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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 433 records · Page 24Linked to original sources
USGS2024

Enhancing assessments of coastal wetland migration potential with sea-level rise: Accounting for uncertainty in elevation data, tidal data, and future water levels

Sea-level rise rates are predicted to surpass rates of wetland vertical adjustment in the coming decades in many areas, increasing the potential for wetland submergence. Information on where wetland migration is possible can help natural resource managers for planning land acquisition or enhancing habitat connectivity to bolster adaptation of coastal wetlands to rising seas. Elevation-based models of wetland migration are often hampered with uncertainty associated with ground surface elevation, current water levels (i.e., tides and extreme water levels), and future water levels from sea-level rise. Here, we developed an approach that involved digital elevation model error reduction and the use of Monte Carlo simulations that utilize uncertainty assumptions regarding elevation error, contemporary water levels, and future sea levels to identify potential wetland migration areas. Our analyses were developed for Duvall and Nassau Counties in northeastern Florida (USA). We focus on the migration of regularly oceanic-flooded wetlands (i.e., flooded by oceanic water daily) and irregularly oceanic-flooded wetlands (i.e., flooded by oceanic water less frequently than daily). For two relative sea-level rise scenarios based on the 0.5 m and the 1.5 m global mean sea-level rise scenarios, we quantified migration by wetland flooding frequency class and identified land cover and land use types that are vulnerable to future exposure to oceanic waters. The variability in total coverage and relative coverage of wetland migration from our results highlights how topography and accelerated sea-level rise interact. Our wetland migration results communicate uncertainty by showing flooding frequency class as probabilistic outputs.

Florida↗
USGS2011

Space geodetic data improve seismic hazard assessment in California: Workshop on incorporating geodetic surface deformation data Into UCERF3; Pomona, California, 1–2 April 2010

A workshop was held to begin scientific consideration of how to incorporate space geodetic constraints on strain rates and fault slip rates into the next generation Uniform California Earthquake Rupture Forecast, version 3 (UCERF3), due to be completed in mid-2012. Principal outcomes of the meeting were (1) an assessment of secure science ready for UCERF3 applications within the next year, and (2) an agenda of new research objectives for the Southern California Earthquake Center (SCEC), the U.S. Geological Survey (USGS), and others in support of UCERF3 and related probabilistic seismic hazard assessments (PSHA). A number of goals potentially achievable within a year were identified, including (1) slip rate and fault locking depth estimates, with uncertainties or ranges, for all major and some minor faults of the extended San Andreas system; (2) strain rate estimates or bounds on rates for selected regions lying off the major faults of the San Andreas system; and (3) corrections or bounds on perturbing effects of postseismic deformation and elastic modulus heterogeneities on the observed Global Positioning System (GPS) velocity field (needed as input to models for estimating fault slip and strain rates in goals 1 and 2 above).

California↗
USGS1976

Mathematical models for non-parametric inferences from line transect data

A general mathematical theory of line transects is developed which supplies a framework for nonparametric density estimation based on either right angle or sighting distances. The probability of observing a point given its right angle distance (y) from the line is generalized to an arbitrary function g(y). Given only that g(0) = 1, it is shown there are nonparametric approaches to density estimation using the observed right angle distances. The model is then generalized to include sighting distances (r). Let f(y I r) be the conditional distribution of right angle distance given sighting distance. It is shown that nonparametric estimation based only on sighting distances requires we know the transformation of r given by f(0 I r).

Biometrics↗