CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab

Tucker, G. E.; Hobley, Daniel E. J.; Hutton, Eric; Gasparini, N. M.; Istanbulluoglu, Erkan; Adams, J. M.; Nudurupati, Sai Siddhartha

doi:10.5194/gmd-9-823-2016

Cited by 23 publications

(54 citation statements)

References 56 publications

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“…The choice of Python also means that developers using Landlab can take advantage of that language's affinity for rapid development (Prechelt, 2000). In particular, Python's dynamic typing and interpreted rather than compiled implementation remove the developer's need to deal explicitly with memory management (van Rossum and Drake, 2001). Other advantages of this choice include high portability between platforms, open-source language, numerous existing scientific libraries, and support for selective optimization of time-critical parts of the code base using Cython and/or compiled-language extensions.…”

Section: Programming Languagementioning

confidence: 99%

Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics

et al. 2017

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Abstract. The ability to model surface processes and to couple them to both subsurface and atmospheric regimes has proven invaluable to research in the Earth and planetary sciences. However, creating a new model typically demands a very large investment of time, and modifying an existing model to address a new problem typically means the new work is constrained to its detriment by model adaptations for a different problem.Landlab is an open-source software framework explicitly designed to accelerate the development of new process models by providing (1) a set of tools and existing grid structures -including both regular and irregular gridsto make it faster and easier to develop new process components, or numerical implementations of physical processes; (2) a suite of stable, modular, and interoperable process components that can be combined to create an integrated model; and (3) a set of tools for data input, output, manipulation, and visualization. A set of example models built with these components is also provided. Landlab's structure makes it ideal not only for fully developed modelling applications but also for model prototyping and classroom use. Because of its modular nature, it can also act as a platform for model intercomparison and epistemic uncertainty and sensitivity analyses. Landlab exposes a standardized model interoperability interface, and is able to couple to third-party models and software. Landlab also offers tools to allow the creation of cellular automata, and allows native coupling of such models to more traditional continuous differential equation-based modules. We illustrate the principles of component coupling in Landlab using a model of landform evolution, a cellular ecohydrologic model, and a flood-wave routing model.

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Section: Programming Languagementioning

confidence: 99%

Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics

et al. 2017

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“…It provides a grid architecture, a suite of pre-built components for modeling surface or nearsurface processes, and utilities that handle data creation, management, and interoperability among process components (Tucker et al, 2016;Hobley et al, 2017;. The LandslideProbability component is written in Python and implemented with a model driver (written as a Jupyter Notebook) using the workflow shown in Fig.…”

Section: Model Development In Landlabmentioning

confidence: 99%

A hydroclimatological approach to predicting regional landslide probability using Landlab

et al. 2018

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Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

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“…Landlab is a plug-and-play environment in which users can easily build two-dimensional numerical models consisting of any number of well-vetted components (e.g., Tucker et al, 2016;Adams et al, 2017) along with user-specific equations and functionality. The greatest advantages of using Landlab are (1) its built-in gridding engine, which creates model grids, efficiently stores spatially distributed variables, and handles boundary conditions, and (2) the ability to easily couple different components into a single model sharing a single grid.…”

Section: Implementing Space In Landlab Landlab Modeling Toolkitmentioning

confidence: 99%

The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution

2017

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Abstract. Models of landscape evolution by river erosion are often either transport-limited (sediment is always available but may or may not be transportable) or detachmentlimited (sediment must be detached from the bed but is then always transportable). While several models incorporate elements of, or transition between, transport-limited and detachment-limited behavior, most require that either sediment or bedrock, but not both, are eroded at any given time. Modeling landscape evolution over large spatial and temporal scales requires a model that can (1) transition freely between transport-limited and detachment-limited behavior, (2) simultaneously treat sediment transport and bedrock erosion, and (3) run in 2-D over large grids and be coupled with other surface process models. We present SPACE (stream power with alluvium conservation and entrainment) 1.0, a new model for simultaneous evolution of an alluvium layer and a bedrock bed based on conservation of sediment mass both on the bed and in the water column. The model treats sediment transport and bedrock erosion simultaneously, embracing the reality that many rivers (even those commonly defined as "bedrock" rivers) flow over a partially alluviated bed. SPACE improves on previous models of bedrockalluvial rivers by explicitly calculating sediment erosion and deposition rather than relying on a flux-divergence (Exner) approach. The SPACE model is a component of the Landlab modeling toolkit, a Python-language library used to create models of Earth surface processes. Landlab allows efficient coupling between the SPACE model and components simulating basin hydrology, hillslope evolution, weathering, lithospheric flexure, and other surface processes. Here, we first derive the governing equations of the SPACE model from existing sediment transport and bedrock erosion formulations and explore the behavior of local analytical solutions for sediment flux and alluvium thickness. We derive steadystate analytical solutions for channel slope, alluvium thickness, and sediment flux, and show that SPACE matches predicted behavior in detachment-limited, transport-limited, and mixed conditions. We provide an example of landscape evolution modeling in which SPACE is coupled with hillslope diffusion, and demonstrate that SPACE provides an effective framework for simultaneously modeling 2-D sediment transport and bedrock erosion.

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CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab

Cited by 23 publications

References 56 publications

Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics

Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics

A hydroclimatological approach to predicting regional landslide probability using Landlab

The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution

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