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At least 145 records · Page 8Linked to original sources
USGS2022

Quantifying streamflow depletion from groundwater pumping: A practical review of past and emerging approaches for water management

Groundwater pumping can cause reductions in streamflow (“streamflow depletion”) that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management-focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion-related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility.

Journal of the American Water Resources Associatio↗
USGS2007

Development of relations of stream stage to channel geometry and discharge for stream segments simulated with Hydrologic Simulation Program-Fortran (HSPF), Chesapeake Bay Watershed and adjacent parts of Virginia, Maryland, and Delaware

The U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (USEPA), Chesapeake Bay Program (CBP), Interstate Commission for the Potomac River Basin (ICPRB), Maryland Department of the Environment (MDE), Virginia Department of Conservation and Recreation (VADCR), and University of Maryland (UMD) are collaborating to improve the resolution of the Chesapeake Bay Regional Watershed Model (CBRWM). This watershed model uses the Hydrologic Simulation Program-Fortran (HSPF) to simulate the fate and transport of nutrients and sediment throughout the Chesapeake Bay watershed and extended areas of Virginia, Maryland, and Delaware. Information from the CBRWM is used by the CBP and other watershed managers to assess the effectiveness of water-quality improvement efforts as well as guide future management activities. A critical step in the improvement of the CBRWM framework was the development of an HSPF function table (FTABLE) for each represented stream channel. The FTABLE is used to relate stage (water depth) in a particular stream channel to associated channel surface area, channel volume, and discharge (streamflow). The primary tool used to generate an FTABLE for each stream channel is the XSECT program, a computer program that requires nine input variables used to represent channel morphology. These input variables are reach length, upstream and downstream elevation, channel bottom width, channel bankfull width, channel bankfull stage, slope of the floodplain, and Manning's roughness coefficient for the channel and floodplain. For the purpose of this study, the nine input variables were grouped into three categories: channel geometry, Manning's roughness coefficient, and channel and floodplain slope. Values of channel geometry for every stream segment represented in CBRWM were obtained by first developing regional regression models that relate basin drainage area to observed values of bankfull width, bankfull depth, and bottom width at each of the 290 USGS streamflow-gaging stations included in the areal extent of the model. These regression models were developed on the basis of data from stations in four physiographic provinces (Appalachian Plateaus, Valley and Ridge, Piedmont, and Coastal Plain) and were used to predict channel geometry for all 738 stream segments in the modeled area from associated basin drainage area. Manning's roughness coefficient for the channel and floodplain was represented in the XSECT program in two forms. First, all available field-estimated values of roughness were compiled for gaging stations in each physiographic province. The median of field-estimated values of channel and floodplain roughness for each physiographic province was applied to all respective stream segments. The second representation of Manning's roughness coefficient was to allow roughness to vary with channel depth. Roughness was estimated at each gaging station for each 1-foot depth interval. Median values of roughness were calculated for each 1-foot depth interval for all stations in each physiographic province. Channel and floodplain slope were determined for every stream segment in CBRWM using the USGS National Elevation Dataset. Function tables were generated by the XSECT program using values of channel geometry, channel and floodplain roughness, and channel and floodplain slope. The FTABLEs for each of the 290 USGS streamflow-gaging stations were evaluated by comparing observed discharge to the XSECT-derived discharge. Function table stream discharge derived using depth-varying roughness was found to be more representative of and statistically indistinguishable from values of observed stream discharge. Additionally, results of regression analysis showed that XSECT-derived discharge accounted for approximately 90 percent of the variability associated with observed discharge in each of the four physiographic provinces. The results of this study indicate that the methodology developed to generate FTABLEs for every s

Chesapeake Bay Watershed↗
USGS2025

Leveraging an observed-data likelihood improves the use of machine learning labels in a Bayesian hierarchical model for bioacoustic data

Classification of massive datasets by machine learning (ML) algorithms is promising for many scientific domains, especially wildlife monitoring programs that rely on passive acoustic surveys for detecting species. However, treating ML-predicted class labels (e.g., species identity) as truth biases inferences of focal parameters within common modeling frameworks. One solution is to model the misclassification process explicitly using human-validated true-class labels for a subset of observations. Validation by experts can present a substantial bottleneck in otherwise efficient workflows that use ML predictions. Bioacoustics practitioners seek guidance on both the quantity and process for selecting ML-labeled data to validate by an expert. We derive an alternative model formulation that jointly models human-validated and ML-predicted class labels with an observed-data likelihood (ODL) and use empirically informed simulations motivated by a real-data application to explore different probability designs for selecting class labels for validation. Simulation results suggest that with smaller validation sets the ODL formulation increases computational speed and reduces estimation error compared to a default MCMC data augmentation routine. Our methodology is transferable to applications that treat predictions from classification algorithms as the response variable of interest.

