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Ground-surface conditions of sand-dust event occurrences in the southern Junggar Basin of Xinjiang, China
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Journal of Arid Environments 70 (2007) 49–62
Journal of Arid Environments www.elsevier.com/locate/jaridenv
Ground-surface conditions of sand-dust event occurrences in the southern Junggar Basin of Xinjiang, China Y.-B. Qiana,, Z.-N. Wub, Q. Yangc, L.-Y. Zhanga, X.-Y. Wangb a
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China b College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China c Institute of Desert and Meteorology, China Meteorological Administration, Urumqi 830002, China Received 17 April 2006; received in revised form 6 October 2006; accepted 1 December 2006 Available online 10 January 2007
Abstract The southern Junggar Basin of Xinjiang is one of the important source regions of sand-dust events in China. During the springtime periods from 2001 to 2004 when frequent sand-dust events can occur, the landform, soil, vegetation and impact of human activities were investigated, and soil and vegetation data collected. The physical and chemical properties of the soil samples were analyzed, and the characteristic indices of the vegetations were calculated. The vegetation cover, community biodiversity, degree of ecological dominance, topsoil water-content, soil organic matter, soil texture, soil salts and pH were chosen as the ground-surface variables most likely to affect the process of sand-dust event occurrence. With canonical discriminant analysis (CDA) using the SPSS10.0 software package, the study effectively discriminated the ground-surface characteristics of the study regions, which were (1) the Aibi Lake region with high-frequency sand-dust events and (2) the Gurbantunggut Desert with medium-frequency sand-dust events. The results show that in the Aibi Lake region, where the gray-brown desert soil and gray desert soil are widely distributed and where agricultural development is intensive, the main factors that negatively affect ground surface stability are the high soil surface pH, low soil organic matter contents and the high degree of ecological dominance (low biodiversity) of the vegetation. In the Gurbantunggut Desert, where stable and semistable aeolian sandy soils are distributed and are less disturbed by human exploitation, the erosion resistance of its topsoil benefits from the high vegetation cover, plant community diversity and coarser soil texture. The discriminant results also show that the agricultural development region in Kelamayi, with a large area of cleared land that previously belonged to a region of low-frequency Corresponding author. Tel.: +86 991 7885411; fax: +86 991 7885320.
E-mail address: [email protected] (Y.-B. Qian). 0140-1963/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2006.12.001
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sand-dust event, now has the ground surface characteristics of the regions with high-frequency sanddust event occurrences. The study demonstrates the likelihood of increasing soil erosion and sand and dust storms in the Kelamayi area. The analysis indicates that the increased risk of wind erosion in the Kelamayi area could be reduced by strategic planting of forest shelter belts to reduce the size of sections of cleared land that are unprotected. r 2006 Elsevier Ltd. All rights reserved. Keywords: Discriminant analysis; Ground-surface conditions; Sand-dust event; The southern Junggar Basin of China
1. Introduction Sand-dust events are catastrophic weather phenomena that frequently occur in arid and semiarid areas with enormous effects on eco-environments and human health (Qian et al., 2002; Shi and Zhao, 2003). Their occurrence, development and transport are related to the dynamics of weather and climate patterns at a range of different scales (Chang et al., 1996; Qiu et al., 2001; Tao et al., 2004). Many researchers have paid a great deal of attention to the synoptic and climatic inducements of sand-dust event occurrences (Qian et al., 2002; In and Park, 2002; Natsagdori et al., 2003; Shi and Zhao, 2003; Wang et al., 2003; Orlovsky et al., 2005; Mao et al., 2005). However, the meteorological statistics of some important source regions for sand-dust events in China showed that the frequencies of sand-dust events were not always significantly correlated with the synoptic and climatic parameters (Hu et al., 2001; He et al., 2003; Liu et al., 2003; Wang et al., 2004; Qian et al., 2004a). This has encouraged scientists to consider the influence of other factors while studying the meteorological causes of sand-dust events. Wang et al. (2001) indicated that a sand-dust event needs a strong wind, an abundant sand-dust source and unstable atmospheric stability. Lin et al. (1999) understood that the ground-surface characteristics of the regions prone to sand-dust events were related to vegetation cover, soil texture and the proportion occupied by bare land. Shi and Zhao (2003), while presenting a forecast system of sanddust events, took soil types, vegetation characteristics, land-use types and topsoil watercontents as important parameters in building a blowing sand model. Furthermore, Qian et al. (2004a, b) noticed that a large area of bare farmland possessed a great potential to generate sand-dusts during spring. In fact, the formation of sand-dust events is not restricted by geographic environment, weather and climatic conditions and vegetation state, but is also closely related to the erosion-resistance of top soils. Many factors, such as vegetation (Wiggs et al., 1995; Lancaster, 1998; Zhang et al., 2004), soil crust (Li et al., 2001; Eldridge and Leys, 2003; Hupy, 2004), topsoil water content (Liu et al., 2003; Wiggs et al., 2004), aerodynamic roughness of cultivated topsoil (Zhang et al., 2002), soil texture (Zobeck, 1991; Dong and Li, 1998), organic matter (Chepil, 1954; Zobeck, 1991; Li et al., 2001; Eldridge and Leys, 2003), and other physical and chemical properties of soil can control soil erosion. All the above-mentioned factors can affect the sand-dust transport process of a source region during a sand-dust event, but no single factor can completely explain the ground-surface conditions of a sand-dust event process. Few scientists have attempted to quantify the complex relationship between ground-surface conditions and sand-dust events. So this research field in Xinjiang, which is an important source region of the sand-dust events in Asia, is still largely unexplored. Therefore, during the spring seasons of frequent sand-dust events, the ground-surface vegetation and soil physical and
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chemical properties of the source areas with the different frequencies of sand-dust events in the southern Junggar Basin were systematically investigated and analyzed. Then, by CDA, these ground surface factors related to sand-dust events were constituted into linearly comprehensive indices, and quantified to provide references for a predictive mechanism of sand-dust events. 2. Study area and method 2.1. Study area The Junggar Basin, located in the center of the Eurasian continent and surrounded by mountains, has a typical temperate continental arid and semi-arid climate. On the Quaternary alluvial-fluvial plain, whose southern part abuts the Tianshan Mountains, the landscapes of the Gobi desert, oasis and sand desert are developed. This region is one of the main sources of sand-dust events in China (Qiu et al., 2001; Wang et al., 2003; Qian et al., 2004b). Weather patterns and dust generation are controlled chiefly by westerly air currents. For example, the sand-dust storm event of 18 April 1998, was captured on the satellite images provided by the SeaWiFS project, NASA/Goddard Space Flight Center, and showed that the sand-dust cloud originated from this region, passed across Japan and traveled as far as North America (He et al., 2001). Three typical regions were selected for this study (Fig. 1), because of their different sanddust storm frequencies. These regions were the Aibi Lake region, the agricultural development region of Kelamayi, and the Gurbantunggut Desert. The Aibi Lake region, located in the southwestern part of the Junggar Basin, is a graben basin, surrounded by mountains in the north, south and west. The east of the basin is joined to the Gurbantunggut Desert. The Lake region exhibits a typical temperate continental arid climate, characterized by low rainfall (the annual average rainfall from 84°
86°
88°
46°
44°
1 Mountains
2 Sandy desert
3 Sampling site
Fig. 1. Sketch map of the study regions and sampling sites.
