Earth's terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution gridded global data set of the average thicknesses of soil, intact regolith, and sedimentary deposits within each 30 arcsec (1 km) pixel using the best available data for topography, climate, and geology as input. Our data set partitions the global land surface into upland hillslope, upland valley bottom, and lowland landscape components and uses models optimized for each landform type to estimate the thicknesses of each subsurface layer. On hillslopes, the data set is calibrated and validated using independent data sets of measured soil thicknesses from the U.S. and Europe and on lowlands using depth to bedrock observations from groundwater wells in the U.S. We anticipate that the data set will prove useful as an input to regional and global hydrological and ecosystems models.

Yardangs are streamlined hills formed in part by the erosive action of wind and wind‐blown sediments. Here we examine the controls on yardang development and morphology using the Ocotillo Wells State Vehicular Recreation Area (OWSVRA), California, as a study site. We measured the compressive strengths, strikes, and dips of bedrock strata, eolian sediment fluxes (including their vertical profiles and spatial variations around yardangs), and erosion rates derived from geologic constraints and multitemporal Terrestrial Laser Scanning (TLS). We used a combination of TLS‐based and airborne lidar‐based Digital Elevation Models (DEMs) to test the applicability of an asymmetric Gaussian function for characterizing yardang form and quantify the relationships among yardang lengths, widths, heights, spacings, and their controlling factors. Yardang aspect ratios are controlled by bedrock structural attributes, specifically by the tangent of the dip and the angle between the strike and the prevailing wind direction. Yardang spacings scale linearly with yardang width. Yardang heights increase as the square root of width such that larger yardangs tend to have gentler side slopes. Sediment fluxes reach a maximum in the troughs among yardangs, consistent with the hypothesis that yardang development involves the focusing of wind and wind‐blown sediments into troughs. The vertical distribution of eolian sediment flux follows a power law with an exponent of −2.5, a result consistent with an advection‐diffusion‐settling model of transport near the saltation‐suspension transition. Erosion rates are several mm/yr over time scales of ~100 and ~106 years.

[1] Shoreline erosion is a natural trend along most sandy coastlines. Humans often respond to shoreline erosion with beach nourishment to maintain coastal property values. Locally extending the shoreline through nourishment alters alongshore sediment transport and changes shoreline dynamics in adjacent coastal regions. If left unmanaged, sandy coastlines can have spatially complex or simple patterns of erosion due to the relationship of large-scale morphology and the local wave climate. Using a numerical model that simulates spatially decentralized and locally optimal nourishment decisions characteristic of much of U.S. East Coast beach management, we find that human erosion intervention does not simply reflect the alongshore erosion pattern. Spatial interactions generate feedbacks in economic and physical variables that lead to widespread emergence of "free riders" and "suckers" with subsequent inequality in the alongshore distribution of property value. Along cuspate coastlines, such as those found along the U.S. Southeast Coast, these long-term property value differences span an order of magnitude. Results imply that spatially decentralized management of nourishment can lead to property values that are divorced from spatial erosion signals; this management approach is unlikely to be optimal.

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We performed power-spectral analyses on 133 globally distributed lake-level time series after removing annual variability. Lake-level power spectra are found to be power-law functions of frequency over the range of 20 d 21 to 27 yr 21 , suggesting that lake levels are globally a f 2b -type noise. The spectral exponent (b), i.e., the best-fit slope of the logarithm of the power spectrum to the logarithm of frequency, is a nonlinear function of lake surface area, indicating that lake size is an important control on the magnitude of water-level variability over the range of time scales we considered. A simple cellular model for lake-level fluctuations that reproduces the observed spectral-scaling properties is presented. The model (an adaptation of a surface-growth model with random deposition and relaxation) is based on the equations governing flow in an unconfined aquifer with stochastic inputs and outputs of water (e.g., random storms). The agreement between observation and simulation suggests that lake surface area, spatiotemporal stochastic forcing, and diffusion of the groundwater table are the primary factors controlling lake water-level variability in natural (unmanaged) lakes. Water-level variability is generally considered to be a manifestation of climate trends or climate change, yet our work shows that an input with short or no memory (i.e., weather) gives rise to a long-memory nonstationary output (lake water-level). This work forms the basis for a null hypothesis of lake water-level variability that should be disproven before water-level trends are to be attributed to climate.