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Emerging technologies provide scientists with methods to measure Earth processes in new ways. One of these technologies--ultra-high-resolution, ground-based light detection and ranging (lidar)--is being used by USGS Western Geographic Science Center scientists to characterize the role of wind and fire processes in shaping desert landscapes of the Southwest United States.
Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.
Several future research topics for herbicide metabolites in surface and ground water are outlined in this chapter. They are herbicide usage, chemical analysis of metabolites, and fate and transport of metabolites in surface and ground water. These three ideas follow the themes in this book, which are the summary of a symposium of the American Chemical Society on herbicide metabolites in surface and ground water. First, geographic information systems allow the spatial distribution of herbicide-use data to be combined with geochemical information on fate and transport of herbicides. Next these two types of information are useful in predicting the kinds of metabolites present and their probable distribution in surface and ground water. Finally, methods development efforts may be focused on these specific target analytes. This chapter discusses these three concepts and provides an introduction to this book on the analysis, chemistry, and fate and transport of herbicide metabolites in surface and ground water.
We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.
The lake trout Salvelinus namaycush is the focus of an international effort by fishery management agencies to restore this once‐native species to Lake Ontario. Evaluation of reproductive success and comparisons among stocked lake trout strains require genetic markers. We used allozyme variation to make genetic comparisons among strains of lake trout stocked into Lake Ontario. Forty‐two proteins presumed to be encoded by 102 loci were resolved electrophoretically; 18 loci were polymorphic. Among 16 samples from five principal origins (Finger Lakes, Great Lakes basin, northern New York–Adirondack, Manitoba, and mixed origins–Lake Ontario), the average observed heterozygosity was 0.029, and the proportion of polymorphic loci was 0.125. Significant differences occurred among the 16 samples at all 18 possible locus comparisons. The average F st value was 0.14. Hierarchical analysis indicated that the variation among samples within an origin was greatest within the Great Lakes basin, which included the greatest number of samples and represented the largest geographic area. Most variation observed among samples, however, occurred among origins. The 1983 and 1984 hatchery year classes produced from the 1978 Seneca brood stock did not differ significantly. The 1981 Seneca brood stock more closely resembled the sample of wild lake trout from Seneca Lake than progeny from the 1978 brood stock. The closer affinity of the 1981 brood stock to wild Seneca lake trout may be due to the larger number of wild adults from Seneca Lake used to found these fish than was used to establish the 1978 brood stock. Siscowet (“fat”) and “lean” lake trout from Lake Superior were significantly different from each other; however, the level of variation between them was not greater than that among samples from other origins, and thus did not support recognition of siscowet lake trout as a distinct subspecies. The Jenny Lake strain possessed a genetic affinity to the siscowet sample from Lake Superior. Historical reports about the origin of this strain suggest that siscowet lake trout from northern Lake Michigan may have been used to found this strain. Therefore, the Jenny Lake strain may serve as a gene source for the establishment of the siscowet lake trout in Lake Ontario. The substantial differentiation among lake trout strains reported here supports the feasibility of using allozyme markers to identify the parental sources of naturally produced young.
'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).
This report presents the parcel and inundation area geographic information system (GIS) layers for various surface-water stages. It also presents data tables containing the water stage, inundation area, and water volume relations developed from analysis of detailed land surface elevation derived from Light Detection and Ranging (LiDAR) data recently collected for the Wood River Valley at the northern margin of Agency Lake in Klamath County, Oregon. Former shoreline wetlands that have been cut off from Upper Klamath and Agency Lakes by dikes might in the future be reconnected to Upper Klamath and Agency Lakes by breaching the dikes. Issues of interest associated with restoring wetlands in this way include the area that will be inundated, the volume of water that may be stored, the change in wetland habitat, and the variation in these characteristics as surface-water stage is changed. Products from this analysis can assist water managers in assessing the effect of breaching dikes and changing surface-water stage. The study area is in the approximate former northern margins of Upper Klamath and Agency Lakes in the Wood River Valley.