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1961 to 2001 is 103.2 mm in Jinghe County), high evaporation (1722 mm) and abundant solar energy. The Arla Mountain pass to its west is a main wind pass through which cold air (the northwesterly winds) intrudes. In Jinghe County, located down wind of the Aibi Lake region, gales with wind velocities X17 m s1 occur statistically on 26.3 days per year and sand-dust events occur on 49.2 days per year, based on data from 1961 to 2001. The agricultural development region of Kelamayi is located on the semi-desert plain of the southwestern part in the Junggar Basin. Its northern extent is near to Zhayier Mountain and the south is joined to the alluvial-fluvial plain of the lower reaches of the Manas River. This region is also characterized by abundant solar energy, low rainfall (109 mm), high evaporation (3545 mm) and a wide temperature range. In Kelamayi it is windy during the spring and summer seasons, and the gales blow for up to 67 days per year and sand-dust events occur on 5.3 days per year. The region has little surface and underground water resources. The Gurbantunggut Desert is located in the center of the semi-closed Junggar Basin with abundant solar energy and high evaporation (2000–2800 mm), but low precipitation (80–160 mm). In the southern part of the desert, the gales occur on average 15.2 days per year, and the sand-dust events on 19.3 days per year. There is almost no runoff, and underground water resources are very deep in this desert.
2.2. Methods Field investigations and sampling were conducted in three regions mentioned above during the April springtime periods of frequent sand-dust events, from 2001 to 2004. In particular data were collected on the day of the sand-dust event on 19 April of 2004, and on the following day. Twenty-seven representative sites were selected for the discriminant analysis (Fig. 1), and located by GPS. At each site 1–4 quadrats were used to collect the characteristic parameters of plant distribution, using quadrats of 10 10 m for communities of shrubs, 20 20 m for communities of low trees, and 3 quadrats, each of 1 1 m for herbs. The ‘herb’ quadrats were arranged along a diagonal, nested within the shrub and low tree quadrats. The species presence, frequency and abundance (herbs), vegetation cover, plant height and crown diameter were recorded. Within each quadrat, soil samples from the surface layer (0–10 cm) were collected for physical and chemical analyses. Topsoil water-content (SM), texture, organic matter (OM), nutrients, salts (TS), and pH were measured using routine methods (Nanjing Institute of Petrology, 1978). The particle sizes of the soil samples were determined by Malvern Laser Analyzer, and the parameters were computed based on the formula of Folk and Ward (Shanbei Team of Chengdu Institute of Geology, 1976). Since we could not accurately determine what kind of influences the ground-surface conditions of a source region were having on sand-dust event occurrence using physical experiments and mathematical simulation, CDA (that analyzes the relationship between ground-surface conditions and sand-dust events), was used, based on observational results and professional knowledge. A large number of mathematical statistics for plant distribution parameters and CDA of ground-surface characteristics for these sand-dust source regions were processed by Excel Spreadsheets and the SPSS10.0 software package. Meteorological data from the Xinjiang Meteorological Bureau were used in the analyses.
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3. Results and analyses 3.1. Sand-dust events and landscape characteristics According to the frequencies of the mean annual sand-dust events (Table 1), Jinghe County, as a representative of the Aibi Lake region, has a high frequency of sand-dust events, the Gurbantunggut Desert has a medium frequency of sand-dust events, and the agricultural development region of Kelamayi has a low frequency of sand-dust events. The Aibi Lake region exhibits the strongest and most frequent sand-dust events of the three regions. In Jinghe County drifting dust is the dominant form of sand-dust event. In the Gurbantunggut Desert the dominant form of event is blowing sand, and in Kelamayi the frequencies of the different kinds of sand-dust events are almost equal. The types and frequencies of the sand-dust events do not correlate well with the number of days of gales (Table 1). However, the different landscape types (Table 2) appeared to influence the frequency of sand-dust events. On the alluvial-fluvial plain of the Aibi Lake region, with fine gray-brown desert soil and gray desert soil, the agricultural exploitation is intensive, and the area of bare land cultivated in spring seasons continues to expand. These bare, cultivated lands as well as the dry clay lakebed are the sources of frequent sand-dust events. The contrasts with the lower sand-dust events of the Gurbantunggut Desert, where coarser, stable and semi-stable aeolian sandy soils are absolutely dominant and exhibit surface crusts, and where there is a high percentage of vegetation cover with good biodiversity. Before the land in the agricultural region in Kelamayi was developed, it had a strong erosion resistance with a high vegetation cover and compacted soil. Although the Kelamayi region has a large number of gale-days, sand-dust events did not often occur. According to the researches of Arya (cited by Liu and Dong, 2003) and Jia et al. (1999) a complex aerodynamic surface roughness at a landscape scale (Z0/Z0ef) influences the frequency and strength of sand-dust events. Such complex surface roughness is achieved by the amount of ground-surface vegetation cover and its ‘patchiness’—the distribution and distance between those patches of vegetation that create the roughness effect. Information from sand-dust events shows that the amount of dust generation is not related only to the area of bare lands (such as in the bare spring farmlands in the study regions), nor to only desert lands with low vegetation covers, but to the distribution of the vegetation. In our investigations, we observed that if the bare land or desert land with low vegetation cover (Co) was present as several patches in the landscape or is divided by shelter belt forestry around farmland planted to block the chief wind direction, then the ground-surface roughness can be increased. That is, the larger the separation or fragmentation of bare Table 1 Types and frequencies of sand-dust events (d a1, 1961–2001)
