Quantitative Modeling of Landscape Evolution

This chapter reviews quantitative modeling of landscape evolution. Quantitative modeling is contrasted with conceptual or physical modeling, and four categories of model studies are presented. Procedural studies focus on model experimentation. Descriptive studies use models to learn about landscapes in general. Postdictive and predictive try to correctly simulate the evolution of real landscapes, respectively in the past (with calibration) or in the future (with calibrated models). After classification of 322 landscape evolution studies in these categories, we find that descriptive studies are most common, and predictive studies are least common. Procedural studies have focused on production methods for digital landscapes, spatial resolution effects, the role of sinks and depressions and calculation schemes for flow routing. Descriptive studies focused mainly on surface-tectonic interactions, sensitivity to external forcing, and the definition of crucial field observations from model results. Postdictive and predictive studies operate mainly in time-forward mode and are increasingly validated using independent data. Overall, landscape evolution modeling has progressed to the extent that non-experts are able to easily use modern models, and are commonly used in inversion schemes to obtain the most likely (set of) inputs to produce known topographies. This development will likely continue, with more attention for interactions with ecology and soils over short (ca, ma) timescales, and with climate over long (Ma) timescales.