1,721,060 research outputs found
Uncertainty propagation in aeolian processes: From threshold shear velocity to sand transport rate
Get PDFThe accurate estimation of aeolian saltation events is a fundamental requirement in the modelling of wind erosion, dust emission, dune movement and aeolian hazard prediction. A large number of semi-empirical sand transport rate models exist, with many relying on a single value for a shear velocity threshold above which saltation is initiated. However, measuring and modelling the sand transport rate suffers from the effects of a number of epistemic and aleatory uncertainties which make the identification of a single threshold value for shear velocity problematic. This paper focuses on the uncertainty propagation evident in calculations that use a threshold shear velocity to estimate sand transport rate. Probability density functions of threshold shear velocity are provided from the authors' previous studies. Grain diameter and shear velocity are considered as deterministically varying parameters. Several sand transport rate statistical metrics are estimated via the Monte Carlo approach adopting four different sand transport models. The sand transport rate estimation in probabilistic terms allows us to assess the amplification/reduction in the uncertainty and to provide a deeper insight into established transport rate models. We find that if the wind speed is close to the erosion threshold, every tested model amplifies the variability of the resulting estimated sand transport rate, especially in the case of coarse sand. If the wind speed is large, the adopted models present substantial differences in uncertainty. An interpretation of these differences is given by conditioning the sand transport rate models to the type of erosion threshold adopted, the fluid or impact threshold
Aeolian sand strip mobility and protodune development on a drying beach: examining surface moisture and surface roughness patterns measured by terrestrial laser scanning
No full textEphemeral aeolian sand strips are commonplace on beaches. Their formation during high energy sand transport events often precedes the development of protodunes and their dynamics present interesting feedback mechanisms with surface moisture patterns. However, due to their temporary nature, little is known of their formation, mobility or the specifics of their interaction with beach surface characteristics. Similarly surface moisture has an important influence on sediment availability and transport in aeolian beach systems, yet it is difficult to quantify accurately due to its inherent variability over both short spatial and temporal scales. Whilst soil moisture probes and remote sensing imagery techniques can quantify large changes well, their resolution over mainly dry sand, close to the aeolian transport threshold is not ideal, particularly where moisture gradients close to the surface are large. In this study we employed a terrestrial laser scanner to monitor beach surface moisture variability during a three and a half hour period after a rain event and investigated relationships between bedform development, surface roughness and surface moisture. Our results demonstrate that as the beach surface dries, sand transport increases, with sediment erosion occurring at the wet/dry surface boundary, and deposition further downwind. This dynamic structure, dependent upon changing surface moisture characteristics, results in the formation of a rippled sand strip and ultimately a protodune. Our findings highlight dynamic mobility relationships and confirm the need to consider transient bedforms and surface moisture across a variety of scales when measuring aeolian transport in beach settings. The terrestrial laser scanner provides a suitable apparatus with which to accomplish this
Surface and Meteorological Data at Sand Creek, Great Sand Dunes National Park, Colorado, USA in March and April 2019
No full textWind, sediment transport and surface morphological data collected at Sand Creek during a month long field campaign in March and April 2019 to investigate protodune development under bimodal winds. Data is used in the accepted paper ‘Dune initiation in a bimodal wind regime’, Journal of Geophysical Research: Earth Surface, by Delorme, P., Wiggs, G.F.S., Baddock, M.C., Claudin, P., Nield, J.M. and Valdez, A. (accepted 18th September 2020, article reference number 2020JF005757R; https://repository.lboro.ac.uk/articles/Dune_initiation_in_a_bimodal_wind_regime/12973817) Surface morphological data: This is terrestrial laser scanned (TLS) data collected of the creek sand surface during multiple visits. The data is raw point cloud format in text columns of x, y and z coordinate data. It has been orientation in local format (the origin is located at 13UTM 443152, 4184478). *_full_lowres cover the whole creek surface and the banks on either side. * is the date that the data was collected in yymmdd format. All other data is high resolution section of the actual creek surface within the channel. Each data set uses the same coordinate system. Data can be viewed in any spatial software. Wind and sediment data were collected from a fixed point on the eastern edge of the creek channel. The data is in csv file format with column titles and can be viewed in any text or database software. See Delorme et al. (accepted) for more details.</span
Early-stage aeolian protodunes: bedform development and sand transport dynamics
