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Projections of Landscape Evolution on a 10,000 Year Timescale With Assessment and Partitioning of Uncertainty Sources
Author(s) -
Barnhart Katherine R.,
Tucker Gregory E.,
Doty Sandra G.,
Glade Rachel C.,
Shobe Charles M.,
Rossi Matthew W.,
Hill Mary C.
Publication year - 2020
Publication title -
journal of geophysical research: earth surface
Language(s) - English
Resource type - Journals
eISSN - 2169-9011
pISSN - 2169-9003
DOI - 10.1029/2020jf005795
Subject(s) - watershed , erosion , uncertainty analysis , variance (accounting) , calibration , environmental science , uncertainty quantification , term (time) , set (abstract data type) , partition (number theory) , hydrology (agriculture) , computer science , statistics , geology , mathematics , paleontology , physics , accounting , geotechnical engineering , quantum mechanics , machine learning , business , programming language , combinatorics
Long‐term erosion can threaten infrastructure and buried waste, with consequences for management of natural systems. We develop erosion projections over 10 ky for a 5 km 2 watershed in New York, USA. Because there is no single landscape evolution model appropriate for the study site, we assess uncertainty in projections associated with model structure by considering a set of alternative models, each with a slightly different governing equation. In addition to model structure uncertainty, we consider the following uncertainty sources: selection of a final model set; each model's parameter values estimated through calibration; simulation boundary conditions such as the future incision of downstream rivers and future climate; and initial conditions (e.g., site topography which may undergo near‐term anthropogenic modification). We use an analysis‐of‐variance approach to assess and partition uncertainty in projected erosion into the variance attributable to each source. Our results suggest one sixth of the watershed will experience erosion exceeding 5 m in the next 10 ky. Uncertainty in projected erosion increases with time, and the projection uncertainty attributable to each source manifests in a distinct spatial pattern. Model structure uncertainty is relatively low, which reflects our ability to constrain parameter values and reduce the model set through calibration to the recent geologic past. Beyond site‐specific findings, our work demonstrates what information prediction‐under‐uncertainty studies can provide about geomorphic systems. Our results represent the first application of a comprehensive multi‐model uncertainty analysis for long‐term erosion forecasting.