Research Geologist
Landslide Hazards Program
Email:
ccerovski-darriau@usgs.gov
Office Phone:
303-273-8415
ORCID:
0000-0002-0543-0902
Location
1711 Illinois St
P.O. Box 25046
Denver
, CO
80225-0046
US
Supervisor:
Jonathan W Godt
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This dataset comprises repeat surveys of field-saturated hydraulic conductivity and sorptivity following the October 2017 Nuns and Tubbs wildfires as part of an effort to document soil-hydraulic recovery. A summary table includes associated physiographic properties for each site, including: pre-burn vegetation, lithology, soil burn severity, location, soil texture, and associated van Genuchten parameters determined using Carsel and Parrish (1988). Soil-hydraulic properties were calculated using the model of Zhang (1997) and Vandervaere et al. (2000). We separately include the raw cumulative infiltration measurements used for the calculation of soil-hydraulic properties. Lastly, this dataset includes a survey of...
Categories: Data;
Tags: Geomorphology,
Hydrology,
Sedimentology,
USGS Science Data Catalog (SDC),
fires, All tags...
geoscientificInformation,
hazard preparedness,
percolation,
permeability,
runoff,
sedimentation, Fewer tags
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During September 2017, Hurricane Maria caused widespread landsliding throughout mountainous regions of Puerto Rico, with more than 71,000 landslides being subsequently identified from aerial imagery (Hughes et al., 2019). Most landslides apparently mobilized as debris flows and occurred within soil (unconsolidated material overlying saprolite and bedrock) and saprolite overlying less-weathered rock (e.g., Bessette-Kirton et al., 2019a). To better understand the characteristics of Maria-triggered landslides, debris flows, and materials in which landslides occurred, we performed reconnaissance-level studies of 118 landslides, 46 soil exposures generally within landslide scars, 24 saprolite exposures, and 37 rock exposures....
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This dataset consists of over 800 field observations of ground failure (landslides, lateral spreading, and liquefaction) and other damage triggered by the 2019-2020 Puerto Rico earthquake sequence. The sequence started with a M4.7 earthquake on 28 December 2019, followed by many more earthquakes, including 15 larger than M5 (as of 7 July 2020). The M6.4 mainshock, which is thought to have triggered much of the observed ground failure, occurred on 7 January 2020. Most field reconnaissance efforts documented here took place as soon as possible after the mainshock, from 12-18 January 2020, to attempt to capture ephemeral data before evidence was destroyed by natural forces or repairs, but observations continued to...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Earthquake Hazards Program,
GHSC,
Geologic Hazards Science Center,
Guayanilla,
Guánica, All tags...
Landslide Hazards Program,
Peñuelas,
Ponce,
Puerto Rico,
USGS,
USGS Science Data Catalog (SDC),
United States,
earth science,
earthquake hazard,
geophysics,
landslides,
lateral spread,
liquefaction,
none,
rock fall,
seismic hazard,
seismology, Fewer tags
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This Data Release includes information to support the characterization of surface/near-surface infiltration rates of selected landslide source area materials following Hurricane Maria across Puerto Rico, USA. The dataset includes comma-delimited measurements of field-saturated hydraulic conductivity (Kfs) collected over two field campaigns (Fall 2018 and Spring 2019) as well as laboratory-derived measurements of soil/saprolite texture. The Kfs experiments were conducted within (or in the vicinity of) landslide source areas across the three primary geologic terranes on the island (Bawiec, 1998), including intrusive, volcaniclastic, and submarine basalt/chert lithologies. Depending on site conditions and the hydrologic...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: GHSC,
Geologic Hazards Science Center,
Hydrology,
Infiltration,
LHP, All tags...
Landslide Hazards Program,
Puerto Rico,
USGS,
USGS Science Data Catalog (SDC),
earth science,
landslide hazard, Fewer tags
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Accurate, high-resolution maps of bedrock outcrops are extremely valuable. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock maps. This data release contains training data created for developing a machine learning model capable of identifying exposed bedrock across the entire Sierra Nevada Mountains (California, USA). The training data consist of 20 thematic rasters in GeoTIFF format, where image labels represent three categories: rock, not rock, and no data. These training data labels were created using 0.6-m imagery from the National Agriculture Imagery Program (NAIP) acquired in 2016. Eight existing labeled sites were available from...
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