The increasing demand of the Nation's natural resource developers for the manipulation, analysis, and display of large quantities of earth-science data has necessitated the use of computers and the building of geoscience information systems. These systems require, in digital form, the spatial data on map products. The basic cartographic data shown on quadrangle maps provide a foundation for the addition of geological and geophysical data. If geoscience information systems are to realize their full potential, large amounts of digital cartographic base data must be available. A major goal of the U.S. Geological Survey is to create, maintain, manage, and distribute a national cartographic and geographic digital database. This unified database will contain numerous categories (hydrography, hypsography, land use, etc.) that, through the use of standardized data-element definitions and formats, can be used easily and flexibly to prepare cartographic products and perform geoscience analysis. ?? 1983.
Sandy beaches of the United States are some of the most popular tourist and recreational destinations. Coastal property constitutes some of the most valuable real estate in the country. Beaches are an ephemeral environment between water and land with unique and fragile natural ecosystems that have evolved in equilibrium with the ever-changing winds, waves, and water levels. Beachfront lands are the site of intense residential and commercial development even though they are highly vulnerable to several natural hazards, including marine inundation, flooding and drainage problems, effects of storms, sea-level rise, and coastal erosion. Because the U.S. population continues to shift toward the coast where valuable coastal property is vulnerable to erosion, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change. One aspect of this effort, the National Assessment of Shoreline Change, uses shoreline position as a proxy for coastal change because shoreline position is one of the most commonly monitored indicators of environmental change (for example, Fletcher, 1992; Dolan and others, 1991; Douglas and others, 1998; Galgano and others, 1998). Additionally, the National Research Council (1990) recommended the use of historical shoreline analysis in the absence of a widely accepted model of shoreline change. A principal purpose of the USGS shoreline change research is to develop a common methodology so that shoreline change analyses for the continental U.S., portions of Hawaii, and Alaska can be updated periodically in a consistent and systematic manner. The primary objectives of this study were to (1) develop and implement improved methods of assessing and monitoring shoreline movement, and (2) improve current understanding of the processes controlling shoreline movement. Achieving these ongoing long-term objectives requires research that (1) examines the original sources of shoreline data (for example, maps, air photos, global positioning system (GPS), Light Detection and Ranging (lidar)); (2) evaluates the utility of different shoreline proxies (for example, geomorphic feature, water mark, tidal datum, elevation), including the errors associated with each; (3) investigates bias and potential errors associated with integrating different shoreline proxies from different sources; (4) develops standard, uniform methods of shoreline change analysis; (5) examines the effects of human activities on shoreline movement and rates of change; and (6) investigates alternative mathematical methods for calculating historical rates of change and uncertainties associated with them. This report summarizes historical shoreline changes on the three most densely populated islands of the eight main Hawaiian Islands: Kauai, Oahu, and Maui. The report emphasizes the hazard from “chronic” (decades to centuries) erosion at regional scales and strives to relate this hazard to the body of knowledge regarding coastal geology of Hawaii because of its potential impact on natural resources, the economy, and society. Results are organized by coastal regions (island side) and sub-regions (common littoral characteristics). This report of Hawaii coasts is part of a series of reports that include text summarizing methods, results, and implications of the results. In addition, geographic information system (GIS) data used in the analyses are made available for download (Romine and others, 2012). The rates of shoreline change and products presented in this report are not intended for site-specific analysis of shoreline movement, nor are they intended to replace any official source of shoreline change information identified by local or State government agencies, or other Federal entities that are used for regulatory purposes. Rates of shoreline change presented herein may differ from other published rates, and differences do not necessarily indicate that the other rates are inaccurate. Some discrepancies are to be expected, considering the many possible ways of determining shoreline positions and rates of change, and the inherent uncertainty in calculating these rates. Rates of shoreline change presented in this report represent shoreline movement under past conditions and are not intended for use in predicting future shoreline positions or future rates of shoreline change.
