Abstract
Abstract. Models of landscape evolution provide insight into the geomorphic history of specific field areas, create testable predictions of landform development, demonstrate the consequences of current geomorphic process theory, and spark imagination through hypothetical scenarios. While the last 4 decades have brought the proliferation of many alternative formulations for the redistribution of mass by Earth surface processes, relatively few studies have systematically compared and tested these alternative equations. We present a new Python package, terrainbento 1.0, that enables multi-model comparison, sensitivity analysis, and calibration of Earth surface process models. Terrainbento provides a set of 28 model programs that implement alternative transport laws related to four process elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs are a systematic subset of the 2048 possible numerical models associated with 11 binary choices. Each binary choice is related to one of these four elements – for example, the use of linear or nonlinear hillslope diffusion. Terrainbento is an extensible framework: base classes that treat the elements common to all numerical models (such as input/output and boundary conditions) make it possible to create a new numerical model without reinventing these common methods. Terrainbento is built on top of the Landlab framework such that new Landlab components directly support the creation of new terrainbento model programs. Terrainbento is fully documented, has 100 % unit test coverage including numerical comparison with analytical solutions for process models, and continuous integration testing. We support future users and developers with introductory Jupyter notebooks and a template for creating new terrainbento model programs. In this paper, we describe the package structure, process theory, and software implementation of terrainbento. Finally, we illustrate the utility of terrainbento with a benchmark example highlighting the differences in steady-state topography between five different numerical models.
Highlights
Computational models of long-term drainage basin and landscape evolution have a wide spectrum of applications in geomorphology, ranging from addressing fundamental questions about how climatic and tectonic processes shape topography, to performing engineering assessments of landform stability and potential for hazardous-waste containment
We review the various options that terrainbento offers for alternative treatment of hillslope processes, surface-water hydrology, channel incision, materials, and boundary conditions
When the dynamic soil option is used in combination with a sediment-tracking entrainment–deposition erosion law, the Stream Power with Alluvium Conservation and Entrainment (SPACE) numerical model described above is used in place of the simpler entrainment–deposition law
Summary
Computational models of long-term drainage basin and landscape evolution have a wide spectrum of applications in geomorphology, ranging from addressing fundamental questions about how climatic and tectonic processes shape topography, to performing engineering assessments of landform stability and potential for hazardous-waste containment (see, e.g., reviews by Coulthard, 2001; Pazzaglia, 2003; Martin and Church, 2004; Willgoose, 2005; Codilean et al, 2006; Bishop, 2007; Tucker and Hancock, 2010; Willgoose and Hancock, 2011; Pelletier, 2013; Temme et al, 2013; Chen et al, 2014; Valters, 2016). The basic principles of drainage basin evolution are reasonably well understood – such as the fundamental concept that erosion is driven by gravitational and water-runoff processes, the latter of which depend strongly on surface gradient and water flow – uncertainty remains concerning the appropriate forms of the governing transport laws for any particular set of materials and environmental conditions (Dietrich et al, 2003) This situation creates a need for comparative testing in order to gauge the overall performance of various mathematical formulations, to identify knowledge gaps in areas where numerical. Terrainbento takes advantage of Python class inheritance such that all common features of terrainbento model programs (such as input/output, and the handling of boundary conditions) are provided in a generic “ErosionModel” base class from which specific programs are derived This ErosionModel class enables modelers to craft and apply their own implementations without needing to reinvent the overarching software framework or the necessary utility functions. This paper presents and describes terrainbento version 1.0, including its basic structure, mathematical underpinnings, software implementation, and the 28 constituent model programs
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