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Passive margin stratigraphy contains time‐integrated records of landscapes that have long since vanished. Quantitatively reading the stratigraphic record using coupled landscape evolution and stratigraphic forward models (SFMs) is a promising approach to extracting information about landscape history. However, there is no consensus about the optimal form of simple SFMs because there has been a lack of direct tests against observed stratigraphy in well‐constrained test cases. Specifically, the extent to which SFM behaviour over geologic space and timescales should be governed by local (downslope sediment flux depends only on local slope) versus nonlocal (sediment flux depends on factors other than local slope, such as the history of slopes experienced along a transport pathway) processes is currently unclear. Here, we develop a nonlocal, nonlinear SFM that incorporates slope bypass and long‐distance sediment transport, both of which have been previously identified as important model components but not thoroughly tested. Our model collapses to the local, linear model under certain parameterizations such that best‐fit parameter values can indicate optimal model structure. Comparing 2‐D implementations of both models against seven detailed seismic sections from the Southeast Atlantic Margin, we invert the stratigraphic data for best‐fit model parameter values and demonstrate that best‐fit parameterizations are not compatible with the local, linear diffusion model. Fitting observed stratigraphy requires parameter values consistent with important contributions from slope bypass and long‐distance transport processes. The nonlocal, nonlinear model yields improved fits to the data regardless of whether the model is compared against only the modern bathymetric surface or the full set of seismic reflectors identified in the data. Results suggest that processes of sediment bypass and long‐distance transport are required to model realistic passive margin stratigraphy and are therefore important to consider when inverting the stratigraphic record to infer past perturbations to source regions. [ABSTRACT FROM AUTHOR]

Erosion following human disturbance threatens ecosystem health and inhibits effective land use. Mountaintop removal/valley fill (MTR/VF) mined landscapes of the Appalachian Coalfields region, USA, provide a unique opportunity to quantify the geomorphic trajectory of disturbed lands. Here we assess how MTR/VF-induced changes to topography and vegetation influence spatiotemporal erosion patterns in five mined watersheds. We use landscape evolution models starting from pre- and post-MTR/VF topographic data to isolate the influence of mining-induced topographic change. We then constrain ranges of erodibility from incision depths of gully features on mine margins, and use those estimates to model the influence of vegetation recovery trends on erosion. Topographic alterations alone reduce total sediment export from mined catchments. Model runs that incorporate the disturbance and recovery of vegetation in mined watersheds show that complete vegetation recovery keeps millennial sediment export from mined catchments within the range of unmined catchments. If vegetation recovery is anything less than complete, vegetation disturbance drives greater total sediment export from mined catchments than unmined catchments. Full vegetation recovery causes sediment fluxes to decline over millennia beyond the recovery period, while watersheds without full recovery experience fluxes that increase over the same time period. Spatiotemporal erosion trends depend on 1) the extent of vegetation recovery and 2) the extent to which MTR/VF creates slope–area disequilibrium. Valley fills and mine scarps experience erosion rates several times higher than those found in the unmined landscapes. Rapid erosion of mined areas drives deposition in colluvial hollows, headwater stream valleys, and below scarps. Our experiments suggest that reclamation focused on maximizing vegetation recovery and reducing hotspots of slope–area disequilibrium would reduce MTR's influence on Appalachian watersheds both during and long after the vegetation recovery period. Insights from MTR/VF-influenced landscapes can inform mined land management as the renewable energy transition drives increased surface mining. [Display omitted] • We model 10,000 years of erosion beginning from both pre- and post-mining topography. • Mining-driven topographic changes alone reduce total erosion due to ridge flattening. • Incomplete vegetation recovery increases erosion in mined over unmined basins. • Erosion is focused in valley fills, deposition in low-order valleys and below scarps. • Vegetation recovery sets decadal sediment pulses and millennial landscape trajectory. [ABSTRACT FROM AUTHOR]

