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At least 343 records · Page 19Linked to original sources
USGS2013

Practical guidance on characterizing availability in resource selection functions under a use-availability design

Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.

Ecology↗
USGS2019

oSCR: A spatial capture–recapture R package for inference about spatial ecological processes

Spatial capture–recapture (SCR) methods have become widely applied in ecology. The immediate adoption of SCR is due to the fact that it resolves some major criticisms of traditional capture–recapture methods related to heterogeneity in detectabililty, and the emergence of new technologies (e.g. camera traps, non‐invasive genetics) that have vastly improved our ability to collection spatially explicit observation data on individuals. However, the utility of SCR methods reaches far beyond simply convenience and data availability. SCR presents a formal statistical framework that can be used to test explicit hypotheses about core elements of population and landscape ecology, and has profound implications for how we study animal populations. In this software note, we describe the technical basis and analytical workflow of oSCR, an R package for analyzing spatial encounter history data using a multi‐session sex‐structured likelihood. The impetus for developing oSCR was to create an accessible and transparent analysis tool that allows users to conveniently and intuitively formulate statistical models that map directly to fundamental processes of interest in spatial population ecology (e.g. space use, resource selection, density and connectivity). We have placed an emphasis on creating a transparent and accessible code base that is coupled with a logical workflow that we hope stimulates active participation in further technical developments.

Ecography↗
USGS2012

An assessment of the accuracy of the geodetic measurements that define the southern California uplift

Examination of the charge that the geodetic measurements which define the southern California uplift are seriously flawed by height-dependent systematic errors indicates that this charge is unfounded. Our review of those factors designed to minimize the effects of rod error shows that systematic errors attributable to rod miscalibration are trivial and would tend to cancel during the particularly pertinent period 1955–1965. Comparisons between the results of measurements in which the correlation between topography and signal is generally poor reveal large aseismic tilts in a number of places within and around the margins of the uplift. Especially significant in this context are the results of preuplift and postuplift levelings over routes characterized by diverse length, topography, and atmospheric conditions that produce closely matching, temporally equivalent heights for a representative bench mark within the uplift. Statistical analyses of the short-wavelength components of uplift signal and elevation along a frequently repeated survey line indicate variable correlations between the two. However, the spatial and temporal patterns of these correlations are inconsistent with their attribution to height-dependent systematic errors. Statistically significant short-wavelength correlations along this survey line are reasonably explained in part as due to real movement associated with differentially subsiding bench marks, and there is a strong likelihood that this movement tends to dominate many of the correlations.

Journal of Geophysical Research Solid Earth↗
USGS2009

Application of models to conservation planning for terrestrial birds in North America

Partners in Flight (PIF), a public–private coalition for the conservation of land birds, has developed one of four international bird conservation plans recognized under the auspices of the North American Bird Conservation Initiative (NABCI). Partners in Flight prioritized species most in need of conservation attention and set range-wide population goals for 448 species of terrestrial birds. Partnerships are now tasked with developing spatially explicit estimates of the distribution, and abundance of priority species across large ecoregions and identifying habitat acreages needed to support populations at prescribed levels. The PIF Five Elements process of conservation design identifies five steps needed to implement all bird conservation at the ecoregional scale. Habitat assessment and landscape characterization describe the current amounts of different habitat types and summarize patch characteristics, and landscape configurations that define the ability of a landscape to sustain healthy bird populations and are a valuable first step to describing the planning area before pursuing more complex species-specific models. Spatially linked database models, landscape-scale habitat suitability models, and statistical models are viable alternatives for predicting habitat suitability or bird abundance across large planning areas to help assess conservation opportunities, design landscapes to meet population objectives, and monitor change in habitat suitability or bird numbers over time. Bird conservation in the United States is a good example of the use of models in large-scale wildlife conservation planning because of its geographic extent, focus on multiple species, involvement of multiple partners, and use of simple to complex models. We provide some background on the recent development of bird conservation initiatives in the United States and the approaches used for regional conservation assessment and planning. We focus on approaches being used for landscape characterization and assessment, and bird population response modeling.