Gale (X17 m s1) Drifting dust Blowing sand Sand-dust storm Total
Aibi Lake region (Jinghe County)
Southern Gurbantunggut desert (Caijiahu)
Kelamayi
26.3 28.5 16.3 4.4 49.2
15.2 3.0 12.7 3.6 19.3
67.0 2.0 2.1 1.2 5.3
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Table 2 Main types of landforms, vegetation covers and soils Regions
Number of samples
Geomorphologic landscape
Types and covers of vegetation
Types of soils
Aibi Lake region
27
Alluvial/fluvial fan
Kochia prostrata and Artemisia spp. community and Haloxyon ammodendron community, vegetation cover of 10–15% Seriphidium borotalense and ephemeral plants community, vegetation cover of 15% Limonium suffruticosum, Halocnemum strobilaceum and Kalidium capsicum community, vegetation cover of about 30%; Nitraria sibirica sand dune and Tamarix sand dune, vegetation cover of 7–50% Tamarix sand dune, vegetation cover of 7%; Haloxyon persicum community, vegetation cover of 20% Halostachys caspica, Phragmites communis community, vegetation cover of over 70%; H. persicum and Calligonum leucocladum community, vegetation cover of over 30%; Seriphidium santolinum and ephemeral plants community, vegetation cover of about 35% Bare cultivated land
Gray-brown desert soil
Alluvial plain
Lakeshore/dried lakebed, shrub sand dunes
Mobile/semi-mobile sand dunes
Lands of interdunes
New cultivated land on alluvial plain New cultivated land on interdunes Oasis cultivated land Gurbantunggut 21 desert
Bare cultivated land, original vegetation is Phragmites communis meadow Bare cultivated land
Fluvial-alluvial plain Ceratoides lateens, Salsola in the northern desert arbuscula and Stipa glareosa, vegetation cover of over 45% Great longitudinal H. persicum, Ephedra distachya, dunes in the northern C. leucocladum and herb plants, desert vegetation cover of about 25–35% Complex longitudinal E. distachya, Seriphidium dunes santolina and ephemeral plants community, vegetation cover of about 40–60% Mobile belt on the top H. persicum, Artemisia arenaria of sanddunes and ephemeral plants community, shrub cover over 30%
Gray desert soil
Solonchak
Aeolian sandy soil
Saline soil, aeolian sandy soil, gray desert soil
Gray desert soil Meadow soil
Irrigated-cultivated soil Gray-brown desert soil
Aeolian sandy soil
Aeolian sandy soil
Aeolian sandy soil
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Table 2 (continued ) Regions
Number of samples
Geomorphologic landscape
Types and covers of vegetation
H. persicum and ephemeral plants community, vegetation cover of about 35–70% H. ammodendron, Reaumuria soongorica and annual halophytes, vegetation cover of 30–80% Lands of interdunes E. distachya and ephemeral plants community, vegetation cover of over 30%, soil crust developed Alluvial-fluvial plain in H. ammodendron and Reaumuria the northern desert soongorica, vegetation cover of 32%
Aeolian sandy soil
New cultivated land
Residual meadow soil/ Salinized dry boggy soil Dry boggy soil
Stable sanddunes of checkerboard-shaped form Transitional belt between desert and oasis
Kelamayi
14
Types of soils
Bare cultivated land
H. ammodendron community, vegetation cover of about 45% Lands of interdunes H. ammodendron, Nitraria sibirica, R. soongorica and ephemeral plants community, vegetation cover of over 40% Land eroded by wind Anabasis aphylla community, vegetation cover of about 35% Mobile sanddunes No vegetation Alluvial-fluvial plain
Salinized gray desert soil
Aeolian sandy soil
Salinized gray desert soil
Solonchak with surface sandy soil
Calcium-accumulated soil of dry salt basin Aeolian sandy soil
cultivated lands and desert lands with low vegetation covers are, the more difficult it is for sand-dust events to occur. Zhou (2003) analyzed the landscape pattern of the Jinghe Basin using the data of Landsat TM, and calculated that the landscape separation of the grasslands and desert lands with low vegetation covers was 0.468 and that for the cultivated lands was 1.761, and the total landscape fragmentation was 0.371. This indicated that the cover for both the desert land with low vegetation in its natural state, and for the broad scale cultivated land (bare land in spring) was not high. This landscape characteristic showed that the aerodynamic roughness coefficient, Z0ef, applied over a large area was falling, providing an underlying surface condition for increasing blowing sand in the Aibi Lake region. A shelter forest belt of 200 m width and 25 km length has been established at the northwestern and southwestern sides of 180 km2 of cleared land in the agricultural development region of Kelamayi. With the addition of an incomplete shelter forest network for farmlands and some woodlands for timber production, the ratio of woodland cover to cleared land reaches 34.2%. However, because these woodlands are planted in large blocks, they could not effectively divide the cultivated land and increase its fragmentation. Hence the aerodynamic roughness of the local underlying surface, Z0, was not increased.
