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Improved methods of radiocarbon analysis have enabled us to date more precisely the earthquake ruptures of the San Andreas fault that are recorded in the sediments at Pallett Creek. Previous dates of these events had 95% confidence errors of 50-100 calendar years. New error limits are less than 23 calendar years for all but two of the dated events. This greater precision is due to larger sample size, longer counting time, lower background noise levels, more precise conversion of radiocarbon ages to calendric dates, and better stratigraphic constraints and statistical techniques. The new date ranges, with one exception, fall within the broader ranges estimated previously, but our estimate of the average interval between the latest 10 episodes of faulting is now about 132 years. Variability about the mean interval is much greater than was suspected previously.

We present a probability-based model for estimating fire risk. Risk is defined using three probabilities: the probability of fire occurrence; the conditional probability of a large fire given ignition; and the unconditional probability of a large fire. The model is based on grouped data at the 1 km2-day cell level. We fit a spatially and temporally explicit non-parametric logistic regression to the grouped data. The probability framework is particularly useful for assessing the utility of explanatory variables, such as fire weather and danger indices for predicting fire risk. The model may also be used to produce maps of predicted probabilities and to estimate the total number of expected fires, or large fires, in a given region and time period. As an example we use historic data from the State of Oregon to study the significance and the forms of relationships between some of the commonly used weather and danger variables on the probabilities of fire. We also produce maps of predicted probabilities for the State of Oregon. Graphs of monthly total numbers of fires are also produced for a small region in Oregon, as an example, and expected numbers are compared to actual numbers of fires for the period 1989–1996. The fits appear to be reasonable; however, the standard errors are large indicating the need for additional weather or topographic variables.