Executive Summary -- Automated spatial processing technology such as geographic information systems (GIS), telemetry, and satellite-based remote sensing are some of the more recent developments in the long history of geographic inquiry. For millennia, humankind has endeavored to map the Earth's surface and identify spatial relationships. But the precision with which we can locate geographic features has increased exponentially with satellite positioning systems. Remote sensing, GIS, thematic mapping, telemetry, and satellite positioning systems such as the Global Positioning System (GPS) are tools that greatly enhance the quality and rapidity of analysis of biological resources. These technologies allow researchers, planners, and managers to more quickly and accurately determine appropriate strategies and actions. Researchers and managers can view information from new and varying perspectives using GIS and remote sensing, and GPS receivers allow the researcher or manager to identify the exact location of interest. These geospatial technologies support the mission of the U.S. Geological Survey (USGS) Biological Resources Division (BRD) and the Strategic Science Plan (BRD 1996) by providing a cost-effective and efficient method for collection, analysis, and display of information. The BRD mission is 'to work with others to provide the scientific understanding and technologies needed to support the sound management and conservation of our Nation's biological resources.' A major responsibility of the BRD is to develop and employ advanced technologies needed to synthesize, analyze, and disseminate biological and ecological information. As the Strategic Science Plan (BRD 1996) states, 'fulfilling this mission depends on effectively balancing the immediate need for information to guide management of biological resources with the need for technical assistance and long-range, strategic information to understand and predict emerging patterns and trends in ecological systems.' Information sharing plays a key role in nearly everything BRD does. The Strategic Science Plan discusses the need to (1) develop tools and standards for information transfer, (2) disseminate information, and (3) facilitate effective use of information. This effort centers around the National Biological Information Infrastructure (NBII) and the National Spatial Data Infrastructure (NSDI), components of the National Information Infrastructure. The NBII and NSDI are distributed electronic networks of biological and geographical data and information, as well as tools to help users around the world easily find and retrieve the biological and geographical data and information they need. The BRD is responsible for developing scientifically and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources. Scientists also conduct important inventory and monitoring studies to maintain baseline information on these same resources. Research on those species for which the Department of the Interior (DOI) has trust responsibilities (including endangered species and migratory species) involves laboratory and field studies of individual animals and the environments in which they live. Researchboth tactical and strategicis conducted at the BRD's 17 science centers and 81 field stations, 54 Cooperative Fish and Wildlife Research Units in 40 states, and at 11 former Cooperative Park Study Units. Studies encompass fish, birds, mammals, and plants, as well as their ecosystems and the surrounding landscape. Biological Resources Division researchers use a variety of scientific tools in their endeavors to understand the causes of biological and ecological trends. Research results are used by managers to predict environmental changes and to help them take appropriate measures to manage resources effectively. The BRD Geospatial Technology Program facilitates the collection, analysis, and dissemination of data and informat
Multiple-regression equations are presented for estimating flood-peak discharges having recurrence intervals of 2, 5, 10, 25, 50, and 100 years at ungaged sites on rural, unregulated streams in Ohio. The average standard errors of prediction for the equations range from 33.4% to 41.4%. Peak discharge estimates determined by log-Pearson Type III analysis using data collected through the 1987 water year are reported for 275 streamflow-gaging stations. Ordinary least-squares multiple-regression techniques were used to divide the State into three regions and to identify a set of basin characteristics that help explain station-to- station variation in the log-Pearson estimates. Contributing drainage area, main-channel slope, and storage area were identified as suitable explanatory variables. Generalized least-square procedures, which include historical flow data and account for differences in the variance of flows at different gaging stations, spatial correlation among gaging station records, and variable lengths of station record were used to estimate the regression parameters. Weighted peak-discharge estimates computed as a function of the log-Pearson Type III and regression estimates are reported for each station. A method is provided to adjust regression estimates for ungaged sites by use of weighted and regression estimates for a gaged site located on the same stream. Limitations and shortcomings cited in an earlier report on the magnitude and frequency of floods in Ohio are addressed in this study. Geographic bias is no longer evident for the Maumee River basin of northwestern Ohio. No bias is found to be associated with the forested-area characteristic for the range used in the regression analysis (0.0 to 99.0%), nor is this characteristic significant in explaining peak discharges. Surface-mined area likewise is not significant in explaining peak discharges, and the regression equations are not biased when applied to basins having approximately 30% or less surface-mined area. Analyses of residuals indicate that the equations tend to overestimate flood-peak discharges for basins having approximately 30% or more surface-mined area. (USGS)