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Stable and semi-stable sand dunes dominate the mid-southern Gurbantunggut Desert, and bare lands without vegetation cover are only found on the tops of mobile sand dunes in some sections of limited area. Although the vegetation covers among the microlandforms exhibit some differences, the macro-distribution of the vegetation still shows a high homogeneity. Differing from bare cultivated lands and desert lands with low vegetation cover, areas with patterns of small separation between vegetation, or with fragmentation of sandy lands with medium-high vegetation covers (over 20%) have a higher chance of decreasing outbreaks of drifting sand, due to their higher regional Z0. 3.2. Discrimination to ground-surface characteristics of sand-dust events In this discriminant analysis, the characteristics of vegetation structure, the internal factors of soil erodibility, and topsoil water-content were taken as chief ground-surface parameters that affect the process of blowing sand and dust. According to previous studies, P 3 indices were chosen: (1) Simpson ecological dominance C, where, C ¼ ðn2i =N 2 Þ. In the formula ni is the importance value of i species and N is the summation of all species’ importance values community; (2) Herbert diversity index Pin the PIE, where PIE ¼ N=ðN 1Þ 1 ð1=N 2 Þ n2i . In this formula ni is the number of i species and N is the summation of all species’ numbers in the community; and (3) vegetation cover describing the composition and structure of vegetation communities (Qian et al., 2004c). The factors influencing the soil’s internal stability include the mean particle size (Mz) and sorting index (s), (reflecting soil texture), and soil organic matter, total salts and pH. These nine variables were used to discriminate the ground-surface conditions susceptible to sand-dust event occurrence (Table 3). In the total data sets from the field investigations, several quadrats that were affected by other occasional factors were eliminated. Then, based on previous researches (Qian et al., 2004a, c), bare farmlands and desert lands of low vegetation cover in the region with high frequent sand-dust events, and the lands of sand dunes in the region with medium frequency sand-dust events were selected as the sampling sites, and 27 quadrats were used as the representatives of the regions for the discriminating analysis. Through CDA, the ground-surface conditions of the Aibi Lake region (with highfrequency storm-dust events) and the Gurbantunggut Desert (with low-frequency stormdust events) were compared to identify disadvantageous or advantageous threshold values of ground-surface conditions. Because a large area of the agricultural development in Kelamayi started in 2001 and disturbed the original ground-surface conditions, this region was used to compare actual conditions with those predicted by CDA. The canonical discriminant function coefficient was compared with the original data and the results of statistical tests are given below (Table 4). The w2 test is significant (Po0:001) and 100% of original grouped cases were correctly classified, showing that this canonical discriminant functions is effective. P The formula of discriminant function from original data: F ¼ ki¼1 ai xi þ Co: In the formula, F is the discriminant function, ai the coefficient of discriminant function, xi a variable, Co a constant term of the discriminant function, and k the number of variables (k ¼ 9). Here, the threshold value, F, between the regions of high- and lowfrequent storm-dust events is zero, that is, the region of F40 has the ground-surface conditions of high-frequency sand-dust event occurrences, and the region of Fo0 has that of low-frequency sand-dust event occurrences (Fig. 2).
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Table 3 The parameters for describing the ground-surface characteristics Site
PIE
C
Vegetation cover
Topsoil water-content
Mz
s
Organic matter
Soil salts
pH
F
04A6-1 04A6-2 04A9-1 04A9-2 04A9-3 04A10-2 04A11-1 04A11-2 04A11-3
0.70 0.00 0.02 0.30 0.00 0.60 0.65 0.53 0.00
0.33 0.00 0.59 0.50 0.00 0.49 0.51 0.44 0.00
16.09 0.00 20.80 73.03 0.00 25.14 21.13 20.01 0.00
1.73 3.27 0.50 1.58 8.64 0.42 0.32 0.32 0.96
0.05 0.03 0.28 0.09 0.06 0.08 0.14 0.15 0.03
2.84 2.47 0.87 1.59 1.82 2.03 0.46 0.57 1.97
0.54 0.72 0.09 0.55 1.10 0.07 0.13 0.13 2.04
0.06 0.11 0.14 1.69 1.94 0.06 0.06 0.05 0.11
9.23 9.86 9.10 9.08 9.08 9.33 9.61 8.38 8.54
2.07 3.02 2.90 2.83 2.04 2.42 2.58 0.42 2.71
02G12 02G15 03G6-1 03G6-2 03G6-3 03G10-1 03G10-2 03G10-3 03G10-4 03G3-1 03G3-2 03G3-3 03G3-4
0.38 0.75 0.66 0.71 0.68 0.47 0.51 0.70 0.73 0.73 0.46 0.65 0.56
0.16 0.16 0.20 0.15 0.12 0.30 0.25 0.20 0.12 0.32 0.18 0.17 0.18
18.67 29.80 27.72 53.87 63.74 31.83 35.03 58.55 23.53 26.49 27.14 35.72 90.52
2.45 3.97 1.08 1.22 0.72 1.21 0.55 1.28 0.71 2.03 2.36 2.22 2.71
0.20 0.12 0.15 0.16 0.19 0.13 0.15 0.19 0.16 0.18 0.18 0.25 0.21
0.32 0.57 0.73 0.63 0.60 1.11 0.72 0.54 0.74 1.89 1.54 1.44 1.52
0.04 0.18 0.16 0.10 0.11 0.14 0.12 0.05 0.09 0.13 0.39 0.11 0.19
0.02 0.03 0.03 0.02 0.02 0.03 0.03 0.01 0.02 0.03 0.04 0.02 0.02
7.69 8.10 8.33 7.69 8.27 8.08 8.29 7.46 7.56 8.30 8.31 7.40 7.50
2.37 1.89 1.35 3.09 2.49 0.67 0.92 3.38 3.35 0.88 0.78 3.52 3.09
K12 K13 K14-2 K15 K17
0.00 0.47 0.47 0.00 0.35
0.00 0.31 0.45 0.00 0.51
0.00 37.55 34.71 0.00 31.33
2.31 0.87 1.21 1.84 2.04
0.02 0.07 0.03 0.02 0.01
2.44 1.93 2.39 1.87 1.45
1.14 0.52 0.44 0.91 0.74
0.99 0.15 1.07 2.88 0.45
8.14 8.20 8.18 8.39 8.27
0.75 0.42 1.23 0.44 2.62
Table 4 Canonical discriminant function coefficients No. for variable (xi)
Discriminant function coefficient of original data (ai)
Contributing ratio of a variable in discriminant formula (%)
PIE C Vegetation cover Topsoil water content Mz s Organic matter Total salt pH Constant term
1.654 7.410 0.819 0.035 5.585 0.002 1.944 0.218 1.361 12.127
7.7 10.5 2.6 1.0 1.7 0.0 25.7 3.3 47.5
Wilks’ l ¼ 0:148, w2 ¼ 29:615, df ¼ 9, Sig. ¼ 0.001.