Annals of Applied Statistics↗
USGS2014

Extracellular enzyme kinetics scale with resource availability

Microbial community metabolism relies on external digestion, mediated by extracellular enzymes that break down complex organic matter into molecules small enough for cells to assimilate. We analyzed the kinetics of 40 extracellular enzymes that mediate the degradation and assimilation of carbon, nitrogen and phosphorus by diverse aquatic and terrestrial microbial communities (1160 cases). Regression analyses were conducted by habitat (aquatic and terrestrial), enzyme class (hydrolases and oxidoreductases) and assay methodology (low affinity and high affinity substrates) to relate potential reaction rates to substrate availability. Across enzyme classes and habitats, the scaling relationships between apparent V max and apparent K m followed similar power laws with exponents of 0.44 to 0.67. These exponents, called elasticities, were not statistically distinct from a central value of 0.50, which occurs when the K m of an enzyme equals substrate concentration, a condition optimal for maintenance of steady state. We also conducted an ecosystem scale analysis of ten extracellular hydrolase activities in relation to soil and sediment organic carbon (2,000–5,000 cases/enzyme) that yielded elasticities near 1.0 (0.9 ± 0.2, n = 36). At the metabolomic scale, the elasticity of extracellular enzymatic reactions is the proportionality constant that connects the C:N:P stoichiometries of organic matter and ecoenzymatic activities. At the ecosystem scale, the elasticity of extracellular enzymatic reactions shows that organic matter ultimately limits effective enzyme binding sites. Our findings suggest that one mechanism by which microbial communities maintain homeostasis is regulating extracellular enzyme expression to optimize the short-term responsiveness of substrate acquisition. The analyses also show that, like elemental stoichiometry, the fundamental attributes of enzymatic reactions can be extrapolated from biochemical to community and ecosystem scales.

Biogeochemistry↗
USGS2004

Topogrid Derived 10 Meter Resolution Digital Elevation Model of the Shenandoah National Park and Surrounding Region, Virginia

Explanation The purpose of developing a new 10m resolution DEM of the Shenandoah National Park Region was to more accurately depict geologic structure, surfical geology, and landforms of the Shenandoah National Park Region in preparation for automated landform classification. Previously, only a 30m resolution DEM was available through the National Elevation Dataset (NED). During production of the Shenandoah10m DEM of the Park the Geography Discipline of the USGS completed a revised 10m DEM to be included into the NED. However, different methodologies were used to produce the two similar DEMs. The ANUDEM algorithm was used to develop the Shenadoah DEM data. This algorithm allows for the inclusion of contours, streams, rivers, lake and water body polygons as well as spot height data to control the elevation model. A statistical analysis using over 800 National Geodetic Survey (NGS) first and second order vertical control points reveals that the Shenandoah10m DEM, produced as a part of the Appalachian Blue Ridge Landscape project, has a vertical accuracy of ?4.87 meters. The metadata for the 10m NED data reports a vertical accuracy of ?7m. A table listing the NGS control points, the elevation comparison, and the RMSE for the Shenandoah10m DEM is provided. The process of automated terrain classification involves developing statistical signatures from the DEM for each type of surficial deposit and landform type. The signature will be a measure of several characteristics derived from the elevation data including slope, aspect, planform curvature, and profile curvature. The quality of the DEM is of critical importance when extracting terrain signatures. The highest possible horizontal and vertical accuracy is required. The more accurate Shenandoah 10m DEM can now be analyzed and integrated with the geologic observations to yield statistical correlations between the two in the development of landform and surface geology mapping projects.