The Seismic Research Observatory (SRO) network is generating an important new data base for seismological research. The SRO systems have extended both the range and resolution of seismic measurements beyond the limits of conventional seismographs and the data are recorded in digital format making it convenient to automate processing and analysis. The SRO network now comprises 12 stations and one additional station will be installed. The data produced by the SRO network, and other digital systems being installed to supplement the SRO network, will be collected by the U.S. Geological Survey's Albuquerque Seismological Laboratory, organized into network-day tapes, and made available to research organizations throughout the world. The network-day tapes are particularly unique and useful data files as they will contain not only seismic data but all of the information needed by an analyst to interpret the data, including station constants and calibration and other instrumental parameters. Hoffman (in preperation) describe these tapes and the format used in their compilation. The global digital data base for seismology is still relatively limited in terms of both geographical and temporal coverage. For this reason there is a tendency, perhaps, to stretch the applicability of the available data beyond the intended purposes. The danger in this is compounded by the fact that computer processing and analysis often can be done without even looking at the waveforms, and it is done with deceptively high computational precision that belies the basic accuracy of the measurements. The day may be passing when seismologists have a hands-on familiarity with the data acquisition systems, but clearly it is as important as ever that they have a knowledge of the capabilities and limitations of the instruments. The primary objective of the SRO network was to lower the threshold of event detection in the long-period band. This was achieved by using a newly-developed borehole seismometer that is capable of resolving both vertical and horizontal components of earth background noise at the quietest sites on earth. Another objective was to accommodate a large range of signla amplitudes. This was achieved by using an advanced digital recording systems which provides at least 110 dB of separation between system noise levels and clipping levels in the principal bands of interest. The broadband capability was not an initial program requirement and is not being fully utilized at present, but it adds significantly to the research potential of the network. The SRO systems have proven to be reliable in operation and the general quality of the data is excellent. Every seismograph has functional limits of accuracy, and the SRO system is no exception. The chief purpose of this report is to define these limits for the SRO system and provide the most accurate calibration data currently available. At the same time we will describe the testing program in sufficient detail so that the data user can make his own judgments regarding the effectiveness and accuracy of the tests. This report is not a final statement; further work is needed in some areas to completely define the SRO system, in some cases purely for academic reasons. Refinements in calibration will appear on the network-day tapes as the new data become available.
In 2012, the U.S. Geological Survey and the Oregon Department of Transportation began a cooperative study to demonstrate use of the Stochastic Empirical Loading and Dilution Model (SELDM) for runoff-quality analyses in Oregon. SELDM can be used to estimate stormflows, constituent concentrations, and loads from the area upstream of a stormflow discharge site, from the site of interest and in the receiving waters downstream of the discharge. SELDM also can be used to assess the potential effectiveness of best management practices (BMP) for mitigating potential effects of runoff in receiving waters. Nominally, SELDM is a highway-runoff model, but it is well suited for analysis of runoff from other land uses as well. This report provides case studies and examples to demonstrate stochastic-runoff modeling concepts and to demonstrate application of the model. Basin characteristics from six Oregon highway study sites were used to demonstrate various applications of the model. The highway catchment and upstream basin drainage areas of these study sites ranged from 3.85 to 11.83 acres and from 0.16 to 6.56 square miles, respectively. The upstream basins of two sites are urbanized, and the remaining four sites are less than 5 percent impervious. SELDM facilitates analysis by providing precipitation, pre-storm streamflow, and other variables by region or from hydrologically similar sites. In Oregon, there can be large variations in precipitation and streamflow among nearby sites. Therefore, spatially interpolated geographic information system data layers containing storm-event precipitation and pre-storm streamflow statistics specific to Oregon were created for the study using Kriging techniques. Concentrations and loads of cadmium, chloride, chromium, copper, iron, lead, nickel, phosphorus, and zinc were simulated at the six Oregon highway study sites by using statistics from sites in other areas of the country. Water‑quality datasets measured at hydrologically similar basins in the vicinity of the study sites in Oregon were selected and compiled to estimate stormflow-quality statistics for the upstream basins. The quality of highway runoff and some upstream stormflow constituents were simulated by using statistical moments (average, standard deviation, and skew) of the logarithms of data. Some upstream stormflow constituents were simulated by using transport curves, which are relations between stormflow and constituent concentrations. Stochastic analyses were done by using SELDM to demonstrate use of the model and to illustrate the types of information that stochastic analyses may provide: 1. An analysis was done to demonstrate use of dilution factors as an initial reconnaissance tool for comparing relative risk among sites. 