Geomorphic process modeling allows us to evaluate different methods forestimating moraine ages from cosmogenic exposure dates, and may provide ameans to identify the processes responsible for the excess scatter amongexposure dates on individual moraines. Cosmogenic exposure dating is anelegant method for estimating the ages of moraines, but individual exposuredates are sometimes biased by geomorphic processes. Because exposure datesmay be either "too young" or "too old," there are a variety of methodsfor estimating the ages of moraines from exposure dates. In this paper, wepresent Monte Carlo-based models of moraine degradation and inheritance ofcosmogenic nuclides, and we use the models to examine the effectiveness ofthese methods. The models estimate the statistical distributions of exposuredates that we would expect to obtain from single moraines, given reasonablegeomorphic assumptions. The model of moraine degradation is based on priorexamples, but the inheritance model is novel. The statistical distributionsof exposure dates from the moraine degradation model are skewed toward youngvalues; in contrast, the statistical distributions of exposure dates fromthe inheritance model are skewed toward old values. Sensitivity analysisshows that this difference is robust for reasonable parameter choices. Thus,the skewness can help indicate whether a particular data set has problemswith inheritance or moraine degradation. Given representative distributionsfrom these two models, we can determine which methods of estimating moraineages are most successful in recovering the correct age for test cases wherethis value is known. The mean is a poor estimator of moraine age for datasets drawn from skewed parent distributions, and excluding outliers beforecalculating the mean does not improve this mismatch. The extreme estimators(youngest date and oldest date) perform well under specific circumstances,but fail in other cases. We suggest a simple estimator that uses theskewnesses of individual data sets to determine whether the youngest date,mean, or oldest date will provide the best estimate of moraine age. Althoughthis method is perhaps the most globally robust of the estimators we tested,it sometimes fails spectacularly. The failure of simple methods to provideaccurate estimates of moraine age points toward a need for moresophisticated statistical treatments.
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