parameters took extreme values that made the trend either very difficult or very easy to detect. However, at moderate values of the other parameters, spatial variation in trend had a strong effect, reducing statistical power by up to 60%. In some cases, ignoring spatial variation in trend resulted in an 80% probability of a Type I error (falsely detecting a trend in a stable population). Spatial variation in trend is therefore an important consideration when designing a long-term monitoring program for many species, especially those affected by local conditions at sites that are repeatedly surveyed. If variation in trend is ignored, as in most previous power analyses, the recommended sampling design will likely be insufficient to detect the trend of interest and lead to potentially false conclusions of a stable population.

Ecography↗
USGS2014

Accounting for imperfect detection and survey bias in statistical analysis of presence-only data

Aim During the past decade ecologists have attempted to estimate the parameters of species distribution models by combining locations of species presence observed in opportunistic surveys with spatially referenced covariates of occurrence. Several statistical models have been proposed for the analysis of presence-only data, but these models have largely ignored the effects of imperfect detection and survey bias. In this paper I describe a model-based approach for the analysis of presence-only data that accounts for errors in the detection of individuals and for biased selection of survey locations. Innovation I develop a hierarchical, statistical model that allows presence-only data to be analysed in conjunction with data acquired independently in planned surveys. One component of the model specifies the spatial distribution of individuals within a bounded, geographic region as a realization of a spatial point process. A second component of the model specifies two kinds of observations, the detection of individuals encountered during opportunistic surveys and the detection of individuals encountered during planned surveys. Main conclusions Using mathematical proof and simulation-based comparisons, I demonstrate that biases induced by errors in detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence-only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high-quality data (from planned surveys) can be used to leverage the information in presence-only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence-only data is widely applicable. In addition, since the point-process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.

Global Ecology and Biogeography↗
USGS2014

Please don't misuse the museum: 'declines' may be statistical

Detecting declines in populations at broad spatial scales takes enormous effort, and long-term data are often more sparse than is desired for estimating trends, identifying drivers for population changes, framing conservation decisions or taking management actions. Museum records and historic data can be available at large scales across multiple decades, and are therefore an attractive source of information on the comparative status of populations. However, changes in populations may be real (e.g., in response to environmental covariates) or resulting from variation in our ability to observe the true population response (also possibly related to environmental covariates). This is a (statistical) nuisance in understanding the true status of a population. Evaluating statistical hypotheses alongside more interesting ecological ones is important in the appropriate use of museum data. Two statistical considerations are generally applicable to use of museum records: first without initial random sampling, comparison with contemporary results cannot provide inference to the entire range of a species, and second the availability of only some individuals in a population may respond to environmental changes. Changes in the availability of individuals may reduce the proportion of the population that is present and able to be counted on a given survey event, resulting in an apparent decline even when population size is stable.

Global Change Biology↗
USGS2007

Geostatistical Modeling of Sediment Abundance in a Heterogeneous Basalt Aquifer at the Idaho National Laboratory, Idaho

The spatial distribution of sediment in the eastern Snake River Plain aquifer was evaluated and modeled to improve the parameterization of hydraulic conductivity (K) for a subregional-scale ground-water flow model being developed by the U.S. Geological Survey. The aquifer is hosted within a layered series of permeable basalts within which intercalated beds of fine-grained sediment constitute local confining units. These sediments have K values as much as six orders of magnitude lower than the most permeable basalt, and previous flow-model calibrations have shown that hydraulic conductivity is sensitive to the proportion of intercalated sediment. Stratigraphic data in the form of sediment thicknesses from 333 boreholes in and around the Idaho National Laboratory were evaluated as grouped subsets of lithologic units (composite units) corresponding to their relative time-stratigraphic position. The results indicate that median sediment abundances of the stratigraphic units below the water table are statistically invariant (stationary) in a spatial sense and provide evidence of stationarity across geologic time, as well. Based on these results, the borehole data were kriged as two-dimensional spatial data sets representing the sediment content of the layers that discretize the ground-water flow model in the uppermost 300 feet of the aquifer. Multiple indicator kriging (mIK) was used to model the geographic distribution of median sediment abundance within each layer by defining the local cumulative frequency distribution (CFD) of sediment via indicator variograms defined at multiple thresholds. The mIK approach is superior to ordinary kriging because it provides a statistically best estimate of sediment abundance (the local median) drawn from the distribution of local borehole data, independent of any assumption of normality. A methodology is proposed for delineating and constraining the assignment of hydraulic conductivity zones for parameter estimation, based on the locally estimated CFDs and relative kriging uncertainty. A kriging-based methodology improves the spatial resolution of hydraulic property zones that can be considered during parameter estimation and should improve calibration performance and sensitivity by more accurately reflecting the nuances of sediment distribution within the aquifer.

