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R. A. Crovelli

Publications and source records attributed to R. A. Crovelli.

At least 19 recordsLinked to original sources
USGS2009

Probabilistic estimation of numbers and costs of future landslides in the San Francisco Bay region

We used historical records of damaging landslides triggered by rainstorms and a newly developed Probabilistic Landslide Assessment Cost Estimation System (PLACES) to estimate the numbers and direct costs of future landslides in the 10-county San Francisco Bay region. Historical records of damaging landslides in the region are incomplete. Therefore, our estimates of numbers and costs of future landslides are minimal estimates. The estimated mean annual number of future damaging landslides for the entire 10-county region is about 65. Santa Cruz County has the highest estimated mean annual number of damaging future landslides (about 18), whereas Napa, San Francisco, and Solano Counties have the lowest estimated mean numbers of damaging landslides (about 1 each). The estimated mean annual cost of future landslides in the entire region is about US $14.80 million (year 2000 $). The estimated mean annual cost is highest for San Mateo County ($3.24 million) and lowest for Solano County ($0.18 million). The annual per capita cost for the entire region will be about $2.10. Santa Cruz County will have the highest annual per capita cost at $8.45, whereas San Francisco County will have the lowest per capita cost at $0.31. Normalising costs by dividing by the percentage of land area with slopes equal to or greater than 17% indicates that San Francisco County will have the highest cost per square km ($7,101), whereas Santa Clara County will have the lowest cost per square km ($229). These results indicate that the San Francisco Bay region has one of the highest levels of landslide risk in the United States. Compared with landslide cost estimates from the rest of the world, the risk level in the Bay region seems high, but not exceptionally high.

Georisk↗
USGS2008

Landslide risk in the San Francisco Bay region

We have used historical records of damaging landslides triggered by rainstorms, and a newly developed Probabilistic Landslide Assessment Cost Estimation System (PLACES), to estimate the numbers and direct costs of future landslides in the San Francisco Bay region. The estimated annual cost of future landslides in the entire region is about US $15 million (year 2000 $). The estimated annual cost is highest for San Mateo County ($3.32 million) and lowest for Solano County ($0.18 million). Normalizing costs by dividing by the percentage of land area with slopes equal or greater than about 10° indicates that San Francisco County will have the highest cost per square km ($7,400), whereas Santa Clara County will have the lowest cost per square km ($230). These results indicate that the San Francisco Bay region has one of the highest levels of landslide risk in the United States. Compared to landslide cost estimates from the rest of the world, the risk level in the Bay region seems high, but not exceptionally high.

California↗
USGS2003

Probabilistic assessment methodology for continuous-type petroleum accumulations

The analytic resource assessment method, called ACCESS (Analytic Cell-based Continuous Energy Spreadsheet System), was developed to calculate estimates of petroleum resources for the geologic assessment model, called FORSPAN, in continuous-type petroleum accumulations. The ACCESS method is based upon mathematical equations derived from probability theory in the form of a computer spreadsheet system. ?? 2003 Elsevier B.V. All rights reserved.

International Journal of Coal Geology↗
USGS2002

Worldwide estimates of deep natural gas resources based on the U.S. Geological Survey World Petroleum Assessment 2000

The U.S. Geological Survey recently assessed undiscovered conventional gas and oil resources in eight regions of the world outside the U.S. The resources assessed were those estimated to have the potential to be added to reserves within the next thirty years. This study is a worldwide analysis of the estimated volumes and distribution of deep (>4.5 km or about 15,000 ft), undiscovered conventional natural gas resources based on this assessment. Two hundred forty-six assessment units in 128 priority geologic provinces, 96 countries, and two jointly held areas were assessed using a probabilistic Total Petroleum System approach. Priority geologic provinces were selected from a ranking of 937 provinces worldwide. The U.S. Geological Survey World Petroleum Assessment Team did not assess undiscovered petroleum resources in the U.S. For this report, mean estimated volumes of deep conventional undiscovered gas resources in the U.S. are taken from estimates of 101 deep plays (out of a total of 550 conventional plays in the U.S.) from the U.S. Geological Survey's 1995 National Assessment of Oil and Gas Resources. A probabilistic method was designed to subdivide gas resources into depth slices using a median-based triangular probability distribution as a model for drilling depth to estimate the percentages of estimated gas resources below various depths. For both the World Petroleum Assessment 2000 and the 1995 National Assessment of Oil and Gas Resources, minimum, median, and maximum depths were assigned to each assessment unit and play; these depths were used in our analysis. Two-hundred seventy-four deep assessment units and plays in 124 petroleum provinces were identified for the U.S. and the world. These assessment units and plays contain a mean undiscovered conventional gas resource of 844 trillion cubic ft (Tcf) occuring at depths below 4.5 km. The deep undiscovered conventional gas resource (844 Tcf) is about 17% of the total world gas resource (4,928 Tcf) based on the provinces assessed and includes a mean estimate of 259 Tcf of U.S. gas from the U.S. 1995 National Assessment. Of the eight regions, the Former Soviet Union (Region 1) contains the largest estimated volume of undiscovered deep gas with a mean resource of343 Tcf.

Natural Resources Research↗
USGS1997

Probabilistic and statistical relationships between number of vehicles and number of visitors at a geologic site in a National Park

The National Park Service needs to establish in all of the national parks how large the parking lots should be in order to enjoy and presence our natural resources, for example, in the Delicate Arch in the Arches National Park. Probabilistic and statistical relationships were developed between the number of vehicles (N) at one time in the Wolfe Ranch parking lot and the number of visitors (X) at Delicate Arch 1.5 miles away in the Arches National Park, southeastern Utah. The value of N is determined such that 30 or more visitors are at the arch only 10% of the time.

Mathematical Geology↗