Runout model evaluation based on back-calculation of building damage

We evaluated the ability of three debris-flow runout models (RAMMS, FLO2D and D-Claw) to predict the number of damaged buildings in simulations of the 9 January 2019 Montecito, California, debrisflow event. Observations of building damage after the event were combined with OpenStreetMap building footprints to construct a database of all potentially impacted buildings. At the estimated event volume, all models overpredict the number of damaged buildings by a factor of 1.5–3

Quantifying the stratigraphic completeness of delta shoreline trajectories

Understanding the incomplete nature of the stratigraphic record is fundamental for interpreting stratigraphic sequences. Methods for quantifying stratigraphic completeness for one-dimensional stratigraphic columns, defined as the proportion of time intervals of some length that contain stratigraphy, are commonplace; however, quantitative assessments of completeness in higher dimensions are lacking. Here we present a metric for defining stratigraphic completeness of two-dimensional shoreline trajectories using topset-foreset rollover positions in dip-parallel sections and describe the preservation potential of a shoreline trajectory derived from the geometry of the delta surface profile and the kinematics of the geomorphic shoreline trajectory. Two end-member forward models are required to fully constrain the preservation potential of the shoreline dependent on whether or not a topset is eroded during base level fall. A laboratory fan-delta was constructed under nonsteady boundary conditions, and one-dimensional stratigraphic column and two-dimensional shoreline completeness curves were calculated. Results are consistent with the hypothesis derived from conservation of sediment mass that completeness over all timescales should increase given increasing dimensions of analysis. Stratigraphic trajectories and completeness curves determined from forward models using experimental geomorphic trajectories compare well to values from transects when subsampled to the equivalent stratigraphic resolution as observed in the actual preserved sequence. The concept of stratigraphic completeness applied to two-dimensional trajectory analysis and the end-member forward models presented here provide novel tools for a conceptual understanding of the nature of stratigraphic preservation at basin scales

Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution

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 &ndash; 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.</p

Forecasting the inundation of postfire debris flows

In the semi-arid regions of the western United States, postfire debris flows are typically runoff generated. The U.S. Geological Survey has been studying the mechanisms of postfire debris-flow initiation for multiple decades to generate operational models for forecasting the timing, location, and magnitude of postfire debris flows. Here we discuss challenges and progress for extending operational capabilities to include modeling postfire debris-flow inundation extent. Analysis of volume and impacted area scaling relationships indicated that postfire debris flows do not conform to assumptions of geometric self-similarity. We documented sensitivity of impacted areas to rainfall intensity using a candidate methodology for generating inundation hazard assessments. Our results emphasize the importance of direct measurements of debris-flow volume, inundated area, and high temporal resolution rainfall intensity

Short communication: Landlab v2.0: a software package for Earth surface dynamics

umerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab

On transient semi‐arid ecosystem dynamics using Landlab: vegetation shifts, topographic refugia, and response to climate

Projecting how arid and semi‐arid ecosystems respond to global change requires the integration of a wide array of analytical and numerical models to address different aspects of complex ecosystems. We used the Landlab earth surface modeling toolkit (Hobley et al., 2017, https://doi.org/10.5194/esurf-5-21-2017) to couple several ecohydrologic and vegetation dynamics processes to investigate the controls of exogenous drivers (climate, topography, fires, and grazing) and endogenous grass‐fire feedback mechanisms. Aspect‐controlled ecosystems and historical woody plant encroachment (WPE) narratives in central New Mexico, USA are used to construct simulations. Modeled ecosystem response to climatic wetness (i.e., higher precipitation, lower potential evapotranspiration) on topography follows the Boyko's “geo‐ecological law of distribution.” Shrubs occupy cooler pole‐facing slopes in the dry end of their ecoclimatic range (Mean Annual Precipitation, MAP ≤ 200 mm), and shift toward warmer equator‐facing slopes as regional moisture increases (MAP > 250 mm). Trees begin to occupy pole‐facing slopes when MAP > 200 mm, and gradually move to valleys. Pole‐facing slopes increase species diversity at the landscape scale by hosting relict populations during dry periods. WPE observed in the region since the middle 1800s is predicted as a three‐phase phenomenon. Phase II, rapid expansion, requires the removal of the positive grass‐fire feedback by livestock grazing or fire suppression. Regime shifts from grassland to shrubland are marked by critical thresholds that involve grass cover remaining below 40%, shrub cover increasing to 10%–20% range, and the grass connectivity, Cg, remaining below 0.15. A critical transition to shrubland is predicted when grazing pressure is not removed before shrub cover attains 60%

Short communication: Landlab v2.0: a software package for Earth surface dynamics

Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon-Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components - modular implementations of single physical processes - and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components - for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.</p