Arnaud J. A. M. Temme

The spatial pattern of landslide susceptibility is a key input for decision making by many natural hazard agencies. Therefore, the estimation of landslide susceptibility maps has received much attention in the last decades. Increasingly, such maps are produced by statistical methods that relate the locations of observed landslides to geofactors such as slope steepness or vegetation density. Almost without exception, these susceptibility assessments are entirely spatial. At the same time, recent studies of large multitemporal landslide datasets have shown empirically that landslide susceptibility changes over time as well as space, as a result of the impact of recent nearby landslides. In at least two study sites, places near previous landslides are temporarily more susceptible to landsliding, sometimes substantially so. Several candidate mechanisms underlie this form of complexity (called path-dependency) in the landslide system, and targeted field measurements in landslide-prone study sites should be recorded to fully understand which mechanism is most important.Awaiting such measurements, physically-based mechanistic modelling of landslide impacts in the soil-landscape system can help explore the possible mechanisms. Here, we report on our development of landslide simulation capabilities in soil-landscape evolution model LORICA. In this model, landslides affect not only surface elevation, but also local soil and vegetation properties. Since other processes in the model also affect these properties, the impact of landslides is not permanent. Applied to a hypothetical soil-landscape, this model allows us to explore whether a) local topographic effects such as oversteepening, b) temporarily changed soil hydraulic parameters, or c) disruption of vegetation and roots, are the most likely mechanisms behind landslide path-dependency.

Geomorphic transport laws that describe the movement of material across landscapes generally hide the role of lithological and climatic boundary conditions behind proportionality terms. The geomorphic community aims to characterize the role of these boundary conditions and include them in mechanistic transport laws. A more detailed mechanistic understanding is needed for hillslopes formed in heterolithic rock that weathers into weak regolith and hard blocks, such as in hogbacks. The properties of such blocks may determine both the process and rate of their transport downslope. We focused on soil mantled hillslopes in the Flint Hills of Kansas in the United States, where we studied the role of block shape and size under a limestone cliff that breaks up approximately into equal amounts of cubic and tile‐shaped clasts. Based on previous studies on the transport of rock clasts, we hypothesized that cubic and tile‐shaped blocks should be transported differently, leading to different distributions of size and orientation with increasing distance from the cliff. Block shape does have significant influence over how soil collects and is stored on the hillslope near blocks. Yet, few cubic blocks appear to be transported through tumbling in contrast with assumptions in recent modeling work. We found complex relationships between block size and distance from the cliff face and we propose that this is due to weathering via fragmentation. This process produces discrete smaller fragments from a larger parent block. We suggest that at least in Kansas both cubic and tile‐shaped blocks are transported primarily by creep processes.

We present a set of new geomorphometric variables that express landscape position relative to breaklines in hillslopes, and test whether these variables are of value in explaining soil property variation in three study sites in the United States, the Netherlands, and Spain. Underlying this work is the recognition that slope breaks, such as cliff lines, lynchets, and large slumped blocks, are associated with processes that affect soil formation around them. For each study site, we digitized slope breaks and calculated vertical and horizontal distance to the nearest lower and higher slope breaks, as well as the relative position between multiple slope breaks, the slope increase at the next higher slope break, and the slope decrease at the next lower slope break. We then assessed the value of these geomorphometric variables in the prediction of slope properties by simple linear regression. At each study site, models were fitted to existing soil observations using a traditional set of geomorphometric variables, the new set of variables, and both sets together.

Significance: Peatlands are sensitive ecosystems that store carbon and water and support biodiversity. Currently, European peatlands are threatened by climate change and exploitation. In this study, we show that many landscape settings may support both wetland ecosystems on thick peat soils and forest ecosystems on thin organic soils. Both ecosystems have distinctly different water–carbon dynamics that create internal positive feedbacks, allowing both ecosystems to coexist (bistability) but also to shift when critical limits are exceeded. With this new landscape perspective, we find that currently, 20% of European raised bogs are threatened by climate change and drainage. This study demonstrates that a landscape perspective including interactions between peatlands, forests, and rivers is essential to understand and steer the future of peatlands.

