2019 Supplemental Appropriations Activities
The Additional Supplemental Appropriations for Disaster Relief Act of 2019 (H.R. 2157) was signed by the President on June 6, 2019. The USGS received $98.5 million to support recovery and rebuilding activities in the wake of the 2018 Kīlauea volcano eruption, Hurricanes Florence and Michael, the Anchorage earthquake, and California wildfires.
USGS activities funded under the FY2019 Additional Supplemental Appropriations for Disaster Relief Requirements Act include:
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- New Hawaiian Volcano Observatory (HVO)
- Response Activities, Equipment Repair, and Hardening from the Kīlauea eruption
- Geologic Investigations of the Kīlauea Summit Collapse
- Equipment Repair and Replacement from Hurricanes Florence and Michael
- Coastal Hazard Assessments and Forecasts from Hurricane Florence
- Assessment of Landslide and Debris-Flow Impacts from California Wildfires
- Fire Behavior Models: Enhanced Support for Recovery of U.S. Department of the Interior (DOI) Lands
- Equipment Replacement and Geologic Investigations Related to the Alaska Earthquake
- Acquisition and Publication of 3D Elevation Program (3DEP) Lidar for Hurricanes and Wildfires
USGS Factsheet: 2019 Disaster Relief Act: USGS Recovery Activities
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The steep, tectonically active terrain along the Central California (USA) coast is well known to produce deadly and destructive debris flows. However, the extent to which fire affects debris-flow susceptibility in this region is an open question. We documented the occurrence of postfire debris floods and flows following the landfall of a storm that delivered intense rainfall across...
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Filter Total Items: 39
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To support Hurricane Florence impact modeling of storm-induced flooding and sediment transport, the U.S. Geological Survey (USGS) Coastal National Elevation Database (CoNED) Applications Project has created an integrated 1-meter topobathymetric digital elevation model (TBDEM) for coastal North Carolina, and South Carolina. High-resolution coastal topobathymetric data are required to...
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The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM)...
Aerial Imagery of the North Carolina Coast: 2019-10-11 Aerial Imagery of the North Carolina Coast: 2019-10-11
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM)...
Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM)...
Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM)...
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Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic...
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Kai Alexander Parker, Li H. Erikson, Jennifer Anne Thomas, Cornelis M. Nederhoff, Patrick L. Barnard, Sanne Muis
Rapid modeling of compound flooding across broad coastal regions and the necessity to include rainfall driven processes: A case study of Hurricane Florence (2018) Rapid modeling of compound flooding across broad coastal regions and the necessity to include rainfall driven processes: A case study of Hurricane Florence (2018)
In this work, we show that large-scale compound flood models developed for North and South Carolina, USA, can skillfully simulate multiple drivers of coastal flooding as confirmed by measurements collected during Hurricane Florence (2018). Besides the accuracy of representing observed water levels, the importance of individual processes was investigated. We demonstrate that across the...
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Tim Leijnse, Cornelis M. Nederhoff, Jennifer Anne Thomas, Kai Alexander Parker, Maarten van Ormondt, Li H. Erikson, Robert T. McCall, Ap van Dongeren, Andrea C. O'Neill, Patrick L. Barnard
User needs assessment for postfire debris-flow inundation hazard products User needs assessment for postfire debris-flow inundation hazard products
Debris flows are a type of mass movement that is more likely after wildfires, and while existing hazard assessments evaluate the rainfall intensities that are likely to trigger debris flows, no operational hazard assessment exists for identifying the areas where they will run out after initiation. Fifteen participants who work in a wide range of job functions associated with southern...
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Katherine R. Barnhart, Veronica Romero, Katherine R. Clifford
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The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models...
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