Book chapter↗
USGS2012

Compartment-based hydrodynamics and water quality modeling of a northern Everglades wetland, Florida, USA

The last remaining large remnant of softwater wetlands in the US Florida Everglades lies within the Arthur R. Marshall Loxahatchee National Wildlife Refuge. However, Refuge water quality today is impacted by pumped stormwater inflows to the eutrophic and mineral-enriched 100-km canal, which circumscribes the wetland. Optimal management is a challenge and requires scientifically based predictive tools to assess and forecast the impacts of water management on Refuge water quality. In this research, we developed a compartment-based numerical model of hydrodynamics and water quality for the Refuge. Using the numerical model, we examined the dynamics in stage, water depth, discharge from hydraulic structures along the canal, and exchange flow among canal and marsh compartments. We also investigated the transport of chloride, sulfate and total phosphorus from the canal to the marsh interior driven by hydraulic gradients as well as biological removal of sulfate and total phosphorus. The model was calibrated and validated using long-term stage and water quality data (1995-2007). Statistical analysis indicates that the model is capable of capturing the spatial (from canal to interior marsh) gradients of constituents across the Refuge. Simulations demonstrate that flow from the eutrophic and mineral-enriched canal impacts chloride and sulfate in the interior marsh. In contrast, total phosphorus in the interior marsh shows low sensitivity to intrusion and dispersive transport. We conducted a rainfall-driven scenario test in which the pumped inflow concentrations of chloride, sulfate and total phosphorus were equal to rainfall concentrations (wet deposition). This test shows that pumped inflow is the dominant factor responsible for the substantially increased chloride and sulfate concentrations in the interior marsh. Therefore, the present day Refuge should not be classified as solely a rainfall-driven or ombrotrophic wetland. The model provides an effective screening tool for studying the impacts of various water management alternatives on water quality across the Refuge, and demonstrates the practicality of similarly modeling other wetland systems. As a general rule, modeling provides one component of a multi-faceted effort to provide technical support for ecosystem management decisions.

Florida↗
USGS2000

Chamber measurement of surface-atmosphere trace gas exchange: Numerical evaluation of dependence on soil interfacial layer, and source/sink products

We employed a three-dimensional finite difference gas diffusion model to simulate the performance of chambers used to measure surface-atmosphere tace gas exchange. We found that systematic errors often result from conventional chamber design and deployment protocols, as well as key assumptions behind the estimation of trace gas exchange rates from observed concentration data. Specifically, our simulationshowed that (1) when a chamber significantly alters atmospheric mixing processes operating near the soil surface, it also nearly instantaneously enhances or suppresses the postdeployment gas exchange rate, (2) any change resulting in greater soil gas diffusivity, or greater partitioning of the diffusing gas to solid or liquid soil fractions, increases the potential for chamber-induced measurement error, and (3) all such errors are independent of the magnitude, kinetics, and/or distribution of trace gas sources, but greater for trace gas sinks with the same initial absolute flux. Finally, and most importantly, we found that our results apply to steady state as well as non-steady-state chambers, because the slow rate of gas diffusion in soil inhibits recovery of the former from their initial non-steady-state condition. Over a range of representative conditions, the error in steady state chamber estimates of the trace gas flux varied from -30 to +32%, while estimates computed by linear regression from non-steadystate chamber concentrations were 2 to 31% too small. Although such errors are relatively small in comparison to the temporal and spatial variability characteristic of trace gas exchange, they bias the summary statistics for each experiment as well as larger scale trace gas flux estimates based on them.

Journal of Geophysical Research D: Atmospheres↗
USGS2024

Agricultural tile drains increase the susceptibility of streams to longer and more intense streamflow droughts

Streamflow droughts are receiving increased attention worldwide due to their impact on the environment and economy. One region of concern is the Midwestern United States, whose agricultural productivity depends on subsurface pipes known as tile drains to improve trafficability and soil conditions for crop growth. Tile drains accomplish this by rapidly transporting surplus soil moisture and shallow groundwater from fields, resulting in reduced watershed storage. However, no work has previously examined the connection between tile drainage and streamflow drought. Here, we pose the question: does the extent of watershed-level tile drainage lead to an increased susceptibly and magnitude of streamflow droughts? To answer this, we use daily streamflow data for 122 watersheds throughout the Midwestern United States to quantify streamflow drought duration, frequency, and intensity. Using spatial multiple regression models, we find that agricultural tile drainage generates statistically significant ( p < 0.05) increases in streamflow drought duration and intensity while significantly reducing drought frequency. The magnitude of the effect of tile drainage on streamflow drought characteristics is similar to that of water table depth and precipitation seasonality, both of which are known to influence streamflow droughts. Furthermore, projected changes in regional precipitation characteristics will likely drive the installation of additional tile drainage. We find that for each 10% increase in tile-drained watershed area, streamflow drought duration and intensity increase by 0.03 d and 12%, respectively, while frequency decreases by 0.10 events/year. Such increases in tile drainage may lead to more severe streamflow droughts and have a detrimental effect on the socio-environmental usage of streams throughout the Midwest.