Humans have triggered or accelerated erosion processes since prehistoric times through agricultural practices. Optically stimulated luminescence (OSL) is widely used to quantify phases and rates of the corresponding landscape change, by measuring the last moment of daylight exposure of sediments. However, natural and anthropogenic mixing processes, such as bioturbation and tillage, complicate the use of OSL as grains of different depositional ages become mixed, and grains become exposed to light even long after the depositional event of interest. Instead, OSL determines the stabilization age, indicating when sediments were buried below the active mixing zone. These stabilization ages can cause systematic underestimation when calculating deposition rates.Our focus is on colluvial deposition in a kettle hole in the Uckermark region, northeastern Germany. We took 32 samples from five locations in the colluvium filling the kettle hole to study both spatial and temporal patterns in colluviation. We combined OSL dating with advanced age modelling to determine the stabilization age of colluvial sediments. These ages were combined with an archaeological reconstruction of historical ploughing depths to derive the levels of the soil surface at the moment of stabilization; the deposition depths, which were then used to calculate unbiased deposition rates.We identified two phases of colluvial deposition. The oldest deposits (~5 ka) were located at the fringe of the kettle hole and accumulated relatively slowly, whereas the youngest deposits (<0.3 ka) rapidly filled the central kettle hole with rates of two orders of magnitude higher. We suggest that the latter phase is related to artificial drainage, facilitating accessibility in the central depression for agricultural practices. Our results show the need for numerical dating techniques that take archaeological and soil‐geomorphological information into account to identify spatiotemporal patterns of landscape change, and to correctly interpret landscape dynamics in anthropogenically influenced hilly landscapes. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.

Semi‐arid ecosystems are often spatially self‐organized in typical patterns of vegetation bands with high plant cover interspersed with bare soil areas, also known as ‘tiger bush’. In modelling studies, most often, straight planar slopes were used to analyse vegetation patterning. The effect of slope steepness has been investigated widely, and some studies investigated the effects of microtopography and hillslope orientation. However, at the larger catchment scale, the overall form of the landscape may affect vegetation patterning and these more complex landscapes are much more prevalent than straight slopes. Hence, our objective was to determine the effect of landform variation on vegetation patterning and sediment dynamics. We linked two well‐established models that simulate (a) plant growth, death and dispersal of vegetation, and (b) erosion and sedimentation dynamics. The model was tested on a straight planar hillslope and then applied to (i) a set of simple synthetic topographies with varying curvature and (ii) three more complex, real‐world landscapes of distinct morphology. Results show banded vegetation patterning on all synthetic topographies, always perpendicular to the slope gradient. Interestingly, we also found that movement of bands – a debated phenomenon – seems to be dependent on curvature. Vegetation banding was simulated on the slopes of the alluvial fan and along the valley slopes of the dissected and rolling landscapes. In all landscapes, local valleys developed a full vegetation cover induced by water concentration, which is consistent with observations worldwide. Finally, banded vegetation patterns were found to reduce erosion significantly as compared to other vegetation configurations. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Landscape evolution models (LEMs) are an increasingly popular resource for geomorphologists as they can operate as virtual laboratories where the implications of hypotheses about processes over human to geological timescales can be visualized at spatial scales from catchments to mountain ranges. Hypothetical studies for idealized landscapes have dominated, although model testing in real landscapes has also been undertaken. So far however, numerical landscape evolution models have rarely been used to aid field‐based reconstructions of the geomorphic evolution of actual landscapes. To help make this use more common, we review numerical landscape evolution models from the point of view of model use in field reconstruction studies. We first give a broad overview of the main assumptions and choices made in many LEMs to help prospective users select models appropriate to their field situation. We then summarize for various timescales which data are typically available and which models are appropriate. Finally, we provide guidance on how to set up a model study as a function of available data and the type of research question. Copyright © 2017 John Wiley & Sons, Ltd.