2. An analysis of hardness-dependent, water-quality criteria was done to illustrate the effects of variations in hardness and flow on the application and interpretation of such criteria. This analysis shows that hardness-dependent criteria can vary by an order of magnitude among storm events because hardness is diluted by stormflows. 3. An analysis of uncertainties in input and output values was done to demonstrate that properly selected robust datasets are needed to represent conditions at a site of interest. This analysis shows that the rate of water-quality exceedances that are measured or simulated may depend on sample size and the luck of the draw. 4. An analysis was done to demonstrate that SELDM and other Monte Carlo models may generate extreme values from input statistics, which may or may not be feasible based on physicochemical or hydrological limits. 5. An analysis of BMP modeling methods was done to demonstrate use of the model for estimating treatment requirements for meeting water-quality objectives. 6. An analysis of the use of grab sampling and nonstochastic upstream modeling methods was done to evaluate the potential effects on modeling outcomes. Additional analyses using surrogate water-quality datasets for the upstream basin and highway catchment were provided for six Oregon study sites to illustrate the risk-based information that SELDM will produce. These analyses show that the potential effects of highway runoff on receiving-water quality downstream of the outfall depends on the ratio of drainage areas (dilution), the quality of the receiving water upstream of the highway, and the concentration of the criteria of the constituent of interest. These analyses also show that the probability of exceeding a water-quality criterion may depend on the input statistics used, thus careful selection of representative values is important.
Tables, graphs, and maps are used to portray the frequency characteristics and spatial distribution of manganese oxide-rich phase geochemical data, to characterize the northern Pacific in terms of publicly available nodule geochemical data, and to develop data portrayal methods that will facilitate data analysis. Source data are a subset of the Scripps Institute of Oceanography's Sediment Data Bank. The study area is bounded by 0° N., 40° N., 120° E., and 100° W. and is arbitrarily subdivided into 14-20°x 20° geographic subregions. Frequency distributions of trace metals characterized in the original raw data are graphed as ogives, and salient parameters are tabulated. All variables are transformed to enrichment values relative to median concentration within their host subregions. Scatter plots of all pairs of original variables and their enrichment transforms are provided as an aid to the interpretation of correlations between variables. Gridded spatial distributions of all variables are portrayed as gray-scale maps. The use of tables and graphs to portray frequency statistics and gray-scale maps to portray spatial distributions is an effective way to prepare for and facilitate multivariate data analysis.
Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine ( Pinus ponderosa ), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum ) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
Prioritization is a central component of natural resource management because conservation needs routinely exceed available resources. Waterfowl and wetland conservation programs in North America are at the forefront of landscape-scale prioritization and transboundary management decisions due to the migratory nature of ducks, geese, and swans. The growing availability of geographic information systems (GIS) and geospatial technologies has accelerated the development of multi-objective landscape prioritization models, including applications of structured decision making and multi-criteria decision analysis to spatial planning for waterfowl and wetlands at the continental scale. However, regional managers and conservationists could benefit from flexibility in downscaling continental tools, selecting objectives, and assigning weights for rapid production of spatial prioritization models at smaller spatial scales without extensive computer coding or GIS analysis. We developed a spatial value model that prioritizes landscapes at sub-continental scales (e.g., states and provinces, bird conservation regions, etc.) and provides flexibility for users to select waterfowl conservation objectives of interest and weights. Our model can be used for direct downscaling of an existing continental geospatial model or further customized with region-specific geospatial data. We illustrate how regional prioritization can vary with the spatial scale selected by the user. The spatial value modeling framework and the downscaling tool presented here could increase the use of multi-criteria decision analysis and linear value modeling in spatial landscape prioritization, while also providing flexibility for selecting scales, objectives, and weights. Our spreadsheet tool was developed specifically for use by regional biologists, conservationists, and managers and does not require knowledge of GIS software (although results can be exported from the spreadsheet for spatial analysis using GIS). Together, the model outputs and the accompanying spreadsheet tool provide a bridge between continental waterfowl conservation and regional implementation, enabling rapid, stakeholder-driven, value-explicit prioritization.
