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
72Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit 73 comprising much of Earth's uplands. The convergent topography of these landscapes generates 74 spatially variable substrate and moisture content, facilitating biological diversity and influencing 75 how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of 76 these significant ecosystem services, refining our understanding of how these functions are 77 affected by landscape evolution, weather variability, and long-term climate change is imperative. 78 In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale 79 controllable infrastructure consisting of three replicated artificial landscapes (each 330 m 2 80 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, 81 experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are 82 possible at unprecedented scale. The Landscape Evolution Observatory was designed as a 83 community resource to advance understanding of how topography, physical and chemical 84 properties of soil, and biological communities coevolve, and how this coevolution affects water, 85 carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and 86 an extensive network of sensors and samplers, LEO enables an iterative scientific approach that 87 includes numerical model development and virtual experimentation, physical experimentation, 88 data analysis, and model refinement. We plan to engage the broader scientific community 89 through public dissemination of data from LEO, collaborative experimental design, and 90 community-based model development. 91 coevolution 93 94 95 1. Introduction 96Hillslopes and their adjacent hollows (i.e., zero-order drainage basins, or ZOBs) 97 constitute a large fraction of upland areas over Earth's surface and provide critical ecosystem 98 services. Within ZOBs there is exchange of water, carbon dioxide, and energy with the 99 atmosphere and transport of soil, water, and solutes into fluvial drainage networks-processes 100 that link ZOBs with the climate system and downstream water quantity and quality. The time-101 varying rates of these exchange and transport processes are integrated responses to many 102 physical and biological phenomena that occur from below the base of the soil profile to the 103 vertical extent of the atmospheric boundary layer (e.g., see discussion by Chorover et al., 2011). 104 Zero-order basins evolve as climate varies, soils form and erode, and biological 105 communities establish, compete, and change in response to environmental stimuli. Across 106 spatial and topographic gradients, these interacting processes may result in consistently 107 observable correlations between temperature and precipitation dynamics, soil depth and hillslope 108 length, and plant biomass accumulation (e.g., Rasmussen et al., 2011; Pelletier et al., 2013). 109 Coupled soil-production and ...
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