Surface mining may be humanity's most tangible impact on Earth's surface and will become more prevalent as the energy transition progresses. Prediction of post-mining landscape change can help mitigate environmental damage, but requires understanding how mining changes geomorphic processes and variables. Here we investigate surface mining's complex influence on surface processes in a case study of mountaintop removal/valley fill (MTR/VF) coal mining in the Appalachian Coalfields, USA. The future of MTR/VF landscapes is unclear because mining's effects on geomorphic processes are poorly understood. We use geospatial analysis—leveraging the existence of pre- and post-MTR/VF elevation models—and synthesis of literature to ask how MTR/VF alters topography, hydrology, and land-surface erodibility and how these changes could be incorporated into numerical models of post-MTR/VF landscape evolution. MTR/VF reduces slope and area–slope product, and rearranges drainage divides. Creation of closed depressions alters flow routing and casts doubt on the utility of models that assume steady flow. MTR/VF creates two contrasting hydrologic domains, one in which overland flow is generated efficiently due to a lack of infiltration capacity, and one in which waste rock deposits act as extensive subsurface reservoirs. This dichotomy creates localized hotspots of overland flow and erosion. Loss of forest cover probably reduces cohesion in near-surface soils for at least the timescale of vegetation recovery, while waste rock fills and minesoils also likely experience reduced erosion resistance. Our analysis suggests three necessary ingredients for numerical modeling of post-MTR/VF landscape change: 1) accurate routing and accumulation of unsteady overland flow and accompanying sediment across low-gradient, depression-rich, engineered landscapes, 2) separation of the landscape into cut, filled, and unmined regions, and 3) incorporation of vegetation recovery trajectories. Improved modeling of post-mining landscape evolution will mitigate environmental degradation from past mining and reduce the impacts of future mining that supports the energy transition. [Display omitted] • Mountaintop removal mining flattens topography and moves drainage divides. • Cut and filled domains generate different quantities of erosive runoff. • Creation of many closed depressions reduces applicability of common models. • Vegetation loss and material property changes increase erodibility. • Our work reveals the necessary elements for models of post-mining landscape change. [ABSTRACT FROM AUTHOR]

Constraining Earth's sediment mass balance over geologic time requires a quantitative understanding of how landscapes respond to transient tectonic perturbations. However, the mechanisms by which bedrock lithology governs landscape response remain poorly understood. Rock type influences the size of sediment delivered to river channels, which controls how efficiently rivers respond to tectonic forcing. The Mendocino triple junction region of northern California, USA, is one landscape in which large boulders, delivered by hillslope failures to channels, may alter the pace of landscape response to a pulse of rock uplift. Boulders frequently delivered by earthflows in one lithology, the Franciscan mélange, have been hypothesized to steepen channels and slow river response to rock uplift, helping to preserve high-elevation, low-relief topography. Channels in other units (the Coastal Belt and the Franciscan schist) may experience little or no erosion inhibition due to boulder delivery. Here we investigate spatial patterns in channel steepness, an indicator of erosion resistance, and how it varies between mélange and non-mélange channels. We then ask whether lithologically controlled boulder delivery to rivers is a possible cause of steepness variations. We find that mélange channels are steeper than Coastal Belt channels but not steeper than schist channels. Though channels in all units steepen with increasing proximity to mapped hillslope failures, absolute steepness values near failures are much higher (~2x) in the mélange and schist than in Coastal Belt units. This could reflect reduced rock erodibility or increased erosion rates in the mélange and schist, or disproportionate steepening due to enhanced boulder delivery by hillslope failures in those units. To investigate the possible influence of lithology-dependent boulder delivery, we map boulders at failure toes in the three units. We find that boulder size, frequency, and concentration are greatest in mélange channels and that Coastal Belt channels have the lowest concentrations. Using our field data to parameterize a mathematical model for channel slope response to boulder delivery, we find that the modeled influence of boulders in the mélange could be strong enough to account for some observed differences in channel steepness between lithologies. At the landscape scale, we lack the data to fully disentangle boulder-induced steepening from that due to spatially varying erosion rates and in situ rock erodibility. However, our boulder mapping and modeling results suggest that lithology-dependent boulder delivery to channels could retard landscape adjustment to tectonic forcing in the mélange and potentially also in the schist. Boulder delivery may modulate landscape response to tectonics and help preserve high-elevation, low-relief topography at the Mendocino triple junction and elsewhere. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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 interaction among these earth 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 Topo Toolbox 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 in HyLands 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 is calculated using a Mohr-Coulomb stability analysis while landslide-derived sediment is routed and deposited using a multiple flow direction, non-linear 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 configurations. 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 FROM AUTHOR]

