ALBERT

Description: Tectonic plate motion, and the resulting change in land surface elevation, has been shown to have a fundamental impact on landscape morphology. Changes to uplift rates can drive a response in fluvial channels, which then drives changes to hillslopes. Because hillslopes respond on different time scales than fluvial channels, investigating the geometry of channels and hillslopes in concert provides novel opportunities to examine how uplift rates may have changed through time. Here we perform coupled topographic analysis of channel and hillslope geometry across a series of catchments at the Mendocino triple junction (MTJ) in northern California, USA. These catchments are characterized by an order-of-magnitude difference in uplift rate from north to south. We find that dimensionless hillslope relief closely matches the uplift signal across the area and is positively correlated with channel steepness. Furthermore, the range of uncertainty in hillslope relief is lower than that of channel steepness, suggesting that it may be a more reliable recorder of uplift in the MTJ region. We find that hilltop curvature lags behind relief in its response to uplift, which in turn lags behind channel response. These combined metrics show the northward migration of the MTJ and the corresponding uplift field from topographic data alone.

Description: In many locations, our ability to study the processes which shape the Earth are greatly enhanced through the use of high resolution digital topographic data. However, although the availability of such datasets has markedly increased in recent years, many locations of significant geomorphic interest still do not have high resolution topographic data available. Here, we aim to constrain how well we can understand surface processes through topographic analysis performed on lower resolution data. We generate digital elevation models from point clouds at a range of grid sizes from 1 to 30 m, which covers the range of widely used data resolutions available globally, at three locations in the United States. Using this data, the relationship between curvature and grid resolution is explored, alongside the estimation of the hillslope sediment transport coefficient (D, in m2 yr−1) for each landscape. Curvature, and consequently D, values are shown to be generally insensitive to grid resolution, particularly in landscapes with broad hilltops and valleys. Curvature distributions, however, become increasingly condensed around the mean, and theoretical considerations suggest caution should be used when extracting curvature from landscapes with sharp ridges. Two methods of extracting channels from topographic data are tested. A geometric method of channel extraction that finds channels by detecting threshold values of planform curvature is shown to perform well at resolutions up to 30 m in all three landscapes. The landscape parameters of hillslope length and relief are both successfully extracted at the same range of resolutions. These parameters can be used to detect landscape transience and our results suggest that such work need not be confined to high resolution topographic data. A synthesis of the results presented in this work indicate that although high resolution (e.g., 1 m) topographic data does yield exciting possibilities for geomorphic research, many key parameters can be understood in lower resolution data, given careful consideration of how analyses are performed.

Description: In many locations, our ability to study the processes which shape the Earth are greatly enhanced through the use of high-resolution digital topographic data. However, although the availability of such datasets has markedly increased in recent years, many locations of significant geomorphic interest still do not have high-resolution topographic data available. Here, we aim to constrain how well we can understand surface processes through topographic analysis performed on lower-resolution data. We generate digital elevation models from point clouds at a range of grid resolutions from 1 to 30 m, which covers the range of widely used data resolutions available globally, at three locations in the United States. Using these data, the relationship between curvature and grid resolution is explored, alongside the estimation of the hillslope sediment transport coefficient (D, in m2 yr−1) for each landscape. Curvature, and consequently D, values are shown to be generally insensitive to grid resolution, particularly in landscapes with broad hilltops and valleys. Curvature distributions, however, become increasingly condensed around the mean, and theoretical considerations suggest caution should be used when extracting curvature from landscapes with sharp ridges. The sensitivity of curvature and topographic gradient to grid resolution are also explored through analysis of one-dimensional approximations of curvature and gradient, providing a theoretical basis for the results generated using two-dimensional topographic data. Two methods of extracting channels from topographic data are tested. A geometric method of channel extraction that finds channels by detecting threshold values of planform curvature is shown to perform well at resolutions up to 30 m in all three landscapes. The landscape parameters of hillslope length and relief are both successfully extracted at the same range of resolutions. These parameters can be used to detect landscape transience and our results suggest that such work need not be confined to high-resolution topographic data. A synthesis of the results presented in this work indicates that although high-resolution (e.g., 1 m) topographic data do yield exciting possibilities for geomorphic research, many key parameters can be understood in lower-resolution data, given careful consideration of how analyses are performed.