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58 5.00 4.00 3.00 2.00 F
1.00
Threhold value F = 0
0.00 -1.00 -2.00 -3.00
04
A 6 04 -1 A 6 04 -2 A 9 04 -1 A 9 04 -2 A 04 9-3 A 1 04 0-2 A 1 04 1-1 A 1 04 1-2 A 1 03 1-3 G 3 03 -1 G 3 03 -2 G 3 03 -3 G 3 03 -4 G 6 03 -1 G 6 03 -2 G 6 02 -3 G 1 02 2 G 03 15 G 1 03 0-1 G 1 03 0-2 G 10 03 -3 G 10 K -4 12 K 13 K 14 -2 K 15 K 17
-4.00
Sites
Fig. 2. Distribution of discriminant values of the ground-surface conditions.
The discriminant results for the agricultural development region of Kelamayi show that its ground-surface conditions represent the characteristics of a region with high frequency sand-dust events (Fig. 2). This indicates the danger of future deterioration of this environment. 3.3. Erosion-resistance action and discriminant significance of ground-surface parameters The results of CDA about the ground-surface conditions of Aibi Lake region (with highfrequency sand-dust events) and the Gurbantunggut Desert (with low-frequency sand-dust events) indicated that the region of F40 has the ground-surface conditions of high frequency sand-dust event occurrences, and the region of Fo0 has that of low-frequency sand-dust event occurrences. The parameters with positive coefficients in the discriminant function made F40, so, in primary data presented in this paper (Table 3), they were negative factors for ground-surface stability and favored the occurrence of sand-dust events, whereas the parameters with negative coefficients made Fo0 and have positive effects on ground-surface stability. According to the negative and positive values of the corresponding coefficients and the small and large values of the contributing ratios for these parameters in the discriminant function, the character and weakness and strength of these parameters in predicting ground-surface stability can be determined. The results show that the parameters with negative effects are the ecological dominance of vegetation (C), the organic matter, soil salinity and soil pH (Table 4). Among these, the discriminant meaning of pH values is significant, and the contributing ratio is up to 47%. The values of soil pH are affected by many factors (Qi, 1994; Dai, 1997), particularly the soil content of carbonate ions (mainly CO2 3 /HCO3 ), organic matter and the level of salts in the soil. In a previous study (Qian et al., 2004d), it was noticed that a significant correlation existed 2 between pH, salts (Ca2+, Na+, HCO 3 , CO3 ) and soil organic matter. The values of soil pH from the Aibi Like region showed a direct correlation to the content of CO2 3 (R ¼ 0:76, P40.01). The soil pH of the agricultural development region in Kelamayi was negatively correlated with soil organic matter (Qian et al., 2004d), and according to experimental data from the Xinjiang University of Agriculture (Qian et al., 2003), the pH
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value is closely related to the soil HCO 3 content in this region. Since high soil pH values in these regions correlated with high soil carbonate levels, it is likely that the sandy soil stability in this region is low, similar to the findings of Chepil (1952) and Singer et al. (2003). Ecological dominance of vegetation, with a contributing ratio of 10.5% is also a negative factor, whose influence on ground-surface stability is obvious. In the study regions, the ecological dominance of vegetation is generally high because of the farmland with a single species planting structure and the natural desert land with a low vegetation cover of a single species of shrub or semi-shrub. This lack of biodiversity greatly weakens the erosion-resistance of the topsoil. Soil organic matter content proved to be an important parameter with a significant discriminant effect (contributing ratio ¼ 25.7%). It has both positive and negative effects on the erosion-resistance of soil. Generally, if high organic matter in the topsoil is caused by a well-developed bio-crust or by surface litter, this will undoubtedly increase the erosion-resistance of soil (Li et al., 2001; Eldridge and Leys, 2003). However, if the organic matter in the soil is decomposed, this will make the soil susceptible to erosion (Chepil, 1954; Singer et al., 2003). The type of organic matter existing in these regions is not clear, so the discriminant meaning is a statistical result. A high content of topsoil salts (TS) in the study regions is usually a negative factor because it affects plant growth and adds caustic salts to the material blowing across the soil surface (Qian et al., 2004a). However, Singer et al. (2003) noticed that the dust generation potential of fine silt soil, PM10 containing a high salt content was low when he studied the southern Aral Sea. The reason for this was that salt crystals and fine soil particles form into a stable crust preventing the soil from erosion. The important point here is that erosionresistance of salty soil crusts are sensitive to soil particle sizes. The salty crust of a sandy soil or sandy loam is easily broken so that the soil generates dust (Singer et al., 2003). The integrated effect of salt and soil particle size makes the contributing value of the soil salt-content only 3.3%. The main factors for strengthening the stability of surface soils are their vegetation cover, plant biodiversity and mean soil particle size (Table 4). Vegetation cover and biodiversity increase the aerodynamic roughness Z0 and reduce the wind speed at the ground surface (Zhang et al., 2003). Furthermore, their plant litter can cover the soil surface to avoid wind-erosion (Wiggs et al., 1995; Eldridge and Leys, 2003). Comparison of these two parameters, cover and biodiversity, shows that the plant biodiversity is the more important with a contribution ratio of 7.7%. Diverse herb and woody structures in multi-layers decreases the aerodynamic porosity of vegetation and further reduces the flux of sand-dust (Lancaster, 1998). The soil texture (indicated by mean particle size (Mz)) influences soil stability and has an important effect on discriminating the topsoil erodibility with a contributing ratio of 1.7%. According to the observation, a soil with a high Mz has a strong resistance to wind erosion, whereas areas with top soils of fine silt and clay can be the greatest sources for dust events. Orlovsky et al. (2005) and Singer et al. (2003) presented this result when they studied the Aral Sea of central Asia, which exhibits similar environmental conditions to the Aibi Lake region, and Liu and Dong (2003) also proved the effect of soil texture on wind erosion in a wind tunnel experiment. Many researchers conclude that topsoil water content is the most positive factor in controlling soil erodibility (Wang et al., 2001). However, Wiggs et al. (2004) showed that at certain wind speeds a lower topsoil water content (o4–6%) does not exhibit significantly worse soil erodibility than a soil with a higher soil moisture content. However, since the
