OAR@ICRISATQuantitative Modeling of Landscape EvolutionA J A MTemmeauthorJ MSchoorlauthorLClaessensauthorAVeldkampauthorThis chapter reviews quantitative modeling of landscape evolution – which means that not just model studies but also modeling concepts are discussed. 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). The geomorphic process is a central concept in landscape evolution modeling. We discuss problems with the field-based definition of these processes from a modelling perspective. After the classification of 117 landscape evolution studies in these categories, we find that descriptive studies are most common, and predictive studies are least common. In the remainder of the chapter, we list and review the 117 studies. In procedural studies, attention has been focussed at production methods for digital landscapes, spatial resolution and the role of sinks and depressions. Descriptive studies focussed 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 sometimes validated (postdictive studies of soil redistribution over centennial to millennial timescales). Finally, we look ahead to the future of landscape evolution modeling, arguing for a larger role for complexity research, predictive studies and uncertainty analysis, process definition and feedbacks to and from other fields (including ecology).Soil ScienceAgriculture-Farming, Production, Technology, Economics2013ElsevierBook Section

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