Publications

Coastal communities in portions of the United States axe vulnerable to storm-surge inundation from hurricanes and this vulnerability will likely increase, given predicted rises in sea level from climate change and growing coastal development. In this paper, we provide an overview of research to determine current and future societal vulnerability to hurricane storm-surge inundation and to help publ

The Ambos Nogales watershed, which surrounds the twin cities of Nogales, Arizona, United States and Nogales, Sonora, Mexico, has a history of problems related to flooding. This paper describes the process of creating a high-resolution, binational land-cover dataset to be used in modeling the Ambos Nogales watershed. The Automated Geospatial Watershed Assessment tool will be used to model the Ambos

This bulletin/professional paper focuses on the value of geoscientific information and knowledge, as provided in published government bedrock geological maps, to the mineral exploration sector. An economic model is developed that uses an attribute- ranking approach to convert geological maps into domains of mineral favourability. Information about known deposits in these (or analogous) favourabili

Marsh surface deposition, suspended sediment concentrations, tidal hydrodynamics, and marsh surface characteristics were measured within a tidal creek system of a west-central Florida, microtidal, open marine Juncus roemerianus marsh to examine the depositional relationship between a creek system and the surrounding marsh during nonstorm conditions. Results suggest that short-term marsh deposition

Hawai`i has experienced numerous destructive tsunamis and the potential for future events threatens the safety and economic well being of its coastal communities. Although tsunami-evacuation zones have been delineated, what is in these areas and how communities have chosen to develop within them has not been documented. A community-level vulnerability assessment using geographic-information-system

The two geography research programs of the U.S. Geological Survey (USGS), Land Remote Sensing and Geographic Analysis and Monitoring, have very strong relevance to the USGS mission and science strategy. In the western United States, the particular niche of these geography programs is in connecting USGS science to people and communities. Reports from the National Academy of Sciences and other or

The U.S. Geological Survey has developed and is implementing the Land Cover Trends project to estimate and describe the temporal and spatial distribution and variability of contemporary land-use and land-cover change in the United States. As part of the Land Cover Trends project, the purpose of this study was to assess land-use/land-cover change in the Sierra Nevada ecoregion for the period 1973 t

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over nor

No abstract available.

Environmental decision making involving multiple stakeholders can benefit from the use of a formal process to structure stakeholder interactions, leading to more successful outcomes than traditional discursive decision processes. There are many tools available to handle complex decision making. Here we illustrate the use of a multicriteria decision analysis (MCDA) outranking tool (PROMETHEE) to fa

Ecological considerations need to be interwoven with economic policy and planning along the United States‐Mexican border. Non‐point source pollution can have significant implications for the availability of potable water and the continued health of borderland ecosystems in arid lands. However, environmental assessments in this region present a host of unique issues and problems. A common obstacle

Machine learning describes pattern-recognition algorithms - in this case, probabilistic neural networks (PNNs). These can be computationally intensive, in part because of the nonlinear optimizer, a numerical process that calibrates the PNN by minimizing a sum of squared errors. This report suggests efficiencies that are expressed as cost and benefit. The cost is computer time needed to calibrate t