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study regions are arid to semi-arid, and the topsoil in the spring seasons of frequent gales is especially dry (with soil water content commonly less than 2%), the question about the impact of the topsoil moisture on sand-dust events in spring seasons in the study regions is largely irrelevant, with a contributing ratio of only 1%. 4. Conclusions The vegetation cover, community biodiversity, degree of ecological dominance, topsoil moisture, soil organic matter, soil texture, soil salts and pH constitute the discriminant factors that are the ground-surface variables impacting the process of wind erosion and blowing sand and dust. These factors can effectively discriminate the differences between the Aibi Lake region with high-frequency sand-dust events and the Gurbantunggut Desert with medium-frequency sand-dust events during the spring seasons of frequent sand-dust event occurrences. In the Aibi Lake region, where gray-brown desert soil and gray desert soil are widely distributed and land clearing for agricultural development is intensive, the negative factors affecting ground-surface stability are mainly high soil pH, soil salinity, soil organic matter and the degree of ecological dominance in the vegetation. In the Gurbantunggut Desert, where stable and semi-stable aeolian sandy soils are dominant and are less disturbed by human exploitation, the erosion-resistance of its topsoil benefits from the higher vegetation cover, plant community diversity and coarser texture. The results of the CDA also show the agricultural development region of Kelamayi has the ground-surface characteristics similar to the regions with high-frequency sand-dust events. The results of this study indicate that the Kelamayi development area is under threat of serious wind erosion resulting from the pattern of land clearing and development. However, the risk of damaging wind erosion can be reduced by returning some cultivated land to grassland or woodland, and by rationally developing a network of forest shelterbelts throughout the farmland to increase the landscape fragmentation. Such fragmentation of cultivated land (or desert land with low vegetation cover) can raise the aerodynamic roughness of the ground surface, thereby reducing wind speed and the probability of sand-dust event occurrences. Acknowledgments This research was supported by the Xinjiang Uygur Autonomous Region’s Natural Science Foundation Project and the especial support project from the Director’s fund of the Xinjiang Institute of Ecology and Geology. We are grateful to Mr. Adrian Williams for reviewing the English text and to the editor and anonymous referee for improving the manuscript. References Chang, Y.S., Arndt, R.L., Carmichael, G.R., 1996. Mineral base-cation deposition in Asia. Atmospheric Environment 30, 2417–2427. Chepil, W.S., 1952. Factors that influence clod structure and erodibility of soil by wind: I. soil structure. Soil Science 75, 473–483. Chepil, W.S., 1954. Factors that influence clod structure and erodibility of soil by wind: III. calcium carbonate and decomposed organic material. Soil Science 77, 473–480.
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Dai, S.G., 1997. Environmental Chemistry. Higher Education Press, Beijing, pp. 211–214. Dong, Z.B., Li, Z.S., 1998. Wind erodibility of aeolian sand as influenced by particle size parameters. Journal of Soil Erosion and Soil Water Conservation 4 (4), 1–12. Eldridge, D.J., Leys, J.F., 2003. Exploring some relationships between biological soil crusts, soil aggregation and wind erosion. Journal of Arid Environment 53 (4), 457–466. He, Q., Cui, C.X., Lei, J.Q., et al., 2001. Case analysis on an intensely strong sandstorm on April 18th, 1998 in Xinjiang. Arid Land Geography 24 (Supp.), 54–61. He, Q., Yang, Q., Li, H.J., 2003. Variations of air temperature, precipitation and sand-dust weather in Xinjiang in past 40 years. Journal of Glaciology and Geocryology 25 (4), 423–427. Hu, L.Q., Xia, J.D., Sang, C.Q., 2001. Analysis of satellite images of strong sandstorm in Xinjiang. Arid Land Geography 24 (Supp.), 30–32. Hupy, J.P., 2004. Influence of vegetation cover and crust type on wind-blown sediment in a semi-arid climate. Journal of Arid Environment 58 (2), 167–179. In, H.J., Park, S.V., 2002. A simulation of long-range transport of Yellow Sand observed in April 1998 in Korea. Atmospheric Environment 36, 4173–4187. Jia, L., Wang, J.M., Menenti, M., 1999. Local and effective roughness under complex ground-surface conditions between oasis and desert. Acta Meteorologica Sinica 57 (3), 346–357. Lancaster, N.A., 1998. Influence of vegetation cover on sand transport by wind: field studies at Owens Lake, California. Earth Surface Processes and Landforms 23 (1), 69–82. Li, X.R., Jia, Y.K., Long, L.Q., et al., 2001. Advances in microbiotic soil crust research and its ecological significance in arid and semiarid regions. Journal of Desert Research 21 (1), 4–11. Lin, J., Sun, S.H., Wang, J.H., 1999. Evaluation methodology of land desertification level caused by wind-erosion in the sand-dust storm frequently-occurring area. Journal of Desert Research 19 (4), 375–377. Liu, X.P., Dong, Z.B., 2003. Review of aerodynamic roughness length. Journal of Desert Research 23 (4), 337–346. Liu, L.C., An, X.Q., Li, X.R., et al., 2003. Characteristics of sand-dust storm in Yanchi county, Ningxia province. Journal of Desert Research 23 (1), 33–37. Mao, R., Gong, D.Y., Fan, Y.D., 2005. Influences of synoptic variability on spring sand storm frequency in north China. Acta Geographica Sinica 60 (1), 12–20. Nanjing Institute of Petrology (Eds.), 1978. Physical–Chemical Analyses of Soil. Science and Technology Press, Shanghai, pp. 62–146, 196–233, 466, 481–489. Natsagdori, L., Jugder, D., Chung, Y.S., 2003. Analysis of dust storms observed in Mongolia during 1937–1999. Atmospheric Environment 37, 1401–1411. Orlovsky, L., Orlovsky, N., Durdyev, A., 2005. Dust storms in Turkmenistan. Journal of Arid Environment 60, 87–97. Qi, C.M., 1994. General Geochemistry. Geology Press, Beijing, p. 58. Qian, Y.B., Jiang, J., Wu, Z.N., et al., 2003. Soil physical–chemical properties, sustainable development and use of agricultural development area in Kelamayi. Journal of Soil and Water Conservation 17 (1), 50–53. Qian, Y.B., Wu, Z.N., Jiang, J., et al., 2004a. Eco-environmental change and its impact factors in the Aibi Lake catchment during the past 50 years. Journal of Glaciology and Geocryology 26 (1), 17–26. Qian, Y.B., Wu, Z.N., Zhang, L.Y., et al., 2004b. Ground-surface conditions resulting in sand-dust storms in the south Junggar Basin. Arid Land Geography 27 (4), 540–542. Qian, Y.B., Wu, Z.N., Zhang, L.Y., et al., 2004c. Impact of habitat heterogeneity on plant community pattern in Gurbantunggut Desert. Journal of Geographical Sciences 14 (4), 447–455. Qian, Y.B., Zhou, H.R., Xu, M., et al., 2004d. Relationship between water–soil properties and desert plant diversities in agricultural development area of Kelamayi. Journal of Soil and Water Conservation 18 (2), 186–188. Qian, Z.A., Song, M.H., Li, W.Y., 2002. Analyses on distributive variation and forecast of sand-dust storms in recent 50 years in north China. Journal of Desert Research 22 (2), 106–111. Qiu, X.F., Zeng, Y., Miao, Q.L., 2001. Temporal-spatial distribution as well as tracks and source areas of sanddust storms in China. Acta Geographica Sinica 56 (3), 316–322. Shanbei Team of Chengdu Institute of Geology, (Eds.), 1976. Grain Size Analysis of the sedimentary rock (sediment) and its Application. Geology Press, Beijing, pp. 44–54, 66, 90. Shi, G.Y., Zhao, S.X., 2003. Several scientific issues of studies on the dust storms. Chinese Journal of Atmospheric Sciences 27 (4), 591–606.