Illinois, Indiana, Iowa, Michigan, Minnesota, Ohio↗
USGS2011

Digital hydrologic networks supporting applications related to spatially referenced regression modeling

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based &frasl; statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.

Journal of the American Water Resources Associatio↗
USGS2008

Sediment Quality and Comparison to Historical Water Quality, Little Arkansas River Basin, South-Central Kansas, 2007

The spatial and temporal variability in streambed-sediment quality and its relation to historical water quality was assessed to provide guidance for the development of total maximum daily loads and the implementation of best-management practices in the Little Arkansas River Basin, south-central Kansas. Streambed-sediment samples were collected at 26 sites in 2007, sieved to isolate the less than 63-micron fraction (that is, the silt and clay), and analyzed for selected nutrients (total nitrogen and total phosphorus), organic and total carbon, 25 trace elements, and the radionuclides beryllium-7, cesium-137, lead-210, and radium-226. At eight sites, streambed-sediment samples also were collected and analyzed for bacteria. Particulate nitrogen, phosphorus, and organic carbon concentrations in the streambed sediment varied substantially spatially and temporally, and positive correlations among the three constituents were statistically significant. Along the main-stem Little Arkansas River, streambed-sediment concentrations of particulate nitrogen and phosphorus generally were larger at and downstream from Alta Mills, Kansas. The largest particulate nitrogen concentrations were measured in samples collected in the Emma Creek subbasin and may be related to livestock and poultry production. The largest particulate phosphorus concentrations in the basin were measured in samples collected along the main-stem Little Arkansas River downstream from Alta Mills, Kansas. Particulate nitrogen, phosphorus, and organic carbon content in the water and streambed-sediment samples typically decreased as streamflow increased. This inverse relation may be caused by an increased contribution of sediment from channel-bank sources during high flows and (or) increased particle sizes transported by the high flows. Trace element concentrations in the streambed sediment varied from site to site and typically were less than threshold-effects guidelines for possible adverse biological effects. The largest copper, lead, silver, and zinc concentrations, measured for a sample collected from Sand Creek downstream from Newton, Kansas, likely were related to urban sources of contamination. Radionuclide activities and bacterial densities in the streambed sediment varied throughout the basin. Variability in the former may be indicative of subbasin differences in the contribution of sediment from surface-soil and channel-bank sources. Streambed sediment may be useful for reconnaissance purposes to determine sources of particulate nitrogen, phosphorus, organic carbon, and other sediment-associated constituents in the basin. If flow conditions prior to streambed-sediment sampling and during water-quality sampling are considered, it may be possible to use streambed sediment as an indicator of water quality for nitrogen, phosphorus, and organic carbon. Flow conditions affect sediment-associated constituent concentrations in streambed-sediment and water samples, in part, because the sources of sediment (surface soils, channel banks) can vary with flow as can the size of the particles transported.

Scientific Investigations Report↗
USGS2018

Interannual snow accumulation variability on glaciers derived from repeat spatially extensive ground-penetrating radar surveys

There is significant uncertainty regarding the spatiotemporal distribution of seasonal snow on glaciers, despite being a fundamental component of glacier mass balance. To address this knowledge gap, we collected repeat, spatially extensive high-frequency ground-penetrating radar (GPR) observations on two glaciers in Alaska for five consecutive years. GPR measurements showed steep snow water equivalent (SWE) elevation gradients at both sites; continental Gulkana Glacier’s SWE gradient averaged 115 mm 100 m–1 and maritime Wolverine Glacier’s gradient averaged 440 mm 100 m–1 (over >1000 m). We extrapolated GPR point observations across the glacier surface using terrain parameters derived from digital elevation models as predictor variables in two statistical models (stepwise multivariable linear regression and regression trees). Elevation and proxies for wind redistribution had the greatest explanatory power, and exhibited relatively time-constant coefficients over the study period. Both statistical models yielded comparable estimates of glacier-wide average SWE (1 % average difference at Gulkana, 4 % average difference at Wolverine), although the spatial distributions produced by the models diverged in unsampled regions of the glacier, particularly at Wolverine. In total, six different methods for estimating the glacier-wide average agreed within ± 11 %. We assessed interannual variability in the spatial pattern of snow accumulation predicted by the statistical models using two quantitative metrics. Both glaciers exhibited a high degree of temporal stability, with ~85 % of the glacier area experiencing less than 25 % normalized absolute variability over this five-year interval. We found SWE at a sparse network (3 stakes per glacier) of long-term glaciological stake sites to be highly correlated with the GPR-derived glacier-wide average. We estimate that interannual variability in the spatial pattern of SWE is only a small component (4–10 % of glacier-wide average) of the total mass balance uncertainty and thus, our findings support the concept that sparse stake networks effectively measure interannual variability in winter balance on glaciers, rather than some spatially varying pattern of snow accumulation.

