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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]

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]