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| Titel |
Modeling the statistical distributions of cosmogenic exposure dates from moraines |
| VerfasserIn |
P. J. Applegate, N. M. Urban, B. J. C. Laabs, K. Keller, R. B. Alley |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 3, no. 1 ; Nr. 3, no. 1 (2010-04-12), S.293-307 |
| Datensatznummer |
250000807
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| Publikation (Nr.) |
 copernicus.org/gmd-3-293-2010.pdf |
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| Zusammenfassung |
| Geomorphic process modeling allows us to evaluate different methods for
estimating moraine ages from cosmogenic exposure dates, and may provide a
means to identify the processes responsible for the excess scatter among
exposure dates on individual moraines. Cosmogenic exposure dating is an
elegant method for estimating the ages of moraines, but individual exposure
dates are sometimes biased by geomorphic processes. Because exposure dates
may be either "too young" or "too old," there are a variety of methods
for estimating the ages of moraines from exposure dates. In this paper, we
present Monte Carlo-based models of moraine degradation and inheritance of
cosmogenic nuclides, and we use the models to examine the effectiveness of
these methods. The models estimate the statistical distributions of exposure
dates that we would expect to obtain from single moraines, given reasonable
geomorphic assumptions. The model of moraine degradation is based on prior
examples, but the inheritance model is novel. The statistical distributions
of exposure dates from the moraine degradation model are skewed toward young
values; in contrast, the statistical distributions of exposure dates from
the inheritance model are skewed toward old values. Sensitivity analysis
shows that this difference is robust for reasonable parameter choices. Thus,
the skewness can help indicate whether a particular data set has problems
with inheritance or moraine degradation. Given representative distributions
from these two models, we can determine which methods of estimating moraine
ages are most successful in recovering the correct age for test cases where
this value is known. The mean is a poor estimator of moraine age for data
sets drawn from skewed parent distributions, and excluding outliers before
calculating 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 the
skewnesses of individual data sets to determine whether the youngest date,
mean, or oldest date will provide the best estimate of moraine age. Although
this method is perhaps the most globally robust of the estimators we tested,
it sometimes fails spectacularly. The failure of simple methods to provide
accurate estimates of moraine age points toward a need for more
sophisticated statistical treatments. |
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