Alaska↗
USGS2021

A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse. To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow. The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [ R 2 ]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period. The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature. The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance ( R 2 =0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance ( R 2 =0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent ( R 2 =0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations. The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

Wyoming↗
USGS2003

Strong ground-motion prediction from Stochastic-dynamic source models

In the absence of sufficient data in the very near source, predictions of the intensity and variability of ground motions from future large earthquakes depend strongly on our ability to develop realistic models of the earthquake source. In this article we simulate near-fault strong ground motion using dynamic source models. We use a boundary integral method to simulate dynamic rupture of earthquakes by specifying dynamic source parameters (fracture energy and stress drop) as spatial random fields. We choose these quantities such that they are consistent with the statistical properties of slip heterogeneity found in finite-source models of past earthquakes. From these rupture models we compute theoretical strong-motion seismograms up to a frequency of 2 Hz for several realizations of a scenario strike-slip Mw 7.0 earthquake and compare empirical response spectra, spectra obtained from our dynamic models, and spectra determined from corresponding kinematic simulations. We find that spatial and temporal variations in slip, slip rise time, and rupture propagation consistent with dynamic rupture models exert a strong influence on near-source ground motion. Our results lead to a feasible approach to specify the variability in the rupture time distribution in kinematic models through a generalization of Andrews' (1976) result relating rupture speed to apparent fracture energy, stress drop, and crack length to 3D dynamic models. This suggests that a simplified representation of dynamic rupture may be obtained to approximate the effects of dynamic rupture without having to do full dynamic simulations.

Bulletin of the Seismological Society of America↗
USGS2024

First records distribution models to guide biosurveillance for non-native species

Quickly locating new populations of non-native species can reduce the ecological and economic costs of species invasions. However, the difficulty of predicting which new non-native species will establish, and where, has limited active post-border biosurveillance efforts. Because pathways of introduction underlie spatial patterns of establishment risk, an intuitive approach is to search for new non-native species in areas where many non-native species have first been detected in the past. We formalize this intuition via first records distribution models (FRDMs), which apply species distribution modeling methods to the collection of first occurrence records across species (i.e. one record per species). We define FRDMs as statistical models that quantify environmental conditions associated with species' first naturalized records to predict spatial patterns of establishment risk. We model the first records of non-native plants in the conterminous USA as a proof-of-concept. The novelty of FRDMs is that their inferences apply not just to the species that contributed data; they provide a rigorous framework for predicting hotspots of invasion for new non-native taxa that share a pathway of introduction with the modeled species. FRDMs can guide survey efforts for new non-native taxa at multiple scales and across ecosystems.

conterminous United States↗
USGS2012

Probabilistic assessment of beach and dune changes

The recent availability of spatially-dense airborne lidar data makes assessment of the vulnerability of beaches and dunes to storm impacts practical over long reaches of coast. As an initial test, elevations of the tops (D high) and bases (Dlow) of foredune ridges along a 55-km reach on the northern Outer Banks, NC were found to have considerable spatial variability suggesting that different parts of the barrier island would respond differently to storms. Comparing statistics of storm wave runup to D high and Dlow, we found that net erosion due to overwash and dune retreat should be greatest at the northern and southern ends of the study area and least in the central section. This predicted spatial pattern of storm-induced erosion is similar to the spatial pattern of long-term erosion of the shoreline which may be controlled by additional processes (such as gradients in longshore transport) as well as the cross-shore processes considered here. However, consider feedback where at erosional hot spots there is a deficit of sand (caused by gradients in longshore transport) which lead to lower dunes and enhanced erosional cross-shore processes, such as overwash. Hence, the erosional hot spots would be exacerbated, further increasing the vulnerability of the beach and dunes to net erosion.