Open-File Report↗
USGS1985

Regression modeling of ground-water flow

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Open-File Report↗
USGS2010

Estimating length of avian incubation and nestling stages in afrotropical forest birds from interval-censored nest records

In the East Usambara Mountains in northeast Tanzania, research on the effects of forest fragmentation and disturbance on nest survival in understory birds resulted in the accumulation of 1,002 nest records between 2003 and 2008 for 8 poorly studied species. Because information on the length of the incubation and nestling stages in these species is nonexistent or sparse, our objectives in this study were (1) to estimate the length of the incubation and nestling stage and (2) to compute nest survival using these estimates in combination with calculated daily survival probability. Because our data were interval censored, we developed and applied two new statistical methods to estimate stage length. In the 8 species studied, the incubation stage lasted 9.6-21.8 days and the nestling stage 13.9-21.2 days. Combining these results with estimates of daily survival probability, we found that nest survival ranged from 6.0% to 12.5%. We conclude that our methodology for estimating stage lengths from interval-censored nest records is a reasonable and practical approach in the presence of interval-censored data. ?? 2010 The American Ornithologists' Union.

The Auk↗
USGS2011

Recommended methods for range-wide monitoring of prairie dogs in the United States

One of the greatest challenges for conserving grassland, prairie scrub, and shrub-steppe ecosystems is maintaining prairie dog populations across the landscape. Of the four species of prairie dogs found in the United States, the Utah prairie dog (Cynomys parvidens) is listed under the Endangered Species Act (ESA) as threatened, the Gunnison's prairie dog (C. gunnisoni) is a candidate for listing in a portion of its range, and the black-tailed prairie dog (C. ludovicianus) and white-tailed prairie dog (C. leucurus) have each been petitioned for listing at least once in recent history. Although the U.S. Fish and Wildlife Service (USFWS) determined listing is not warranted for either the black-tailed prairie dog or white-tailed prairie dog, the petitions and associated reviews demonstrated the need for the States to monitor and manage for self-sustaining populations. In response to these findings, a multi-State conservation effort was initiated for the nonlisted species which included the following proposed actions: (1) completing an assessment of each prairie dog species in each State, (2) developing a range-wide monitoring protocol for each species using a statistically valid sampling procedure that would allow comparable analyses across States, and (3) monitoring prairie dog status every 3-5 years depending upon the species. To date, each State has completed an assessment and currently is monitoring prairie dog status; however, for some species, the inconsistency in survey methodology has made it difficult to compare data year-to-year or State-to-State. At the Prairie Dog Conservation Team meeting held in November 2008, there was discussion regarding the use of different methods to survey prairie dogs. A recommendation from this meeting was to convene a panel in a workshop-type forum and have the panel review the different methods being used and provide recommendations for range-wide monitoring protocols for each species of prairie dog. Consequently, the Western Association of Fish and Wildlife Agencies (WAFWA), in coordination with USFWS and U.S. Geological Survey (USGS), hosted a prairie dog species survey methodology workshop January 25-28, 2010 in Fort Collins, Colorado. The workshop provided all WAFWA partners and interested parties the opportunity to present their survey methodology to a review panel made up of experts in the fields of quantitative biology, population biology, species biology, and biostatistics. This report presents the panel's survey methodology recommendations for each of the four species of prairie dogs found in the United States and, for the black-tailed prairie dog, a list of action items to facilitate implementation of the recommended methodology.

Scientific Investigations Report↗
USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗
USGS2015

Application of a process-based shallow landslide hazard model over a broad area in Central Italy

Process-based models are widely used for rainfall-induced shallow landslide forecasting. Previous studies have successfully applied the U.S. Geological Survey’s Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (Baum et al. 2002 ) to compute infiltration-driven changes in the hillslopes’ factor of safety on small scales (i.e., tens of square kilometers). Soil data input for such models are difficult to obtain across larger regions. This work describes a novel methodology for the application of TRIGRS over broad areas with relatively uniform hydrogeological properties. The study area is a 550-km 2 region in Central Italy covered by post-orogenic Quaternary sediments. Due to the lack of field data, we assigned mechanical and hydrological property values through a statistical analysis based on literature review of soils matching the local lithologies. We calibrated the model using rainfall data from 25 historical rainfall events that triggered landslides. We compared the variation of pressure head and factor of safety with the landslide occurrence to identify the best fitting input conditions. Using calibrated inputs and a soil depth model, we ran TRIGRS for the study area. Receiver operating characteristic (ROC) analysis, comparing the model’s output with a shallow landslide inventory, shows that TRIGRS effectively simulated the instability conditions in the post-orogenic complex during historical rainfall scenarios. The implication of this work is that rainfall-induced landslides over large regions may be predicted by a deterministic model, even where data on geotechnical and hydraulic properties as well as temporal changes in topography or subsurface conditions are not available.

