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Gravel‐bed rivers that incise into bedrock are common worldwide. These systems have many similarities with other alluvial channels: they transport large amounts of sediment and adjust their forms in response to discharge and sediment supply. At the same time, the occurrence of bedrock incision implies behaviour that falls on a spectrum between fully detachment‐limited ‘bedrock channels’ and fully transport‐limited ‘alluvial channels’. Here, we present a mathematical model of river profile evolution that integrates bedrock erosion, gravel transport and the formation of channels whose hydraulic geometry is consistent with that of near‐threshold alluvial channels. We combine theory for five interrelated processes: bedload sediment transport in equilibrium gravel‐bed channels, channel width adjustment to flow and sediment characteristics, abrasion of bedrock by mobile sediment, plucking of bedrock and progressive loss of gravel‐sized sediment due to grain attrition. This model contributes to a growing class of models that seek to capture the dynamics of both bedrock incision and alluvial sediment transport. We demonstrate the model's ability to reproduce expected fluvial features such as inverse power law scaling between slope and area, and width and depth consistent with near‐threshold channel theory, and we discuss the role of sediment characteristics in influencing the mode of channel behaviour, erosional mechanism, channel steepness and profile concavity. 

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies. 

Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility. 

The Chaos Canyon landslide, which collapsed on the afternoon of 28 June 2022 in Rocky Mountain National Park, presents an opportunity to evaluate instabilities within alpine regions faced with a warming and dynamic climate. Video documentation of the landslide was captured by several eyewitnesses and motivated a rapid field campaign. Initial estimates put the failure area at 66 630 m2, with an average elevation of 3555 m above sea level. We undertook an investigation of previous movement of this landslide, measured the volume of material involved, evaluated the potential presence of interstitial ice and snow within the failed deposit, and examined potential climatological impacts on the collapse of the slope. Satellite radar and optical measurements were used to calculate deformation of the landslide in the 5 years leading up to collapse. From 2017 to 2019, the landslide moved ∼5 m yr−1, accelerating to 17 m yr−1 in 2019. Movement took place through both internal deformation and basal sliding. Climate analysis reveals that the collapse took place during peak snowmelt, and 2022 followed 10 years of higher than average positive degree day sums. We also made use of slope stability modeling to test what factors controlled the stability of the area. Models indicate that even a small increase in the water table reduces the factor of safety to <1, leading to failure. We posit that a combination of permafrost thaw from increasing average temperatures, progressive weakening of the basal shear zone from several years of movement, and an increase in pore-fluid pressure from snowmelt led to the 28 June collapse. Material volumes were estimated using structure from motion (SfM) models incorporating photographs from two field expeditions on 8 July 2022 – 10 d after the slide. Detailed mapping and SfM models indicate that ∼1 258 000 ± 150 000 m3 of material was deposited at the slide toe and ∼1 340 000 ± 133 000 m3 of material was evacuated from the source area. The Chaos Canyon landslide may be representative of future dynamic alpine topography, wherein slope failures become more common in a warming climate. 

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. 

Abstract. Landslides are the main source of sediment in most mountain ranges. Rivers then act as conveyor belts, evacuating landslide-derived sediment. Sediment dynamics are known to influence landscape evolution through interactions among landslide sediment delivery, fluvial transport and river incision into bedrock. Sediment delivery and its interaction with river incision therefore control the pace of landscape evolution and mediate relationships among tectonics, climate and erosion. Numerical landscape evolution models (LEMs) are well suited to study the interactions among these surface processes. They enable evaluation of a range of hypotheses at varying temporal and spatial scales. While many models have been used to study the dynamic interplay between tectonics, erosion and climate, the role of interactions between landslide-derived sediment and river incision has received much less attention. Here, we present HyLands, a hybrid landscape evolution model integrated within the TopoToolbox Landscape Evolution Model (TTLEM) framework. The hybrid nature of the model lies in its capacity to simulate both erosion and deposition at any place in the landscape due to fluvial bedrock incision, sediment transport, and rapid, stochastic mass wasting through landsliding. Fluvial sediment transport and bedrock incision are calculated using the recently developed Stream Power with Alluvium Conservation and Entrainment (SPACE) model. Therefore, rivers can dynamically transition from detachment-limited to transport-limited and from bedrock to bedrock–alluvial to fully alluviated states. Erosion and sediment production by landsliding are calculated using a Mohr–Coulomb stability analysis, while landslide-derived sediment is routed and deposited using a multiple-flow-direction, nonlinear deposition method. We describe and evaluate the HyLands 1.0 model using analytical solutions and observations. We first illustrate the functionality of HyLands to capture river dynamics ranging from detachment-limited to transport-limited conditions. Second, we apply the model to a portion of the Namche Barwa massif in eastern Tibet and compare simulated and observed landslide magnitude–frequency and area–volume scaling relationships. Finally, we illustrate the relevance of explicitly simulating landsliding and sediment dynamics over longer timescales for landscape evolution in general and river dynamics in particular. With HyLands we provide a new tool to understand both the long- and short-term coupling between stochastic hillslope processes, river incision and source-to-sink sediment dynamics. 

Abstract Hillslope topographic change in response to climate and climate change is a key aspect of landscape evolution. The impact of short‐duration rainstorms on hillslope evolution in arid regions is persistently questioned but often not directly examined in landscape evolution studies, which are commonly based on mean climate proxies. This study focuses on hillslope surface processes responding to rainstorms in the driest regions of Earth. We present a numerical model for arid, rocky hillslopes with lithology of a softer rock layer capped by a cliff‐forming resistant layer. By representing the combined action of bedrock and clast weathering, cliff‐debris ravel, and runoff‐driven erosion, the model can reproduce commonly observed cliff‐profile morphology. Numerical experiments with a fixed base level were used to test hillslope response to cliff‐debris grain size, rainstorm intensities, and alternation between rainstorm patterns. The persistence of vertical cliffs and the pattern of sediment sorting depend on rainstorm intensities and the size of cliff debris. Numerical experiments confirm that these two variables could have driven the landscape in the Negev Desert (Israel) toward an observed spatial contrast in topographic form over the past 10⁵–10⁶ years. For a given total storm rain depth, short‐duration higher‐intensity rainstorms are more erosive, resulting in greater cliff retreat distances relative to longer, low‐intensity storms. Temporal alternation between rainstorm regimes produces hillslope profiles similar to those previously attributed to Quaternary oscillations in the mean climate. We suggest that arid hillslopes may undergo considerable geomorphic transitions solely by alternating intra‐storm patterns regardless of rainfall amounts. 

Abstract Bedrock landslides shape topography and mobilize large volumes of sediment. Yet, interactions between landslide‐produced sediment and fluvial systems that together govern large‐scale landscape evolution are not well understood. To explain morphological patterns observed in steep, landslide‐prone terrain, we explicitly model stochastic landsliding and associated sediment dynamics. The model accounts for several common landscape features such as slope frequency distributions, which include values in excess of regional stability limits, quasi‐planar hillslopes decorated with straight, closely spaced channel‐like features, and accumulation of sediment in valley networks rather than on hillslopes. Stochastic landsliding strongly affects the magnitude and timing of sediment supply to the fluvial system. We show that intermittent sediment supply is ultimately reflected in topography. At dynamic equilibrium, landslide‐derived sediment pulses generate persistent landscape dynamism through the formation and breaching of landslide dams and epigenetic gorges as landslides force shifts in channel positions. Our work highlights the importance of interactions between landslides and sediment dynamics that ultimately control landscape‐scale response to environmental change.