poster egu 5
TRANSCRIPT
Aeolian Simulation Laboratory
Spatial differences in aeolian erosion of arid silty- sand soils due to surface featuresSpatial differences in aeolian erosion of arid silty- sand soils due to surface featuresEdri, A1., Katra, I1., Avraham, D2.1Department of Geography and Environmental Development, Ben-Gurion University of the Negev2Environmental Research Unit, Negev Nuclear Research Center
Belnap.J, Gillette. D.A. (1998). "Vulnerability of desert biological soil crusts to wind erosion: the influences of crust development, soil texture, and disturbance". Journal of Arid Environments, 39, 133–142. Leenders. J.K. (2006). "Wind erosion control with scattered vegetation in the Sahelian Zone of Burkina Faso". (Doctoral dissertation), Wageningen University and Research Centre, Wageningen, Nederland. Poesen. J, Lavee. H. (1994). "Rock fragments in top soils: significance and processes". Catena, 23, 1-28.Shao, Y. (2000). Physics and Modelling of Wind Erosion. Kluwer Academic Publishers, Dordrecht.Webb, N.P., Strong, C.L. (2011). Soil erodibility dynamics and its representation for wind erosion and dust emission models. Aeolian Research, doi:10.1016/j.aeolia.2011.03.002
Contact informationEdri, A1.: [email protected], Katra, I1.: [email protected], Avraham, D2.: [email protected] of Geography and Environmental Development, Ben-Gurion University of the Negev2Environmental Research Unit, Negev Nuclear Research Center
IntroductionAeolian soil erosion is common in arid and semi-arid areas. This process impacts the environment including soil degradation and air pollution. Basic surface covers such as soil crusts, rock fragments and spare vegetation influence the aeolian erosion in arid areas (Belnap & Gillette, 1998; Leenders, 2006; Poesen & Lavee, 1994). Integration of topsoil analyses with aeolian field experiments is needed to develop more accurate models of soil erosion from a variety of surfaces (Shao, 2008; Webb and Strong, 2011). The aim of this study is to quantify aeolian erosion in silty-sand soil in an arid region in various surfaces, in two states- native and disturbed soils, using a portable wind tunnel for filed experiments and analysis of topsoil samples in the laboratory.
Study AreaThe experiment is conducted in the Yemin Plain, located in the Northeast of the northern Negev. The area was composed of three main plots: sparse vegetation (SV), rock fragments (RF) and mechanical crust (MC) (Fig.1). The research area is mainly flat and about 400 m above sea level. The average summer temperature is 26°C compared with an average winter temperature of 13°C. Annual average rainfall is 80 mm/year, with large seasonal variations.
MCSV RF
Fig. 1. Selected representative surfaces in the study area: sparse vegetation (SV), rock fragments (RF), and mechanical crust (MC) Aeolian SimulationsExperiments to determine dust emission potential were conducted using a portable wind tunnel which was designed and built at Ben-Gurion University of the Negev (Fig. 2). The development of the tunnel placed an emphasis on the aerodynamic structure to create a natural airflow profile. The experiments were conducted in the tunnel at air velocity of 6 and 11 m/sec. Suspended particles were measured using a particulate matter (PM) sensor (EPAM) and sand transport was recorded by the Sensit. Dust samples were collected during the experiment for laboratory analyses. Two experiments were conducted under native soil conditions and following intentional destruction of the surface structure/aggregation. In parallel to the aeolian measurements, the plots were characterized by analyzing topsoil samples in the field and in the laboratory.
ResultsAnalysis of the top soil particle size shows tri- model distribution for all research plots (Fig.3), significant differences (p≤0.05) were found between plots RF and MC to SV. Fig 4 shows total sediment flux in a vertical plan. For natural condition at wind velocity of 6 m/sec, plot RF showed the highest flux at 8.5 and 14 times higher than plots SV and MC respectively. Surface destruction and higher wind velocity (11 m/sec) caused increasing flux in all plots, with highest rates in plot MC. PM10 flux in all experimental plots demonstrated a similar temporal pattern (Fig. 5). Comparisons between the plots revealed that plot MC lost the most PM10 under natural conditions. For disturbed conditions, both plots RF (at 6m/sec) and SV (at 11m/sec) demonstrated the highest PM10 loss .Horizontal sand flux (100µm<) is presented in Fig. 6. At wind velocity of 6m/sec plot MC demonstrated the highest sand loss for both surface conditions.
Fig. 2. The BGU portable wind tunnel. In the left, the segments of the tunnel are presented in the air-suck configuration. In the right, instruments installed in the test section of the wind tunnel, including a digital small-vane probe system for wind speeds, piezo-electric sensor (Sensit) for sand flux, a real-time isokinetic dust monitor (EPAM 5000) for TSP, and PM 10 concentrations, and dust samplers (total sediments) in a vertical profile.
Fig. 5. PM10 flux measured in the wind tunnel during the experiments in the different plots (SV, RF, MC) for both topsoil states, native disturbed at wind velocity of 11 m/sec.
Fig. 6. Horizontal fluxes of sand during experiments in different plots (SV, RF, MC), for both native and disturbed conditions at wind velocity of 11 m/sec.
Fig. 4. Total aeolian sediment in a vertical profile (Z1= 3 cm, Z2= 8 cm, Z3= 15 cm, Z4= 35 cm) during tunnel experiments at wind speed of 11 m/sec for different plots at natural condition.
Fig. 3. Average particle size distribution for the three surface types, received by the laser diffraction technique (ANALYSSETE 22).
Wind Speed (m/s)
ConditionPlot
Total Aeolian FluxTotal PM10 FluxTotal Sand Flux
Normalized Value
SpringSummerSpringSummerSpringSummer
6
NaturalSV0.120.890.310.080.170.17RF10.990.990.590.510.28MC0.0711111
DisturbSV0.080.530.080.120.320.14RF10.310.4810.710.17MC0.16110.9611
11
NaturalSV0.350.520.150.1711RF0.050.490.190.420.380.29MC11110.830.36
DisturbSV0.510.750.090.0211RF0.060.44110.410.24MC110.970.540.840.64
ConclusionSignificant differences in soil loss were found between natural and disturbed soils. However, increase of wind velocity was the most decisive factor affecting emission in all plots. The soil water content was found to be the main soil factor reducing PM10 emission. Increasing saltation flux encourages PM10 emission in all the experimental plots, especially at wind velocity of 11m/s (Fig. 7). Plot MC was revealed as more erodible compared to plots SV and RF (Table 1). Further research will focus on integrated analysis of top soil properties and aeolian erosion processes.
Table 1. Summary of normalized values for the two seasons of the experiment, were calculated based on the results of soil loss flux calculations obtained from measurements in the wind tunnel.
Fig. 7. Correlation between sand flux to PM10 flux for both wind velocity and surface condition.
10m
•Wind speed profile
•TSP
•Suspended dust
•Sand flux
0.5m
0.5m
Negev Nuclear Research CenterIsrael Science Foundation