Get PDFEarly-stage aeolian bedforms, or protodunes, are elemental in the continuum of dune development and act as essential precursors to mature dunes. Despite this, we know very little about the processes and feedback mechanisms that shape these nascent bedforms. Whilst theory and conceptual models have offered some explanation for protodune existence and development, until now, we have lacked the technical capability to measure such small bedforms in aeolian settings. Here, we employ terrestrial laser scanning to measure morphological change at the high frequency and spatial resolution required to gain new insights into protodune behaviour. On a 0.06 m high protodune, we observe vertical growth of the crest by 0.005 m in two hours. Our direct measurements of sand transport on the protodune account for such growth, with a reduction in time-averaged sediment flux of 18% observed over the crestal region. Detailed measurements of form also establish key points of morphological change on the protodune. The position on the stoss slope where erosion switches to deposition is found at a point 0.07 m upwind of the crest. This finding supports recent models that explain vertical dune growth through an upwind shift of this switching point. Observations also show characteristic changes in the asymmetric cross section of the protodune. Flow-form feedbacks result in a steepening of the lee slope and a decline in lower stoss slope steepness (by 3°), constituting a reshaping of protodune form towards more mature dune morphology. The approaches and findings applied here, a) demonstrate an ability to quantify processes at requisite spatial and temporal scales for monitoring early-stage dune evolution, b) highlight the crucial role of form-flow feedbacks in enabling early-stage bedform growth, alluding to a fluctuation in feedbacks that require better representation in dune models, and c) provide a new stimulus for advancing understanding of aeolian bedforms
Surface and Meteorological Data at Medano Creek, Great Sand Dunes National Park, Colorado, USA on 15th April 2019
No full textWind and surface morphological data collected at Medano Creek on the 15th April 2019 to investigate protodune initiation. Surface morphological data: This is terrestrial laser scanned (TLS) data collected of the creek sand surface using three different co-located Leica TLS (C10, P20 and P50). The data is raw point cloud format in text columns of x, y and z coordinate data. It has been orientation into the same local coordinate system. Each data set uses the same coordinate system. Data can be viewed in any spatial software. Data is labelled using C10, P20 or P50, followed by the scan number. Scan times are indicated in a separate file. Wind data were collected from a fixed point next to the TLS instruments using a Gill 3D sonic anemometer. The data is in csv file format with column titles and can be viewed in any text or database software.</span
Surface and Meteorological Data at Huab River Valley, Skeleton Coast National Park, Namibia in September 2019
No full textWind, sediment transport and surface/saltation data collected at Huab River Valley during a field campaign in September 2019 to investigate saltation on gravel and sand surfaces. Surface/saltation data: This is terrestrial laser scanned (TLS) data collected over sand and gravel surfaces during multiple days when saltation was active, on a surface approximately 8 m from the TLS, perpendicular to the wind direction. The data is raw point cloud format in text columns of x, y and z coordinate data. Files are named *_^_scan&amp; where * is the date that the data was collected in yymmdd format, ^ is surface type (sand or gravel) and &amp; is the scan number. Each data set uses the same coordinate system. Data can be viewed in any spatial software. Wind and sediment data were collected from a fixed point on each surface, directly downwind of the TLS data. The data is in csv file format with column titles and can be viewed in any text or database software. Data include hot wire measurements at different heights, Wenglor counts, sensit counts and 3D sonic measurements on some days. Sonic data is at 10 Hz, hotwire data at 10 second intervals, transport data is given within both datasets.</span
Salt polygons and porous media convection
Get PDFFrom fairy circles to patterned ground and columnar joints, natural patterns
spontaneously appear in many complex geophysical settings. Here, we investigate
the origins of polygonally patterned crusts of salt playa and salt pans. These
beautifully regular features, approximately a meter in diameter, are found
worldwide and are fundamentally important to the transport of salt and dust in
arid regions. We show that they are consistent with the surface expression of
buoyancy-driven convection in the porous soil beneath a salt crust. By
combining quantitative results from direct field observations, analogue
experiments and numerical simulations, we further determine the conditions
under which salt polygons should form, as well as how their characteristic size
emerges.Comment: 22 pages, 12 figure
Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'
