[1] A geometric framework for the automatic extraction of channels and channel networks from high-resolution digital elevation data is introduced in this paper. The proposed approach incorporates nonlinear diffusion for the preprocessing of the data, both to remove noise and to enhance features that are critical to the network extraction. Following this preprocessing, channels are defined as curves of minimal effort, or geodesics, where the effort is measured on the basis of fundamental geomorphological characteristics such as flow accumulation area and isoheight contours curvature. The merits of the proposed methodology, and especially the computational efficiency and accurate localization of the extracted channels, are demonstrated using light detection and ranging (lidar) data of the Skunk Creek, a tributary of the South Fork Eel River basin in northern California.

International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

Connectivity describes the efficiency of material transfer between geomorphic system components such as hillslopes and rivers or longitudinal segments within a river network. Representations of geomorphic systems as networks should recognize that the compartments, links, and nodes exhibit connectivity at differing scales. The historical underpinnings of connectivity in geomorphology involve management of geomorphic systems and observations linking surface processes to landform dynamics. Current work in geomorphic connectivity emphasizes hydrological, sediment, or landscape connectivity. Signatures of connectivity can be detected using diverse indicators that vary from contemporary processes to stratigraphic records or a spatial metric such as sediment yield that encompasses geomorphic processes operating over diverse time and space scales. One approach to measuring connectivity is to determine the fundamental temporal and spatial scales for the phenomenon of interest and to make measurements at a sufficiently large multiple of the fundamental scales to capture reliably a representative sample. Another approach seeks to characterize how connectivity varies with scale, by applying the same metric over a wide range of scales or using statistical measures that characterize the frequency distributions of connectivity across scales. Identifying and measuring connectivity is useful in basic and applied geomorphic research and we explore the implications of connectivity for river management. Common themes and ideas that merit further research include; increased understanding of the importance of capturing landscape heterogeneity and connectivity patterns; the potential to use graph and network theory metrics in analyzing connectivity; the need to understand which metrics best represent the physical system and its connectivity pathways, and to apply these metrics to the validation of numerical models; and the need to recognize the importance of low levels of connectivity in some situations. We emphasize the value in evaluating boundaries between components of geomorphic systems as transition zones and examining the fluxes across them to understand landscape functioning. © 2018 John Wiley & Sons, Ltd.

Deltaic systems are composed of distributary channels and interdistributary islands. While previous work has focused either on the channels or on the islands, here we study the hydrological exchange between channels and islands and point at its important role in delta morphology and ecology. We focus our analysis on Wax Lake Delta in coastal Louisiana (USA) and characterize the surface water component of hydrological connectivity through measurements of water discharge and hydraulic tracer propagation. We find that deltaic islands are zones of significant water flux as 23-54% of the incoming distributary channel flux enters the islands. A calculation of the travel times through a channel-island complex shows travel times through the islands to be at least 3 times their channel counterparts. A dye release experiment also indicates that travel times in islands are much longer that those within channels as dye remained in the island for the 3.8 day duration of the experiment. Additionally, islands are more sensitive than channels to environmental forces such as tides, which cause flow reversal and thus can increase travel times through the islands. Our work defines the ''hydrological network'' of a river delta to include not only the distributary channel network but also the interdistributary islands, quantifies the implications of channel-island hydrological connectivity to travel times through the system, and discusses the relevance of our findings to channel mouth dynamics at the delta front and the potential for denitrification in coastal systems.

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River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.

[1] The next generation of digital elevation data (≤3 m resolution) calls for the development of new algorithms for the objective extraction of geomorphic features, such as channel networks, channel heads, bank geometry, landslide scars, and service roads. In this work, we test the performance of two newly developed algorithms for the extraction of geomorphic features: the wavelet-based extraction methodology developed by Lashermes et al. (2007) and the GeoNet nonlinear diffusion and geodesic paths methodology proposed by Passalacqua et al. (2010). The study area is part of the Rio Cordon basin, a headwater alpine catchment located in the Dolomites, a mountainous region in the eastern Italian Alps. The aim of this work is to compare the capability of the two new algorithms in extracting the channel network and capturing channel heads, relevant channel disruptions corresponding to landslides, and representative channel cross sections. The extracted channel networks are also compared to the ones obtained using classical methodologies on the basis of an area threshold and an area-slope threshold. A high-resolution digital terrain model of 1 m served as the basis for such analysis. The results suggest that, although the wavelet-based methodology performs well in the channel network extraction and is able to detect channel heads and channel disruptions, the local nonlinear filter together with the global geodesic optimization used in GeoNet is more robust and computationally efficient while achieving better localization and extraction of features, especially in areas where gentle slopes prevail. We conclude that these new methodologies should be considered as valid alternatives to classical methodologies for channel network extraction from lidar, in addition to offering the potential for calibrationfree channel source identification and also extraction of additional features of interest.Citation: Passalacqua, P., P. Tarolli, and E. Foufoula-Georgiou (2010), Testing space-scale methodologies for automatic geomorphic feature extraction from lidar in a complex mountainous landscape, Water Resour. Res., 46, W11535,

[1] High-resolution topographic data derived from light detection and ranging (lidar) technology enables detailed geomorphic observations to be made on spatially extensive areas in a way that was previously not possible. Availability of this data provides new opportunities to study the spatial organization of landscapes and channel network features, increase the accuracy of environmental transport models, and inform decisions for targeting conservation practices. However, with the opportunity of increased resolution topographic data come formidable challenges in terms of automatic geomorphic feature extraction, analysis, and interpretation. Low-relief landscapes are particularly challenging because topographic gradients are low, and in many places both the landscape and the channel network have been heavily modified by humans. This is especially true for agricultural landscapes, which dominate the midwestern United States. The goal of this work is to address several issues related to feature extraction in flat lands by using GeoNet, a recently developed method based on nonlinear multiscale filtering and geodesic optimization for automatic extraction of geomorphic features (channel heads and channel networks) from high-resolution topographic data. Here we test the ability of GeoNet to extract channel networks in flat and human-impacted landscapes using 3 m lidar data for the Le Sueur River Basin, a 2880 km 2 subbasin of the Minnesota River Basin. We propose a curvature analysis to differentiate between channels and manmade structures that are not part of the river network, such as roads and bridges. We document that Laplacian curvature more effectively distinguishes channels in flat, human-impacted landscapes compared with geometric curvature. In addition, we develop a method for performing automated channel morphometric analysis including extraction of cross sections, detection of bank locations, and identification of geomorphic bankfull water surface elevation. Using the slope plotted along each channel-floodplain cross section, we demonstrate the ability to identify and measure the height of river banks and bluffs. Finally, we present an example that demonstrates how extracting such features automatically is important for modeling channel evolution, water and sediment transport, and channel-floodplain sediment exchange.