More Like this

1. Signatures of Varying Climate on Geomorphic and Topologic Characteristics of Channel Networks

   https://doi.org/10.1029/2024WR038760  

   H_Bavojdan, Aysan; Ranjbar, Sevil; Wang, Dingbao; Singh, Arvind (March 2025, Water Resources Research)

   Abstract Channel networks are important landscape features that transport water, sediment, and nutrients. Their emergence and evolution are controlled by the competition between hillslope and fluvial processes on landscapes. Investigating the geomorphic and topologic properties of these networks is crucial for quantifying the roles of processes in creating distinct patterns of channel networks and developing models to predict the network dynamics under changing environment. Here, we study the response of landscapes to changing climatic forcing via numerical‐modeling and the topographic analysis of natural landscapes. We use a physically‐based numerical landscape evolution model to investigate the channel network structure for varying hillslope and fluvial processes represented by different magnitudes of soil transport () and fluvial incision () coefficients. We show that landscapes with the same Péclet number (defined as the ratio of the timescales of advective (fluvial) to diffusive (hillslope) processes) and thus the same characteristic length scale may exhibit different geomorphic and topologic characteristics. Specifically, increasingDandK(mimicking humid conditions) or decreasingDandK(mimicking dry conditions), while keeping the same Péclet number, results in distinct branching structures. These changes lead to an exponential decrease in relief under humid conditions and an increase under dry conditions. For smaller and combinations, higher number of branching channels is observed, whereas for larger and combinations, higher number of side‐branching channels is obtained. These results align with topographic analysis of natural landscapes, suggesting that varying climatic conditions imprint distinct signatures on the branching structure of channel networks. 

   more » « less
2. Groundwater Affects the Geomorphic and Hydrologic Properties of Coevolved Landscapes

   https://doi.org/10.1029/2021JF006239  

   Litwin, David G.; Tucker, Gregory E.; Barnhart, Katherine R.; Harman, Ciaran J. (January 2022, Journal of Geophysical Research: Earth Surface)

   Abstract The hydrologic dynamics and geomorphic evolution of watersheds are intimately coupled—runoff generation and water storage are controlled by topography and properties of the surface and subsurface, while also affecting the evolution of those properties over geologic time. However, the large disparity between their timescales has made it difficult to examine interdependent controls on emergent hydrogeomorphic properties, such as hillslope length, drainage density, and extent of surface saturation. In this study, we develop a new model coupling hydrology and landscape evolution to explore how runoff generation affects long‐term catchment evolution, and analyze numerical results using a nondimensional scaling framework. We focus on hydrologic processes dominating in humid climates where storm runoff primarily arises from shallow subsurface flow and from precipitation on saturated areas. The model solves hydraulic groundwater equations to predict the water‐table elevation given prescribed, constant groundwater recharge. Water in excess of the subsurface capacity for transport becomes overland flow, which generates shear stress on the surface and may detach and transport sediment. This affects the landscape form that in turn affects runoff generation. We show that (a) four dimensionless parameters describe the possible steady state landscapes that coevolve under steady recharge; (b) hillslope length increases with increasing transmissivity relative to the recharge rate; (c) three topographic metrics—steepness index, Laplacian curvature, and topographic index—together provide a basis for interpreting landscapes that have coevolved with runoff generated via shallow subsurface flow. Finally we discuss the possibilities and limitations for quantitative comparisons between the model results and real landscapes. 

   more » « less
3. Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution

   https://doi.org/10.5194/gmd-12-1267-2019  

   Barnhart, Katherine R.; Glade, Rachel C.; Shobe, Charles M.; Tucker, Gregory E. (January 2019, Geoscientific Model Development)

   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. 

   more » « less
4. Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data

   https://doi.org/10.1029/2023EA002845  

   Maxwell, Aaron E.; Odom, William E.; Shobe, Charles M.; Doctor, Daniel H.; Bester, Michelle S.; Ore, Tobi (May 2023, Earth and Space Science)

   Abstract Many studies of Earth surface processes and landscape evolution rely on having accurate and extensive data sets of surficial geologic units and landforms. Automated extraction of geomorphic features using deep learning provides an objective way to consistently map landforms over large spatial extents. However, there is no consensus on the optimal input feature space for such analyses. We explore the impact of input feature space for extracting geomorphic features from land surface parameters (LSPs) derived from digital terrain models (DTMs) using convolutional neural network (CNN)‐based semantic segmentation deep learning. We compare four input feature space configurations: (a) a three‐layer composite consisting of a topographic position index (TPI) calculated using a 50 m radius circular window, square root of topographic slope, and TPI calculated using an annulus with a 2 m inner radius and 10 m outer radius, (b) a single illuminating position hillshade, (c) a multidirectional hillshade, and (d) a slopeshade. We test each feature space input using three deep learning algorithms and four use cases: two with natural features and two with anthropogenic features. The three‐layer composite generally provided lower overall losses for the training samples, a higher F1‐score for the withheld validation data, and better performance for generalizing to withheld testing data from a new geographic extent. Results suggest that CNN‐based deep learning for mapping geomorphic features or landforms from LSPs is sensitive to input feature space. Given the large number of LSPs that can be derived from DTM data and the variety of geomorphic mapping tasks that can be undertaken using CNN‐based methods, we argue that additional research focused on feature space considerations is needed and suggest future research directions. We also suggest that the three‐layer composite implemented here can offer better performance in comparison to using hillshades or other common terrain visualization surfaces and is, thus, worth considering for different mapping and feature extraction tasks. 

   more » « less
5. Land-surface parameters for spatial predictive mapping and modeling

   https://doi.org/10.1016/j.earscirev.2022.103944  

   Maxwell, Aaron E.; Shobe, Charles M. (January 2022, Earthscience reviews)

   Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks. 

   more » « less