Scientific Investigations Report↗
USGS2015

Ecological effects of the harvest phase of geoduck clam (Panopea generosa Gould, 1850) aquaculture on infaunal communities in southern Puget Sound, Washington USA.

Intertidal aquaculture for geoducks ( Panopea generosa Gould, 1850) is expanding in southern Puget Sound, Washington, where gently sloping sandy beaches are used for field culture. Geoduck aquaculture contributes significantly to the regional economy, but has become controversial because of a range of unresolved questions involving potential biological impacts on marine ecosystems. From 2008 through 2012, the authors used a &ldquo;before-after-control-impact&rdquo; experimental design, emphasizing spatial scales comparable with those used by geoduck culturists to evaluate the effects of harvesting market-ready geoducks on associated benthic infaunal communities. Infauna were sampled at three different study locations in southern Puget Sound at monthly intervals before, during, and after harvests of clams, and along extralimital transects extending away from the edges of cultured plots to assess the effects of harvest activities in adjacent uncultured habitat. Using multivariate statistical approaches, strong seasonal and spatial signals in patterns of abundance were found, but there was scant evidence of effects on the community structure associated with geoduck harvest disturbances within cultured plots. Likewise, no indications of significant &ldquo;spillover&rdquo; effects of harvest on uncultured habitat adjacent to cultured plots were noted. Complementary univariate approaches revealed little evidence of harvest effects on infaunal biodiversity and indications of modest effects on populations of individual infaunal taxa. Of 10 common taxa analyzed, only three showed evidence of reduced densities, although minor, after harvests whereas the remaining seven taxa indicated either neutral responses to harvest disturbances or increased abundance either during or in the months after harvest events. It is suggested that a relatively active natural disturbance regime, including both small-scale and large-scale events that occur with comparable intensity but more frequently than geoduck harvest events in cultured plots, has facilitated assemblage-level infaunal resistance and resilience to harvest disturbances.

Washington↗
USGS2014

Development of a globally applicable model for near real-time prediction of seismically induced landslides

Substantial effort has been invested to understand where seismically induced landslides may occur in the future, as they are a costly and frequently fatal threat in mountainous regions. The goal of this work is to develop a statistical model for estimating the spatial distribution of landslides in near real-time around the globe for use in conjunction with the U.S. Geological Survey (USGS) Prompt Assessment of Global Earthquakes for Response ( PAGER ) system. This model uses standardized outputs of ground shaking from the USGS ShakeMap Atlas 2.0 to develop an empirical landslide probability model, combining shaking estimates with broadly available landslide susceptibility proxies, i.e., topographic slope, surface geology, and climate parameters. We focus on four earthquakes for which digitally mapped landslide inventories and well-constrained ShakeMaps are available. The resulting database is used to build a predictive model of the probability of landslide occurrence. The landslide database includes the Guatemala (1976), Northridge (1994), Chi-Chi (1999), and Wenchuan (2008) earthquakes. Performance of the regression model is assessed using statistical goodness-of-fit metrics and a qualitative review to determine which combination of the proxies provides both the optimum prediction of landslide-affected areas and minimizes the false alarms in non-landslide zones. Combined with near real-time ShakeMaps , these models can be used to make generalized predictions of whether or not landslides are likely to occur (and if so, where) for earthquakes around the globe, and eventually to inform loss estimates within the framework of the PAGER system.

