Deviations from self-similarity in barchan form and flux: The case of the Salton Sea dunes, California

Abstract

[1] Barchans are the type of aeolian dune associated with a relatively uniform wind direction, incomplete sand coverage of the substrate, and low vegetation cover. Here I present an analysis of the morphology and migration rates of 40 dunes in the Salton Sea dune field using historical aerial orthophotographs, airborne laser swath mapping, terrestrial laser scanning, and measurements of the aerodynamic roughness length derived from wind velocity profiles. The data demonstrate that the Salton Sea dunes deviate from self-similarity such that smaller dunes have a lower ratio of slip face height to crest height and a lower slope, on average, compared with larger dunes and that smaller dunes migrate more slowly than would be predicted based on an inverse relationship between migration rate and dune height. The lack of self-similarity in barchans has been attributed to the dependence of speed-up ratios on dune size and the presence of a finite saturation length in the physics of aeolian transport. Here I argue that deviations from self-similarity at this study site are more likely due to the systematic decrease in aerodynamic roughness length with increasing elevation on stoss slopes. The data set I developed should prove useful to the aeolian geomorphic community for the further testing of models for barchan evolution.

Laser vision: lidar as a transformative tool to advance critical zone science

“…Meter-and sub-meter-scale time-varying processes, often derived from TLS, have been quantified in the response of point bar and bank morphodynamics (Lotsari et al, 2014) and in the formation of micro-topography due to feedbacks with biota (e.g., Roering et al, 2010;Harman et al, 2014). Examples of larger scale change detection applications, typically ALS-derived, include measuring changes in stream channel pathways resulting from Holocene climate change and anthropogenic activities (e.g., Day et al, 2013;Kessler et al, 2012;James et al, 2012;Belmont et al, 2011), rates of change in migrating sand dunes (Pelletier, 2013), the influence of lithology and climate on hillslope form (e.g., Marshall and Roering, 2014;Hurst et al, 2013;Perron et al, 2008;West et al, 2014), and channel head formation (e.g., Pelletier et al, 2013;Pelletier and Perron, 2012;Perron and Hamon, 2012). Automated tools to identify geomorphic features (e.g., floodplains, terraces, landslides) and transitional zones (e.g., hillslope-to-valley, floodplain-tochannel) have been used in conjunction with high-resolution elevation data sets from lidar, including Geonet 2.0 (Passalacqua et al, 2010), ALMTools (Booth et al, 2009), and TerrEX (Stout and Belmont, 2014).…”

“…The few studies in the center of the triangle could be considered as potential exemplars of CZ science using lidar as they balance well among each cornerstone discipline. Several studies were transdisciplinary in nature, but focused on lidar-derived topography and did not maximize information content on hydrological and ecological processes from lidar , Persson et al, 2012, Brubaker et al, 2013, Pelletier, 2013, Rengers and Tucker, 2014, and Pelletier and Orem, 2014. We instead draw focus to transdisciplinary studies that demonstrate the potential for complementary information to be extracted from lidar and integrated into field campaigns to allow multi-scale observations of interacting geomorphologic, hydrologic, and ecologic processes.…”

“…We highlight three studies that can serve as possible road maps to guide future transdisciplinary investigations using lidar data sets (Fig. 2): Harman et al (2014), Pelletier et al (2013), and Perignon et al (2013). These studies used complementary information from lidar to develop fundamental transdisciplinary advances in the theories and understanding of CZ processes and structure.…”

“…Lidar-based change-detection analyses (CDA), i.e., mapping landscape adjustments through time in multi-temporal ALS and TLS data sets, have provided comprehensive measurements of snow depth (e.g., Harpold et al, 2014b;Tinkham et al, 2014) and ablation (Egli et al, 2011), co-seismic displacements after earthquakes (e.g., Oskin et al, 2012;Nissen et al, 2014), changes in aeolian dune form and migration rates (e.g., Pelletier, 2013), fluvial erosion (e.g., Anderson and Pitlick, 2014;Pelletier and Orem, 2014), earthflow displacements (e.g., DeLong et al, 2011), knickpoint migration in gully/channel systems (e.g., Rengers and Tucker, 2014), cliff retreat along coasts (Young et al, 2010), permafrost degradation (Levy et al, 2013;Barnhart and Crosby, 2013), forest growth (Yu et al, 2004;Naesset and Gobakken, 2005), and changes in biomass (e.g., Meyer et al, 2013;Olsoy et al, 2014). Traditionally, lidar point clouds have been rasterized prior to differencing using open-source processing toolkits (e.g., GCD (Geomorphic Change Detection); e.g., Wheaton et al, 2010a).…”

“…Lidar has also proven to be instrumental in the verification of model states. For example, lidar data sets have been used to verify physically based models, including landscape evolution models (Pelletier et al, 2014;Pelletier and Perron, 2012;Rengers and Tucker, 2014), aeolian models Pelletier, 2013), physiological models , snowpack energy balance models (Essery et al, 2008, Broxton et al, 2014, and an ecosystem dynamics model (Antonarakis et al, 2014). Simpler, empirical models have also been developed using lidar-derived estimates of soil erosion (Pelletier and Orem, 2014) and snow accumulation and ablation (Varhola et al, 2014).…”