Geologists frequently debate the origin of iconic river canyons, as well as the extent to which they record climatic and tectonic signals. Fluvial and hillslope processes work in concert to control canyon evolution; rivers both set the boundary conditions for adjoining hillslopes and respond to delivery of hillslope-derived sediment. But, what happens when canyon walls deliver boulders that are too large for a river to carry? River canyons commonly host large blocks of rock derived from resistant hillslope lithologies. Blocks have recently been shown to control the shapes of hillslopes and channels by inhibiting sediment transport and bedrock erosion. Here, we developed the first process-based model for canyon evolution that incorporates the roles of blocks in both hillslope and channel processes. Our model reveals that two-way negative channel-hillslope feedbacks driven by block delivery to the river result in characteristic plan-view and cross-sectional river canyon forms. Internal negative feedbacks strongly reduce the rate at which erosional signals pass through landscapes, leading to persistent local unsteadiness even under steady tectonic and climatic forcing. Surprisingly, while the presence of blocks in the channel initially slows incision rates, the subsequent removal of blocks from the oversteepened channel substantially increases incision rates. This interplay between channel and hillslope dynamics results in highly variable long-term erosion rates. These autogenic channel-hillslope dynamics can mask external signals, such as changes in rock uplift rate, complicating the interpretation of landscape morphology and erosion histories. [ABSTRACT FROM AUTHOR]

Quantifying off-fault deformation (OFD) rates on geomorphic time scales (10²-105 yr) along strike-slip faults is critical for resolving discrepancies between geologic and geodetic slip-rate estimates, improving knowledge of seismic hazard, and understanding the influence of tectonic motion on landscapes. Quantifying OFD over these time scales is challenging without displacement markers such as offset terraces or geologic contacts. We present a landscape evolution model coupled with distributed lateral tectonic shear to show how drainage basins sheared by lateral tectonic motion can reveal OFD rates. The model shows that OFD rate can control the orientation of drainage basin topography: the faster the OFD rate, the greater the deflection of drainage basins toward a fault-parallel orientation. We apply the model to the southern San Andreas fault near the Mecca Hills (southern California, USA), where drainage basins change in orientation with proximity to the fault. Comparison of observed and modeled topography suggests that the OFD rate in the Mecca Hills follows an exponential-like spatial pattern with a maximum rate nearest the fault of 3.5 ± 1.5 mm/yr, which decays to ~0 at ~600 m distance from the fault. This rate is applicable since the initiation of differential rock uplift in the Mecca Hills ca. 760 ka. Our results suggest that OFD in this 800 m study area may be as high as 10% of total plate motion. This example demonstrates that curved drainage basins may be used to estimate OFD rates along strike-slip faults. [ABSTRACT FROM AUTHOR]

Models of landscape evolution by river erosion are often either transport-limited (sediment is always available, but may or may not be transportable) or detachment-limited (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. 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. 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 steady-state 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. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Characteristics of the land surface, such as steepness, relative slope position (e.g., ridge vs. valley), and roughness, can be digitally represented using a variety of methods. These digital representations, along with human‐annotated labels, can be provided to artificial intelligence algorithms to generate maps of landforms, such as those associated with river systems, glaciers, or human‐induced changes to the landscape. Given the large number of terrain derivatives that can be generated from digital elevation data, it is unclear how the chosen inputs impact the utility of the resulting maps. This study suggests that artificial intelligence mapping algorithms are sensitive to representations provided to them since different inputs resulted in varying map accuracies. We suggest a combination of features, which collectively describe relative slope