Esino river basin, Marche region↗
USGS2021

Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA

Study Region : Rocky Mountains, United States Study Focus : Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer. New hydrological insights for region : In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.

Colorado↗
USGS2020

How processing methodologies can distort and bias power spectral density estimates of seismic background noise

Power spectral density (PSD) estimates are widely used in seismological studies to characterize background noise conditions, assess instrument performance, and study quasi‐stationary signals that are difficult to observe in the time domain. However, these studies often utilize different processing techniques, each of which can inherently bias the resulting PSD estimates. The level of smoothing, the size of the data window, and the method used for actually estimating the spectral content can all have strong influences on PSD estimates and background noise statistics. We show that although smoothing reduces the variance of the PSD estimate, the corresponding decrease in frequency resolution can eliminate or distort features of interest. For instance, popular software packages such as Incorporated Research Institutions for Seismology Modular Utility for STAatistical kNowledge Gathering (MUSTANG) and earlier versions of Portable Array Seismic Studies of the Continental Lithosphere Quick Look eXtended (PQLX), which were designed for data quality control and are effective in that regard, are less suitable for scientific studies that require accurate resolution of spectral peaks, even for peaks as broad as the primary ( ⁠ ∼ 14    s "> ∼ 14 s period) and secondary ( ⁠ ∼ 7    s "> ∼ 7 s period) microseisms. We also demonstrate how the 1 and 3 hr data windows used in MUSTANG and PQLX can be strongly influenced by energy generated from moderate‐size ( ⁠ M > ∼ 4.8 "> M > ∼ 4.8 ⁠ ) teleseismic earthquakes. The ubiquity of these events is likely skewing median ambient‐noise estimates by as much as 5 dB upward, for periods of 10–50 s at high‐quality broadband stations. Finally, we illustrate that many of the discrepancies between global low‐noise models are attributable to processing methodologies rather than fundamental differences in the underlying seismic data.

Seismological Research Letters↗
USGS2012

Integrated watershed-scale response to climate change for selected basins across the United States

A study by the U.S. Geological Survey (USGS) evaluated the hydrologic response to different projected carbon emission scenarios of the 21st century using a hydrologic simulation model. This study involved five major steps: (1) setup, calibrate and evaluated the Precipitation Runoff Modeling System (PRMS) model in 14 basins across the United States by local USGS personnel; (2) acquire selected simulated carbon emission scenarios from the World Climate Research Programme's Coupled Model Intercomparison Project; (3) statistical downscaling of these scenarios to create PRMS input files which reflect the future climatic conditions of these scenarios; (4) generate PRMS projections for the carbon emission scenarios for the 14 basins; and (5) analyze the modeled hydrologic response. This report presents an overview of this study, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the carbon emission scenarios and how this uncertainty propagates through the hydrologic simulations.

Scientific Investigations Report↗
USGS2022

Using a vegetation index to assess wetland condition in the Prairie Pothole Region of North America

Wetlands deliver a suite of ecosystem services to society. Anthropogenic activities, such as wetland drainage, have resulted in considerable wetland loss and degradation, diminishing the intrinsic value of wetland ecosystems worldwide. Protecting remaining wetlands and restoring degraded wetlands are common management practices to preserve and reclaim wetland benefits to society. Accordingly, methods for monitoring and assessing wetlands are required to evaluate their ecologic condition and outcomes of restoration activities. We used an established methodology for conducting vegetation-based assessments and describe a case study consisting of a wetland condition assessment in the Prairie Pothole Region of the North American Great Plains. We provide an overview of an existing method for selecting wetlands to sample across broad geographic distributions using a spatially balanced statistical design. We also describe site assessment protocols, including vegetation survey methods, and how field data were applied to a vegetation index that categorized wetlands according to ecologic condition. Results of the case study indicated that vegetation communities in nearly 50% of the surveyed wetlands were in very poor or poor condition, while only about 25% were considered good or very good . Approximately 70% of wetlands in native grasslands were categorized as good or very good compared to only 12% of those in reseeded grasslands (formerly cropland). In terms of informing restoration and management activities, results indicated that improved restoration practices could include a greater focus on establishing natural vegetation communities, and both restored and native prairie wetlands would benefit from enhanced management of invasive species.