No full textThis repository contains the data used in: Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309–332 (2022). https://doi.org/10.1007/s10546-022-00733-6 where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses. The use this data, one should first look at the GitHub repository https://github.com/Cgadal/GiantDunes and at the corresponding documentation https://cgadal.github.io/GiantDunes/. The description sometimes refers to scripts used in https://github.com/Cgadal/GiantDunes/tree/master/Processing. The two folders 'raw_data' and 'processed_data' contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, '.npy' files contain Python dictionaries with different variables in them. They can be loaded using the Python library numpy as data = np.load('file.npy', allow_pickle=True).item(); and the different keys (variables) can be printed with data.keys() or data[station].keys() if data.keys() return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East. raw_data: DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/ ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/ ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/ KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth. measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers. processed_data: 'Data_preprocessed.npy': preprocessed_data, output of 1_data_preprocessing_plot.py 'Data_DEM.npy': properties of the processed DEM, the output of 2_DEM_analysis_plot.py 'Data_calib_roughness.npy': data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py 'Data_final.npy': file containing all computed quantities 'time_series_hydro_coeffs.npy': file containing the time series of the calculated hydrodynamic coefficients by '5_norun_hydro_coeff_time_series.npy'. Depending on the loaded data file, main dictionary keys can be: 'lat': latitude, in degree 'lon': longitude, in degree 'time': time vector, in datetime objects (https://docs.python.org/3/library/datetime.html) 'DEM': elevation data array in [m], with dimensions matching 'lat' and 'lon' vectors 'z_mes', 'z_insitu', 'z_ERA5LAND': height of the corresponding velocity 'direction': measured wind direction, in [degrees] 'velocity': measured wind velocity, in [m/s] 'orientaion': dune pattern orientation, [deg] 'wavelength': dune pattern wavelength, [km] 'z0_insitu': chosen hydrodynamic roughness for the considered station. 'U_insitu', 'Orientation_insitu': hourly averaged measured wind velocities and direction 'U_era', 'Orientation_era': hourly 10m wind data from the ERA5Land data set 'Boundary layer height', 'blh': boundary layer height from the hourly ERA5 dataset 'Pressure levels', 'levels': Pressure levels from the pressure levels ERA5 dataset 'Temperature', 't': Temperature from the pressure levels ERA5 dataset 'Specific humidity', 'q': Specific humidity from the pressure levels ERA5 dataset 'Geopotential', 'z': Geopotential from the pressure levels ERA5 dataset 'Virtual_potential_temperature': Virtual potential temperature calculated from the pressure levels ERA5 dataset 'Potential_temperature': Potential temperature calculated from the pressure levels ERA5 dataset 'Density': Density calculated from the pressure levels ERA5 dataset 'height': Vertical coordinates calculated from the pressure levels ERA5 dataset 'theta_ground': Averaged virtual potential temperature within the ABL. 'delta_theta': Virtual potential temperature at the ABL. 'gradient_free_atm': Virtual potential temperature gradient in the FA. 'Froude': time series of the Froude number U/((delta_theta/theta_ground)*g*BLH) 'kH': time series of the number 'kH' 'kLB': time series of the internal Froude number kU/N Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: https://github.com/Cgadal/GiantDunes, where all the codes are present.</span
Surface and Meteorological Data of Saltation and Ripple Dynamics at Huab Dune Field, Skeleton Coast National Park, Namibia 2014, 2016 and 2018
No full textThis dataset includes raw point cloud data from repeat terrestrial laser scans (TLS) of rippled surfaces on barchan and dome dunes within the Huab Dune Field, Skeleton Coast National Park, Namibia. This raw data can be used to extract saltation height dynamics as well as 3D ripple data including celerity. As well as the TLS data, additional measurements of the wind speed through a CSAT 3D sonic anemometer or cup anemometer and sediment transport using a Sensit and Wenglor gate sensor.</span
Measurements of flows over isolated valleys
Get PDFWind-tunnel measurements of the flow over an isolated valley both normal and at an angle (45°) to a simulated neutrally stable atmospheric boundary layer are presented. Attention is concentrated on the nature of the flow within the valley itself. The work formed part of a wider study that included detailed field measurements around an African desert valley and some limited comparisons with that work are included. A scale of about 1:1000 was used for the laboratory work, in which an appropriate combination of hot wire and particle image velocimetry was employed. For a valley normal to the upwind flow, it is shown that the upstream influence of the valley extends to a distance of at least one half of the axial valley width upstream of the leading edge, whereas differences in mean flow and turbulence could be identified well beyond two valley widths from the downwind edge. Non-normal wind angles lead to significant along-valley flows within the valley and, even at two valley heights above the valley ridge level, there remains a significant spanwise flow component. Downwind turbulence levels are somewhat lower in this case, but are still considerably higher than in the undisturbed boundary layer. At both flow angles, there are significant recirculation regions within the valleys, starting from mean separation just beyond the leading edge, but the strong spanwise flow in the 45° case reduces the axial extent of the separated zone. The flow is shown to be in some ways analogous to flow over an isolated hill. Our results usefully enhance the field data and could be used to improve modelling of saltation processes in the field
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