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This dataset consists of 65 magnetotelluric (MT) stations collected in 2015 near Mountain Pass, California. The U.S. Geological Survey acquired these data to create a regional conductivity model near the Mountain Pass mine. This work is in support of characterizing mineral deposits.
The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Pacific Region of the Unites States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based...
These data were compiled to evaluate the reproductive ecology of Agassiz's desert tortoises (Gopherus agassizzi) in the Sonoran Desert of California using two populations within Joshua Tree National Park, including five reproductive seasons that spanned 20 years (1997-1999, 2015-2016). Compared to their conspecifics inhabiting the Mojave Desert, the reproductive ecology of G. agassizii in the Sonoran Desert is understudied. Climatic variation between the two deserts can affect reproductive ecology, including fecundity and clutch phenology. Mature female tortoises (straight-line carapace length ≥ 20 cm) outfitted with radiotransmitters were located and X-radiographed approximately every 10-14 days during the reproductive...
Categories: Data;
Tags: Agassiz's desert tortoise,
California,
Ecology,
Geography,
Gopherus agassizii,
This dataset consists of 65 magnetotelluric (MT) stations collected in 2015 near Mountain Pass, California. The U.S. Geological Survey acquired these data to create a regional conductivity model near the Mountain Pass mine. This work is in support of characterizing mineral deposits.
Compressional- (P-) wave seismic refraction data were acquired in December 2018 and July 2019 along fourteen profiles within the spillway of Success Dam in Porterville, California. A new concrete ogee weir is planned for construction within the existing spillway, and the P-wave seismic velocity models will be used to inform further geotechnical investigations, including siting new geologic borings, and the advanced engineering design phases in terms of rock rippability and relative rock hardness/competency. Data acquisition, processing, and modeling were conducted collaboratively between the U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) Sacramento District. Data were acquired with Geometrics...
Categories: Data;
Tags: California,
GGGSC,
Geology, Geophysics, and Geochemistry Science Center,
Lake Success,
Porterville,
This dataset consists of 65 magnetotelluric (MT) stations collected in 2015 near Mountain Pass, California. The U.S. Geological Survey acquired these data to create a regional conductivity model near the Mountain Pass mine. This work is in support of characterizing mineral deposits.
This dataset consists of 42 magnetotelluric (MT) stations collected in 2017 in the Northwest Geysers, CA. The U.S. Geological Survey acquired these data as part of a project to image the heat source in the Northwest Geysers. Support was from the U.S. Geological Survey's Volcano Hazards Program.
This dataset consists of 42 magnetotelluric (MT) stations collected in 2017 in the Northwest Geysers, CA. The U.S. Geological Survey acquired these data as part of a project to image the heat source in the Northwest Geysers. Support was from the U.S. Geological Survey's Volcano Hazards Program.
This dataset consists of 42 magnetotelluric (MT) stations collected in 2017 in the Northwest Geysers, CA. The U.S. Geological Survey acquired these data as part of a project to image the heat source in the Northwest Geysers. Support was from the U.S. Geological Survey's Volcano Hazards Program.
This dataset consists of 42 magnetotelluric (MT) stations collected in 2017 in the Northwest Geysers, CA. The U.S. Geological Survey acquired these data as part of a project to image the heat source in the Northwest Geysers. Support was from the U.S. Geological Survey's Volcano Hazards Program.
Here we present the map of potential suitable habitat for Mojave menodora (Menodora spinescens var. mohavensis). The data indicate both how many models predicted each location to be potentially suitable for the species and the average standardized habitat suitability score for each location.Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. These data are intended to be used only to target future plant surveys in areas where new occurrences are most likely to benefit...
Here we present the map of potential suitable habitat for Beaver Dam breadroot (Pediomelum castoreum). The data indicate both how many models predicted each location to be potentially suitable for the species and the average standardized habitat suitability score for each location.Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. These data are intended to be used only to target future plant surveys in areas where new occurrences are most likely to benefit future...
Here we present the map of potential suitable habitat for Spanish needle onion (Allium shevockii). The data indicate both how many models predicted each location to be potentially suitable for the species and the average standardized habitat suitability score for each location.Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. These data are intended to be used only to target future plant surveys in areas where new occurrences are most likely to benefit future habitat...
Here we present the map of potential suitable habitat for Ash Meadows gum-plant (Grindelia fraxinipratensis). The data indicate both how many models predicted each location to be potentially suitable for the species and the average standardized habitat suitability score for each location.Data are presented at a spatial resolution of 10 m pixels, which was required to harmonize the original model inputs. However, maps of suitable habitat should be used at a resolution no smaller than 360 m (i.e., 36 pixels x 36 pixels), which corresponds with the resolution of the coarsest model input. These data are intended to be used only to target future plant surveys in areas where new occurrences are most likely to benefit...
This dataset consists of 65 magnetotelluric (MT) stations collected in 2015 near Mountain Pass, California. The U.S. Geological Survey acquired these data to create a regional conductivity model near the Mountain Pass mine. This work is in support of characterizing mineral deposits.
This dataset consists of 65 magnetotelluric (MT) stations collected in 2015 near Mountain Pass, California. The U.S. Geological Survey acquired these data to create a regional conductivity model near the Mountain Pass mine. This work is in support of characterizing mineral deposits.
These data represent simulated soil temperature and moisture conditions for current climate, and for future climate represented by all available climate models at two time periods during the 21st century. These data were used to: 1) quantify the direction and magnitude of expected changes in several measures of soil temperature and soil moisture, including the key variables used to distinguish the regimes used in the R and R categories; 2) assess how these changes will impact the geographic distribution of soil temperature and moisture regimes; and 3) explore the implications for using R and R categories for estimating future ecosystem resilience and resistance.
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