Liquefaction-induced ground failure poses substantial challenges to geotechnical earthquake engineering design. Current approaches for designing against liquefaction hazards, as specified in most seismic provisions, focus on estimating a liquefaction factor of safety ( 𝐹𝑆𝐿 ) and typically characterize earthquake loading using design parameters based on probabilistic or deterministic ground motion levels. Because 𝐹𝑆𝐿 is estimated deterministically, this basis of design neglects considerable uncertainties for estimating liquefaction triggering and its consequences and results in a lack of liquefaction-specific design criteria, particularly as structural design has advanced toward risk-targeted performance objectives. This study presents a framework for developing liquefaction-targeted design criteria based on a minimum acceptable return period of liquefaction, informed by probabilistic liquefaction hazard analysis (PLHA). PLHA quantifies annualized rates of liquefaction by considering contributions from (1) the full ground-motion probability space, and (2) uncertainties in liquefaction triggering using probabilistic models. PLHA is used in this study to characterize the current, effective return periods of 𝐹𝑆𝐿 ( 𝑇𝑅,𝐹𝑆 ) obtained from conventional liquefaction hazard analysis (CLHA) using uniform-hazard ground motions. 𝑇𝑅,𝐹𝑆 is evaluated in a parametric study of nearly 100 sites throughout the conterminous United States. The results indicate large geographic variations in acceptable liquefaction hazard levels, with implied 𝑇𝑅,𝐹𝑆 ranging between approximately 1,000 to 3,000 years. To address these inconsistencies without the computational demands of full PLHA, a framework is proposed for developing a liquefaction-targeted design peak ground acceleration, 𝑃𝐺𝐴𝐿 , for use in liquefaction models that result in consistent liquefaction design levels across all geographic locations. The mapped 𝑃𝐺𝐴𝐿 is shown to be somewhat sensitive to site-specific properties, and adjustment factors are developed and presented. The proposed 𝑃𝐺𝐴𝐿 mapping procedure produces 𝐹𝑆𝐿 estimates that are consistent with those obtained from full PLHA at a target 𝑇𝑅,𝐹𝑆 , providing a promising roadmap to incorporating PLHA concepts into current liquefaction design methods.
Dams within the Susquehanna River drainage, Pennsylvania, are potential barriers to migration of diadromous fishes, and many are under consideration for removal to facilitate fish passage. To provide useful input for prioritizing dam removal, we examined relations between landscape-scale factors and habitat suitability indices (HSIs) for native diadromous species of the Susquehanna River. We used two different methods (U.S. Fish and Wildlife Service method: Stier and Crance [1985], Ross et al. [1993a, 1993b, 1997], and Pardue [1983]; Pennsylvania State University method: Carline et al. [1994]) to calculate HSIs for several life stages of American shad Alosa sapidissima, alewives Alosa pseudoharengus, and blueback herring Alosa aestivalis and a single HSI for American eels Anguilla rostrata based on habitat variables measured at transects spaced every 5 km on six major Susquehanna River tributaries. Using geographical information systems, we calculated land use and geologic variables upstream from each transect and associated those data with HSIs calculated at each transect. We then performed canonical correlation analysis to determine how HSIs were linked to geologic and land use factors. Canonical correlation analysis identified the proportion of watershed underlain by carbonate rock as a positive correlate of HSIs for all species and life stages except American eels and juvenile blueback herring. We hypothesize that potential mechanisms linking carbonate rock to habitat suitability include increased productivity and buffering capacity. No other consistent patterns of positive or negative correlation between landscape-scale factors and HSIs were evident. This analysis will be useful for prioritizing removal of dams in the Susquehanna River drainage, because it provides a broad perspective on relationships between habitat suitability for diadromous fishes and easily measured landscape factors. This approach can be applied elsewhere to elucidate relationships between fine- and coarse-scale variables and suitability of habitat for fishes. ?? Copyright by the American Fisheries Society 2008.