Description: Floodplain and terrace features can provide information about current and past fluvial processes, including channel response to varying discharge and sediment flux; sediment storage; and the climatic or tectonic history of a catchment. Previous methods of identifying floodplain and terraces from digital elevation models (DEMs) tend to be semi-automated, requiring the input of independent datasets or manual editing by the user. In this study we present a new, fully automated method of identifying floodplain and terrace features based on two thresholds: local gradient, and elevation compared to the nearest channel. These thresholds are calculated statistically from the DEM using quantile-quantile plots and do not need to be set manually for each landscape in question. We test our method against field-mapped floodplain initiation points, published flood hazard maps, and digitised terrace surfaces from seven field sites from the US and one field site from the UK. For each site, we use high-resolution DEMs derived from light detection and ranging (LiDAR) where available, as well as coarser resolution national datasets to test the sensitivity of our method to grid resolution. We find that our method is successful in extracting floodplain and terrace features compared to the field-mapped data from the range of landscapes and grid resolutions tested. The method is most accurate in areas where there is a contrast in slope and elevation between the feature of interest and the surrounding landscape, such as confined valley settings. Our method provides a new tool for rapidly and objectively identifying floodplain and terrace features on a landscape scale, with applications including flood risk mapping, reconstruction of landscape evolution, and quantification of sediment storage routing.

Description: Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The productivity and even survival of salt marsh vegetation depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. Determining platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labor-intensive field surveys and digitization. We propose a novel, unsupervised method to reproducibly isolate saltmarsh scarps and platforms from a DEM, referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that saltmarshes mature into sub-horizontal platforms delineated by sub-vertical scarps: based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six saltmarshes in England with varying tidal ranges and geometries, for which topographic platforms were manually distinguished from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one sites maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitized platforms, and have similar elevation distributions. We also find that our method allows the accurate detection of local bloc failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitized as part of the marsh platform, unsupervised classification categorizes them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method would have increased accuracy if used in combination with existing creek detection algorithms. Fallen blocs and high tidal flat portions, associated with potential pioneer zones, may also be areas of discordance between our method and supervised mapping. Although pioneer zones prove difficult to classify using a topographic method, it also suggests that these transition areas should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms. Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyze topographic evolution.

Description: Floodplain and terrace features can provide information about current and past fluvial processes, including channel response to varying discharge and sediment flux, sediment storage, and the climatic or tectonic history of a catchment. Previous methods of identifying floodplain and terraces from digital elevation models (DEMs) tend to be semi-automated, requiring the input of independent datasets or manual editing by the user. In this study we present a new method of identifying floodplain and terrace features based on two thresholds: local gradient, and elevation compared to the nearest channel. These thresholds are calculated statistically from the DEM using quantile–quantile plots and do not need to be set manually for each landscape in question. We test our method against field-mapped floodplain initiation points, published flood hazard maps, and digitised terrace surfaces from seven field sites from the US and one field site from the UK. For each site, we use high-resolution DEMs derived from light detection and ranging (lidar) where available, as well as coarser resolution national datasets to test the sensitivity of our method to grid resolution. We find that our method is successful in extracting floodplain and terrace features compared to the field-mapped data from the range of landscapes and grid resolutions tested. The method is most accurate in areas where there is a contrast in slope and elevation between the feature of interest and the surrounding landscape, such as confined valley settings. Our method provides a new tool for rapidly and objectively identifying floodplain and terrace features on a landscape scale, with applications including flood risk mapping, reconstruction of landscape evolution, and quantification of sediment storage and routing.

Description: Changes in the steepness of river profiles or abrupt vertical steps (i.e. waterfalls) are thought to be indicative of changes in erosion rates, lithology, or other factors that affect landscape evolution. These changes are referred to as knickpoints or knickzones and are pervasive in bedrock river systems. Such features are thought to reveal information about landscape evolution and patterns of erosion, and therefore their locations are often reported in the geomorphic literature. It is imperative that studies reporting knickpoints and knickzones use a reproducible method of quantifying their locations, as their number and spatial distribution play an important role in interpreting tectonically active landscapes. In this contribution we introduce a reproducible knickpoint and knickzone extraction algorithm that uses river profiles transformed by integrating drainage area along channel length (the so-called integral or χ method). The profile is then statistically segmented and the differing slopes and step changes in elevations of these segments are used to identify knickpoints and knickzones, and their relative magnitudes. The output locations of identified knickpoints and knickzones compare favourably with human mapping: we test the method on Santa Cruz Island, CA, using previously reported knickzones and also test the method against a new dataset from the Quadrilátero Ferrífero in Brazil. The algorithm allows extraction of varying knickpoint morphologies, including stepped, positive slope-breaks (concave upward) and negative slope-break knickpoints. We identify parameters that most affect the resulting knickpoint and knickzone locations, and provide guidance for both usage and outputs of the method to produce reproducible knickpoint datasets.