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  1. Abstract

    Features of landscape morphology—including slope, curvature, and drainage dissection—are important controls on runoff generation in upland landscapes. Over long timescales, runoff plays an essential role in shaping these same features through surface erosion. This feedback between erosion and runoff generation suggests that modeling long‐term landscape evolution together with dynamic runoff generation could provide insight into hydrological function. Here we examine the emergence of variable source area runoff generation in a new coupled hydro‐geomorphic model that accounts for water balance partitioning between surface flow, subsurface flow, and evapotranspiration as landscapes evolve over millions of years. We derive a minimal set of dimensionless numbers that provide insight into how hydrologic and geomorphic parameters together affect landscapes. Across the parameter space we investigated, model results collapsed to a single inverse relationship between the dimensionless relief and the ratio of catchment quickflow to discharge. Furthermore, we found an inverse relationship between the Hillslope number, which describes topographic relief relative to aquifer thickness, and the proportion of the landscape that was variably saturated. While the model generally produces fluvial topography visually similar to simpler landscape evolution models, certain parameter combinations produce wide valley bottom wetlands and non‐dendritic, trellis‐like drainage networks, which may reflect real conditions in some landscapes where aquifer gradients become decoupled from topography. With these results, we demonstrate the power of hydro‐geomorphic models for generating new insights into hydrological processes, and also suggest that subsurface hydrology may be integral for modeling aspects of long‐term landscape evolution.

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    Free, publicly-accessible full text available June 1, 2025
  2. Abstract. Debris flows regularly traverse bedrock channels that dissect steep landscapes, but our understanding of bedrock erosion by debris flows and their impact on steepland morphology is still rudimentary. Quantitative models of steep bedrock channel networks are based on geomorphic transport laws designed to represent erosion by water-dominated flows. To quantify the impact of debris flow erosion on steep channel network form, it is first necessary to develop methods to estimate spatial variations in bulk debris flow properties (e.g., flow depth, velocity) throughout the channel network that can be integrated into landscape evolution models. Here, we propose and evaluate two methods to estimate spatial variations in bulk debris flow properties along the length of a channel profile. We incorporate both methods into a model designed to simulate the evolution of longitudinal channel profiles that evolve in response to debris flow and fluvial processes. To explore this model framework, we propose a general family of debris flow erosion laws where erosion rate is a function of debris flow depth and channel slope. Model results indicate that erosion by debris flows can explain the occurrence of a scaling break in the slope–area curve at low-drainage areas and that upper-network channel morphology may be useful for inferring catchment-averaged erosion rates in quasi-steady landscapes. Validating specific forms of a debris flow incision law, however, would require more detailed model–data comparisons in specific landscapes where input parameters and channel morphometry can be better constrained. Results improve our ability to interpret topographic signals within steep channel networks and identify observational targets critical for constraining a debris flow incision law.

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  3. Key Points Modeled ecosystem response to climate follows the “geo‐ecological law of distribution,” highlights the importance of ecohdyrologic refugia Woody Plant Encroachment is predicted as a three‐phase phenomenon: early establishment, rapid expansion, and woody plant equilibrium Regime shifts from grassland to shrubland are marked by vegetation cover thresholds 
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  4. Abstract

    Steep landscapes evolve largely by debris flows, in addition to fluvial and hillslope processes. Abundant field observations document that debris flows incise valley bottoms and transport substantial sediment volumes, yet their contributions to steepland morphology remain uncertain. This has, in turn, limited the development of debris‐flow incision rate formulations that produce morphology consistent with natural landscapes. In many landscapes, including the San Gabriel Mountains (SGM), California, steady‐state fluvial channel longitudinal profiles are concave‐up and exhibit a power‐law relationship between channel slope and drainage area. At low drainage areas, however, valley slopes become nearly constant. These topographic forms result in a characteristically curved slope‐area signature in log‐log space. Here, we use a one‐dimensional landform evolution model that incorporates debris‐flow erosion to reproduce the relationship between this curved slope‐area signature and erosion rate in the SGM. Topographic analysis indicates that the drainage area at which steepland valleys transition to fluvial channels correlates with measured erosion rates in the SGM, and our model results reproduce these relationships. Further, the model only produces realistic valley profiles when parameters that dictate the relationship between debris‐flow erosion, valley‐bottom slope, and debris‐flow depth are within a narrow range. This result helps place constraints on the mathematical form of a debris‐flow incision law. Finally, modeled fluvial incision outpaces debris‐flow erosion at drainage areas less than those at which valleys morphologically transition from near‐invariant slopes to concave profiles. This result emphasizes the critical role of debris‐flow incision for setting steepland form, even as fluvial incision becomes the dominant incisional process.

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  5. Abstract. Computational modeling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature – models as both infrastructure and hypotheses – means that modeling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language-bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed. 
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  6. null (Ed.)
  7. 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.

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  8. Abstract. 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. 
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