Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution
Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution
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DOI：
10.5194/gmd-12-1267-2019
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发表时间：
2018-10
影响因子：
5.1
通讯作者：
K. Barnhart;R. Glade;C. Shobe;G. Tucker
中科院分区：
文献类型：
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作者：
K. Barnhart;R. Glade;C. Shobe;G. Tucker
抽象的。景观演变模型提供了对特定领域的地貌历史的洞察，创建可测试的地貌发展预测，展示当前地貌过程理论的后果，并通过假设场景激发想象力。虽然在过去的40年里，地球表面过程的质量再分配带来了许多替代配方的扩散，相对较少的研究系统地比较和测试这些替代方程。我们提供了一个新的Python包terrainbento 1.0，它支持地球表面过程模型的多模型比较、灵敏度分析和校准。Terrainbento提供了一套28个模型程序，实现了与四个过程要素相关的替代传输定律：山坡过程，地表水水文学，流水侵蚀和材料特性。28个模型程序是与11个二元选择相关的2048个可能的数值模型的系统子集。每个二元选择都与这四个元素之一相关-例如，使用线性或非线性山坡扩散。Terrainbento是一个可扩展的框架：处理所有数值模型通用元素（如输入/输出和边界条件）的基类使创建新的数值模型成为可能，而无需重新发明这些通用方法。Terrainbento建立在Landlab框架之上，因此新的Landlab组件直接支持创建新的terrainbento模型程序。Terrainbento有完整的文档记录，具有100%的单元测试覆盖率，包括与流程模型的分析解决方案进行数值比较，以及持续集成测试。我们为未来的用户和开发人员提供入门级的Terrainbento笔记本和用于创建新的Terrainbento模型程序的模板。本文描述了terrainbento的软件包结构、处理原理和软件实现。最后，我们用一个基准例子说明了terrainbento的实用性，突出了五种不同数值模型之间稳态地形的差异。
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.