Inverting Topography for Landscape Evolution Model Process Representation: 1. Conceptualization and Sensitivity Analysis. Issue 7 (8th July 2020)

• Record Type: Journal Article
• Authors: Barnhart, Katherine R.; Tucker, Gregory E.; Doty, Sandra G.; Shobe, Charles M.; Glade, Rachel C.; Rossi, Matthew W.; Hill, Mary C.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-08
• Subjects: landscape evolution -- sensitivity analysis -- natural experiment; Geomorphology -- Periodicals; 551.3
• DOI: 10.1029/2018JF004961
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract : Despite considerable community effort, there is no general set of equations to model long‐term landscape evolution. In order to determine a suitable set of landscape evolution process laws for a site where postglacial erosion has incised valleys up to 50 m deep, we generate a set of alternative models and perform a multimodel analysis. The most basic model we consider includes stream power channel incision, uniform lithology, hillslope transport by linear diffusion, and surface‐water discharge proportional to drainage area. We systematically add one, two, or three elements of complexity to this model from one of four categories: hillslope processes, channel processes, surface hydrology, and representation of geologic materials. We apply methods of formal model analysis to the 37 alternative models. The global Method of Morris sensitivity analysis method is used to identify model input parameters that most and least strongly influence model outputs. Only a few parameters are identified as important, and this finding is consistent across two alternative model outputs: one based on a collection of topographic metrics and one that uses an objective function based on a topographic difference. Parameters that control channel erosion are consistently important, while hillslope diffusivity is important for only select model outputs. Uncertainty in initial and boundary conditions is associated with low sensitivity. Sensitivity analysis provides insight to model dynamics and is a critical step in using model analysis for mechanistic hypothesis testing in landscape evolution theory. Key Points: Multiple landscape evolution models are developed for application in a well‐constrained site undergoing locally rapid (4 m/ka) erosion Global sensitivity analysis reveals that uncertainty in initial and boundary conditions has low impact on model output metrics Global sensitivity analysis finds that channel erosion parameters dominate and that select metrics are sensitive to hillslope diffusivity