Prairie Pothole Region↗
USGS2008

Development of hazard-compatible building fragility and vulnerability models

We present a methodology for transforming the structural and non-structural fragility functions in HAZUS into a format that is compatible with conventional seismic hazard analysis information. The methodology makes use of the building capacity (or pushover) curves and related building parameters provided in HAZUS. Instead of the capacity spectrum method applied in HAZUS, building response is estimated by inelastic response history analysis of corresponding single-degree-of-freedom systems under a large number of earthquake records. Statistics of the building response are used with the damage state definitions from HAZUS to derive fragility models conditioned on spectral acceleration values. Using the developed fragility models for structural and nonstructural building components, with corresponding damage state loss ratios from HAZUS, we also derive building vulnerability models relating spectral acceleration to repair costs. Whereas in HAZUS the structural and nonstructural damage states are treated as if they are independent, our vulnerability models are derived assuming "complete" nonstructural damage whenever the structural damage state is complete. We show the effects of considering this dependence on the final vulnerability models. The use of spectral acceleration (at selected vibration periods) as the ground motion intensity parameter, coupled with the careful treatment of uncertainty, makes the new fragility and vulnerability models compatible with conventional seismic hazard curves and hence useful for extensions to probabilistic damage and loss assessment.

Conference Paper↗
USGS2021

Estimating the influence of oyster reef chains on freshwater detention at the estuary scale using Landsat-8 imagery

Oyster reef chains grow in response to local hydrodynamics and can redirect flows, particularly when reef chains grow perpendicular to freshwater flow paths. Singularly, oyster reef chains can act as porous dams that may facilitate nearshore accumulation of fresh or low-salinity water, in turn creating intermediate salinities that support oyster growth and estuarine conditions. However, oyster-driven freshwater detention has only been confirmed by limited, point-scale observational data, and simplified models. Oyster reef-driven freshwater detention in real ecosystems at the estuary scale remains largely unexplored. In this study, we analyzed the visible bands in 30-m resolution remote sensing (RS) images recorded by the Operational Land Imager aboard Landsat-8 to characterize the freshwater detention effect of oyster reef chains across a set of hydrologic conditions. Our results support prior findings indicating that 30-m resolution RS images recorded by the Operational Land Imager aboard Landsat-8 are useful for analyzing coastal dynamics after atmospheric correction, despite having been originally designed for terrestrial studies. Statistical models of water-leaving reflectance revealed that freshwater detention by oyster reefs was evident across the estuary, with the greatest effect occurring in the region closest to shore. Additionally, statistical modeling results and spatial patterns apparent in the satellite images suggested that reef-driven freshwater detention occurred under high riverine discharge conditions, but was less evident when flow was low. Beyond offering insight on the potential role of oyster reefs as mediators of estuarine hydrology, this study presents a transferable methodological framework for exploring estuarine biophysical feedbacks in blackwater river estuaries using satellite remote sensing.

Florida↗
USGS2008

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

Book↗
USGS2012

Social values for ecosystem services (SolVES): Documentation and user manual, version 2.0

In response to the need for incorporating quantified and spatially explicit measures of social values into ecosystem services assessments, the Rocky Mountain Geographic Science Center (RMGSC), in collaboration with Colorado State University, developed a geographic information system (GIS) application, Social Values for Ecosystem Services (SolVES). With version 2.0 (SolVES 2.0), RMGSC has improved and extended the functionality of SolVES, which was designed to assess, map, and quantify the perceived social values of ecosystem services. Social values such as aesthetics, biodiversity, and recreation can be evaluated for various stakeholder groups as distinguished by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with the previous version, SolVES 2.0 derives a quantitative, 10-point, social-values metric, the Value Index, from a combination of spatial and nonspatial responses to public attitude and preference surveys and calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. Additionally, SolVES 2.0 integrates Maxent maximum entropy modeling software to generate more complete social value maps and to produce robust statistical models describing the relationship between the social values maps and explanatory environmental variables. The performance of these models can be evaluated for a primary study area, as well as for similar areas where primary survey data are not available but where social value mapping could potentially be completed using value-transfer methodology. SolVES 2.0 also introduces the flexibility for users to define their own social values and public uses, model any number and type of environmental variable, and modify the spatial resolution of analysis. With these enhancements, SolVES 2.0 provides an improved public domain tool for decisionmakers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among different ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

Open-File Report↗