Engineering Geology↗
USGS1999

Depth-Duration Frequency of Precipitation for Oklahoma

A regional frequency analysis was conducted to estimate the depth-duration frequency of precipitation for 12 durations in Oklahoma (15, 30, and 60 minutes; 1, 2, 3, 6, 12, and 24 hours; and 1, 3, and 7 days). Seven selected frequencies, expressed as recurrence intervals, were investigated (2, 5, 10, 25, 50, 100, and 500 years). L-moment statistics were used to summarize depth-duration data and to determine the appropriate statistical distributions. Three different rain-gage networks provided the data (15minute, 1-hour, and 1-day). The 60-minute, and 1-hour; and the 24-hour, and 1-day durations were analyzed separately. Data were used from rain-gage stations with at least 10-years of record and within Oklahoma or about 50 kilometers into bordering states. Precipitation annual maxima (depths) were determined from the data for 110 15-minute, 141 hourly, and 413 daily stations. The L-moment statistics for depths for all durations were calculated for each station using unbiased L-mo-ment estimators for the mean, L-scale, L-coefficient of variation, L-skew, and L-kur-tosis. The relation between L-skew and L-kurtosis (L-moment ratio diagram) and goodness-of-fit measures were used to select the frequency distributions. The three-parameter generalized logistic distribution was selected to model the frequencies of 15-, 30-, and 60-minute annual maxima; and the three-parameter generalized extreme-value distribution was selected to model the frequencies of 1-hour to 7-day annual maxima. The mean for each station and duration was corrected for the bias associated with fixed interval recording of precipitation amounts. The L-scale and spatially averaged L-skew statistics were used to compute the location, scale, and shape parameters of the selected distribution for each station and duration. The three parameters were used to calculate the depth-duration-frequency relations for each station. The precipitation depths for selected frequencies were contoured from weighted depth surfaces to produce maps from which the precipitation depth-duration-frequency curve for selected storm durations can be determined for any site in Oklahoma.

Water-Resources Investigations Report↗
USGS2003

A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay watershed

The U.S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced Regressions On Watershed attributes (SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, point-sources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D.C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U.S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow derived from the DEMs. This network improves upon existing digital stream data by increasing the level of spatial detail and providing consistency between the reach locations and topography. The hydrologic network also aids in illustrating the spatial patterns of predicted nutrient loads and sources contributed locally to each stream, and the percentages of nutrient load that reach Chesapeake Bay.

Environmental Monitoring and Assessment↗
USGS2018

A geostatistical state‐space model of animal densities for stream networks

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty underestimated. We developed a novel statistical method to account for spatiotemporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 yr. Increasing the number of survey sites within the network improved the performance of the nonspatial model but only marginally improved the density estimates in the spatiotemporal model. We applied this model to brook trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 yr from 1981 to 2014. We found the model including temporal and spatiotemporal autocorrelation best described young of the year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately high spatiotemporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatiotemporal correlation and higher temporal autocorrelation.

Ecological Applications↗
USGS2014

Distance to human populations influences epidemiology of respiratory disease in desert tortoises

We explored variables likely to affect health of Agassiz's desert tortoises (Gopherus agassizii) in a 1,183-km2 study area in the central Mojave Desert of California between 2005 and 2008. We evaluated 1,004 tortoises for prevalence and spatial distribution of 2 pathogens, Mycoplasma agassizii and M. testudineum, that cause upper respiratory tract disease. We defined tortoises as test-positive if they were positive by culture and/or DNA identification or positive or suspect for specific antibody for either of the two pathogens. We used covariates of habitat (vegetation, elevation, slope, and aspect), tortoise size and sex, distance from another test-positive tortoise, and anthropogenic variables (distances to roads, agricultural areas, playas, urban areas, and centroids of human-populated census blocks). We used both logistic regression models and regression trees to evaluate the 2 species of Mycoplasma separately. The prevalence of test-positive tortoises was low: 1.49% (15/1,004) for M. agassizii and 2.89% (29/1,004) for M. testudineum. The spatial distributions of test-positive tortoises for the 2 Mycoplasma species showed little overlap; only 2 tortoises were test-positive for both diseases. However, the spatial distributions did not differ statistically between the 2 species. We consistently found higher prevalence of test-positive tortoises with shorter distances to centroids of human-populated census blocks. The relationship between distance to human-populated census blocks and tortoises that are test-positive for M. agassizii and potentially M. testudineum may be related to release or escape of captive tortoises because the prevalence of M. agassizii in captive tortoises is high. Our findings have application to other species of chelonians where both domestic captive and wild populations exist. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Journal of Wildlife Management↗