position, steepness, and local terrain texture, as a means to represent the landscape and to serve as input to artificial intelligence algorithms. Our results can make it easier to train artificial intelligence algorithms to consistently and objectively find surficial features of interest across large swaths of Earth's surface. Key Points: We test the sensitivity of convolutional neural network geomorphic feature extraction approaches to the input feature spaceA three‐layer composite incorporating slope and topographic position index outperformed common inputs like hillshades and slopeshadesResults held true across four use cases including both natural and anthropogenic landforms [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A landscape's sediment grain size distribution is the product of, and an important influence on, earth surface processes and landscape evolution. Grains can be large enough that the motion of a single grain, infrequently mobile in size-selective transport systems, constitutes or triggers significant geomorphic change. We define these grains as boulders. Boulders affect landscape evolution; their dynamics and effects on landscape form have been the focus of substantial recent community effort. We review progress on five key questions related to how boulders influence the evolution of unglaciated, eroding landscapes: 1) What factors control boulder production on eroding hillslopes and the subsequent downslope evolution of the boulder size distribution? 2) How do boulders influence hillslope processes and long-term hillslope evolution? 3) How do boulders influence fluvial processes and river channel shape? 4) How do boulder-mantled channels and hillslopes interact to set the long-term form and evolution of boulder-influenced landscapes? 5) How do boulders contribute to geomorphic hazards, and how might improved understanding of boulder dynamics be used for geohazard mitigation? Boulders are produced on eroding hillslopes by landsliding, rockfall, and/or exhumation through the critical zone. On hillslopes dominated by local sediment transport, boulders affect hillslope soil production and transport processes such that the downslope boulder size distribution sets the form of steady-state hillslopes. Hillslopes dominated by nonlocal sediment transport are less likely to exhibit boulder controls on hillslope morphology as boulders are rapidly transported to the hillslope toe. Downslope transport delivers boulders to eroding rivers where the boulders act as large roughness elements that change flow hydraulics and the efficiency of erosion and sediment transport. Over longer timescales, river channels adjust their geometry to accommodate the boulders supplied from adjacent hillslopes such that rivers can erode at the baselevel fall rate given their boulder size distribution. The delivery of boulders from hillslopes to channels, paired with the channel response to boulder delivery, drives channel-hillslope feedbacks that affect the transient evolution and steady-state form of boulder-influenced landscapes. At the event scale, boulder dynamics in eroding landscapes represent a component of geomorphic hazards that can be mitigated with an improved understanding of the rates and processes associated with boulder production and mobility. Opportunities for future work primarily entail field-focused data collection across gradients in landscape boundary conditions (tectonics, climate, and lithology) with the goal of understanding boulder dynamics as one component of landscape self-organization. • We synthesize work to date exploring how boulders affect the evolution of nonglaciated, eroding hillslopes and channels. • Boulder dynamics are one facet of landscape self-organization, influencingtransient adjustment and steady-state form. • The effects of boulders on landscape shape can be quantified by combining field data with simplified theoretical models. • The key research need is for more field data on boulder dynamics spanning gradients in boundary conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Models of landscape evolution provide insight into the development 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 four 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 modeling 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 model elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs stem from 13 binary choices related to one of these four elements -- for example, the use of linear or non-linear hillslope diffusion. terrainbento is an extensible framework: model base classes that treat the elements common to all models (such as input/output and boundary conditions) make it possible to create a new model without re-inventing these common methods. terrainbento is built on top of the Landlab framework, such that new Landlab components directly support the creation of new terrainbento models. terrainbento is fully documented, has 100% unit test coverage including numerical comparison with % all available 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 model 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 process models. [ABSTRACT FROM AUTHOR]