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Singer, A., Zobeck, T., Poberezsky, L., et al., 2003. The PM10 and PM2.5 dust generation potential of soils/ sediments in the Southern Aral Sea Basin, Uzbekistan. Journal of Arid Environments 54, 705–728. Tao, J.H., Wang, J.S., Feng, J.Y., 2004. Applying of helicity in analysis of a severe sandstorm. Journal of Desert Research 24 (1), 83–87. Wang, T., Chen, G.T., Qian, Z.A., et al., 2001. Situation of sand-dust storms and countermeasures in north China. Journal of Desert Research 21 (4), 322–327. Wang, X., Ma, Y., Chen, H.W., 2003. Climatic characteristics of sandstorm in Xinjiang. Journal of Desert Research 23 (5), 147–151. Wang, X., Dong, Z., Zhang, J., et al., 2004. Modern dust storms in China: an overview. Journal of Arid Environment 58 (4), 559–574. Wiggs, G.F.S., Thomas, D.S.J., Bullard, J.E., 1995. Dune mobility and vegetation cover in the southwest Kalahari Desert. Earth Surface Processes and Landforms 20 (6), 515–529. Wiggs, G.F.S., Bullard, J.E., Atherton, R.J., 2004. The dynamic effects of moisture on the entrainment and transport of sand by wind. Geomorphology 59 (1–4), 13–30. Zhang, C.L., Zou, X.Y., Dong, G.R., et al., 2002. Aerodynamic roughness of cultivated soil and its influence on soil erosion by wind in a wind tunnel. Journal of Desert Research 22 (5), 473–475. Zhang, H., Li, F.R., Fu, Q.K., et al., 2004. Field investigation on ecological effect of windbreak and soil erosion reduction from sandy grasslands. Environmental Science 25 (2), 119–124. Zhou, C.H., 2003. Ecological environment domino effect made by nature resource exploiture of Aibi Basin— Jinghe basin as example. Master Thesis, Xinjiang, pp. 27–47. Zobeck, T.M., 1991. Soil properties affecting wind erosion. Soil and Water Conservation 46, 112–118.
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Journal of Arid Environments www.elsevier.com/locate/jaridenv
Ground-surface conditions of sand-dust event occurrences in the southern Junggar Basin of Xinjiang, China Y.-B. Qiana,, Z.-N. Wub, Q. Yangc, L.-Y. Zhanga, X.-Y. Wangb a
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China b College of Resources and Environment Sciences, Xinjiang University, Urumqi 830046, China c Institute of Desert and Meteorology, China Meteorological Administration, Urumqi 830002, China Received 17 April 2006; received in revised form 6 October 2006; accepted 1 December 2006 Available online 10 January 2007
Abstract The southern Junggar Basin of Xinjiang is one of the important source regions of sand-dust events in China. During the springtime periods from 2001 to 2004 when frequent sand-dust events can occur, the landform, soil, vegetation and impact of human activities were investigated, and soil and vegetation data collected. The physical and chemical properties of the soil samples were analyzed, and the characteristic indices of the vegetations were calculated. The vegetation cover, community biodiversity, degree of ecological dominance, topsoil water-content, soil organic matter, soil texture, soil salts and pH were chosen as the ground-surface variables most likely to affect the process of sand-dust event occurrence. With canonical discriminant analysis (CDA) using the SPSS10.0 software package, the study effectively discriminated the ground-surface characteristics of the study regions, which were (1) the Aibi Lake region with high-frequency sand-dust events and (2) the Gurbantunggut Desert with medium-frequency sand-dust events. The results show that in the Aibi Lake region, where the gray-brown desert soil and gray desert soil are widely distributed and where agricultural development is intensive, the main factors that negatively affect ground surface stability are the high soil surface pH, low soil organic matter contents and the high degree of ecological dominance (low biodiversity) of the vegetation. In the Gurbantunggut Desert, where stable and semistable aeolian sandy soils are distributed and are less disturbed by human exploitation, the erosion resistance of its topsoil benefits from the high vegetation cover, plant community diversity and coarser soil texture. The discriminant results also show that the agricultural development region in Kelamayi, with a large area of cleared land that previously belonged to a region of low-frequency Corresponding author. Tel.: +86 991 7885411; fax: +86 991 7885320.