Conference Paper↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗
USGS1997

Spatial analysis of temperature (BHT/DST) data and consequences for heat-flow determination in sedimentary basins

Large numbers of bottom-hole temperatures (BHTs) and temperatures measured during drill-stem tests (DSTs) are available in areas explored for hydrocarbons, but their usefulness for estimating geothermal gradients and heat-flow density is limited. We investigated a large data set of BHT and DST measurements taken in boreholes in the American Midcontinent, a geologically uniform stable cratonic area, and propose an empirical correction for BHTs based on relationships between BHTs, DSTs, and thermal logs. This empirical correction is compared with similar approaches determined for other areas. The data were analyzed by multivariate statistics prior to the BHT correction to identify anomalous measurements and quantify external influences. Spatial patterns in temperature measurements for major stratigraphic units outline relations to regional structure. Comparision of temperature and structure trend-surface residuals reveals a relationship between temperature highs and local structure highs. The anticlines, developed by continuous but intermittent movement of basement fault blocks in the Late Paleozoic, are subtle features having closures of 10-30 m and contain relatively small hydrocarbon reservoirs. The temperature anomalies of the order of 5-7 ??C may reflect fluids moving upward along fractures and faults, rather than changes in thermal conductivity resulting from different pore fluids. ?? Springer-Verlag 1997.

Conference Paper↗
USGS2011

Developing effective sampling designs for monitoring natural resources in Alaskan national parks: an example using simulations and vegetation data

Monitoring natural resources in Alaskan national parks is challenging because of their remoteness, limited accessibility, and high sampling costs. We describe an iterative, three-phased process for developing sampling designs based on our efforts to establish a vegetation monitoring program in southwest Alaska. In the first phase, we defined a sampling frame based on land ownership and specific vegetated habitats within the park boundaries and used Path Distance analysis tools to create a GIS layer that delineated portions of each park that could be feasibly accessed for ground sampling. In the second phase, we used simulations based on landcover maps to identify size and configuration of the ground sampling units (single plots or grids of plots) and to refine areas to be potentially sampled. In the third phase, we used a second set of simulations to estimate sample size and sampling frequency required to have a reasonable chance of detecting a minimum trend in vegetation cover for a specified time period and level of statistical confidence. Results of the first set of simulations indicated that a spatially balanced random sample of single plots from the most common landcover types yielded the most efficient sampling scheme. Results of the second set of simulations were compared with field data and indicated that we should be able to detect at least a 25% change in vegetation attributes over 31. years by sampling 8 or more plots per year every five years in focal landcover types. This approach would be especially useful in situations where ground sampling is restricted by access.

Alaska↗
USGS2022

Geomorphic controls on floodplain connectivity, ecosystem services, and sensitivity to climate change: An example from the lower Missouri River

Floodplains of large rivers are exploited for agricultural production, industrial and municipal development, and transportation infrastructure. Recently, increased frequency of costly floods has prompted consideration of whether offsetting benefits might accrue from management of floodplains for ecosystem services. We employed a simple inundation model for 800 km of the Lower Missouri River, USA, to evaluate spatial and temporal distributions of ecological floodplain inundation metrics and how those distributions might vary with levee removal and climatic change. The model evaluates inundation at 30 × 30 m resolution on a daily basis over 82 years of record. We quantified provisioning of waterfowl habitat and potential denitrification. Spatial variability is affected by ongoing geomorphic adjustments that affect floodplain connectivity. Statistical models indicate that available floodplain area and recent aggradation are predictive of most inundation metrics. Connectivity is sensitive to climate-change scenarios that predict increased floodplain inundation during spring waterfowl migrations; the greatest sensitivity to future climate exists where channel-floodplain geomorphology presently enhances floodplain connectivity. Evaluation of floodplain denitrification indicates that on average, the nonleveed part of the floodplain could denitrify 0.05%–1.7% of the mean annual nitrogen load of the river. Levee removal could increase this rate to only 3.6% of the nitrogen load. The capacity of floodplain connectivity to influence certain ecosystem services is highly variable in space along the Lower Missouri River and may be appreciably influenced by climate change. Hence, decisions to optimize management of large-river floodplains are likely to be highly location dependent.

Illinois, Iowa, Kansas, Missouri, Nebraska, South ↗