United States

WARC researchers partnered with Gulf Sturgeon decision makers and biologists to develop a Bayesian network model that uses habitat characteristics to predict the quantity of juvenile winter foraging habitat under alternative river discharge and timing of juvenile arrival scenarios.

WARC researchers partnered with managers and species experts to develop a Bayesian network model and a geospatial habitat characteristics dataset to predict the number of Brown Pelican breeding pairs on islands in the northern Gulf of Mexico.

WARC researchers partnered with managers and species experts to develop a Bayesian network model and a geospatial habitat characteristics dataset to predict the number of breeding pairs of Black Skimmer and Gull-billed Tern along the northern Gulf of Mexico.

WARC researchers are working to quantify the impacts of future climate and land use/land cover change on greenhouse gas emissions and reductions.

Informing Management of the Nearshore and Continental Shelf

Since 1998, airborne light detection and ranging, or lidar, capabilities have been developed and utilized to support CMHRP research projects and hazard assessments. Lidar is a remote-sensing technique that measures distance to a target by sending out light energy and detecting how long it takes the reflected pulses to return to the sensor. Lidar data provide information about the elevation, shape...

SPARROW (SPAtially Referenced Regression On Watershed attributes) models estimate the amount of a contaminant transported from inland watersheds to larger water bodies by linking monitoring data with information on watershed characteristics and contaminant sources. Interactive, online SPARROW mapping tools allow for easy access to explore relations between human activities, natural processes, and...

Research to identify areas that are most vulnerable to coastal change hazards including beach and dune erosion, long-term shoreline change, and sea-level rise.