E-mail address: [email protected] (Y.-B. Qian). 0140-1963/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaridenv.2006.12.001
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sand-dust event, now has the ground surface characteristics of the regions with high-frequency sanddust event occurrences. The study demonstrates the likelihood of increasing soil erosion and sand and dust storms in the Kelamayi area. The analysis indicates that the increased risk of wind erosion in the Kelamayi area could be reduced by strategic planting of forest shelter belts to reduce the size of sections of cleared land that are unprotected. r 2006 Elsevier Ltd. All rights reserved. Keywords: Discriminant analysis; Ground-surface conditions; Sand-dust event; The southern Junggar Basin of China
1. Introduction Sand-dust events are catastrophic weather phenomena that frequently occur in arid and semiarid areas with enormous effects on eco-environments and human health (Qian et al., 2002; Shi and Zhao, 2003). Their occurrence, development and transport are related to the dynamics of weather and climate patterns at a range of different scales (Chang et al., 1996; Qiu et al., 2001; Tao et al., 2004). Many researchers have paid a great deal of attention to the synoptic and climatic inducements of sand-dust event occurrences (Qian et al., 2002; In and Park, 2002; Natsagdori et al., 2003; Shi and Zhao, 2003; Wang et al., 2003; Orlovsky et al., 2005; Mao et al., 2005). However, the meteorological statistics of some important source regions for sand-dust events in China showed that the frequencies of sand-dust events were not always significantly correlated with the synoptic and climatic parameters (Hu et al., 2001; He et al., 2003; Liu et al., 2003; Wang et al., 2004; Qian et al., 2004a). This has encouraged scientists to consider the influence of other factors while studying the meteorological causes of sand-dust events. Wang et al. (2001) indicated that a sand-dust event needs a strong wind, an abundant sand-dust source and unstable atmospheric stability. Lin et al. (1999) understood that the ground-surface characteristics of the regions prone to sand-dust events were related to vegetation cover, soil texture and the proportion occupied by bare land. Shi and Zhao (2003), while presenting a forecast system of sanddust events, took soil types, vegetation characteristics, land-use types and topsoil watercontents as important parameters in building a blowing sand model. Furthermore, Qian et al. (2004a, b) noticed that a large area of bare farmland possessed a great potential to generate sand-dusts during spring. In fact, the formation of sand-dust events is not restricted by geographic environment, weather and climatic conditions and vegetation state, but is also closely related to the erosion-resistance of top soils. Many factors, such as vegetation (Wiggs et al., 1995; Lancaster, 1998; Zhang et al., 2004), soil crust (Li et al., 2001; Eldridge and Leys, 2003; Hupy, 2004), topsoil water content (Liu et al., 2003; Wiggs et al., 2004), aerodynamic roughness of cultivated topsoil (Zhang et al., 2002), soil texture (Zobeck, 1991; Dong and Li, 1998), organic matter (Chepil, 1954; Zobeck, 1991; Li et al., 2001; Eldridge and Leys, 2003), and other physical and chemical properties of soil can control soil erosion. All the above-mentioned factors can affect the sand-dust transport process of a source region during a sand-dust event, but no single factor can completely explain the ground-surface conditions of a sand-dust event process. Few scientists have attempted to quantify the complex relationship between ground-surface conditions and sand-dust events. So this research field in Xinjiang, which is an important source region of the sand-dust events in Asia, is still largely unexplored. Therefore, during the spring seasons of frequent sand-dust events, the ground-surface vegetation and soil physical and
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chemical properties of the source areas with the different frequencies of sand-dust events in the southern Junggar Basin were systematically investigated and analyzed. Then, by CDA, these ground surface factors related to sand-dust events were constituted into linearly comprehensive indices, and quantified to provide references for a predictive mechanism of sand-dust events. 2. Study area and method 2.1. Study area The Junggar Basin, located in the center of the Eurasian continent and surrounded by mountains, has a typical temperate continental arid and semi-arid climate. On the Quaternary alluvial-fluvial plain, whose southern part abuts the Tianshan Mountains, the landscapes of the Gobi desert, oasis and sand desert are developed. This region is one of the main sources of sand-dust events in China (Qiu et al., 2001; Wang et al., 2003; Qian et al., 2004b). Weather patterns and dust generation are controlled chiefly by westerly air currents. For example, the sand-dust storm event of 18 April 1998, was captured on the satellite images provided by the SeaWiFS project, NASA/Goddard Space Flight Center, and showed that the sand-dust cloud originated from this region, passed across Japan and traveled as far as North America (He et al., 2001). Three typical regions were selected for this study (Fig. 1), because of their different sanddust storm frequencies. These regions were the Aibi Lake region, the agricultural development region of Kelamayi, and the Gurbantunggut Desert. The Aibi Lake region, located in the southwestern part of the Junggar Basin, is a graben basin, surrounded by mountains in the north, south and west. The east of the basin is joined to the Gurbantunggut Desert. The Lake region exhibits a typical temperate continental arid climate, characterized by low rainfall (the annual average rainfall from 84°
86°
88°
46°
44°
1 Mountains
2 Sandy desert
3 Sampling site
Fig. 1. Sketch map of the study regions and sampling sites.
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1961 to 2001 is 103.2 mm in Jinghe County), high evaporation (1722 mm) and abundant solar energy. The Arla Mountain pass to its west is a main wind pass through which cold air (the northwesterly winds) intrudes. In Jinghe County, located down wind of the Aibi Lake region, gales with wind velocities X17 m s1 occur statistically on 26.3 days per year and sand-dust events occur on 49.2 days per year, based on data from 1961 to 2001. The agricultural development region of Kelamayi is located on the semi-desert plain of the southwestern part in the Junggar Basin. Its northern extent is near to Zhayier Mountain and the south is joined to the alluvial-fluvial plain of the lower reaches of the Manas River. This region is also characterized by abundant solar energy, low rainfall (109 mm), high evaporation (3545 mm) and a wide temperature range. In Kelamayi it is windy during the spring and summer seasons, and the gales blow for up to 67 days per year and sand-dust events occur on 5.3 days per year. The region has little surface and underground water resources. The Gurbantunggut Desert is located in the center of the semi-closed Junggar Basin with abundant solar energy and high evaporation (2000–2800 mm), but low precipitation (80–160 mm). In the southern part of the desert, the gales occur on average 15.2 days per year, and the sand-dust events on 19.3 days per year. There is almost no runoff, and underground water resources are very deep in this desert.
2.2. Methods Field investigations and sampling were conducted in three regions mentioned above during the April springtime periods of frequent sand-dust events, from 2001 to 2004. In particular data were collected on the day of the sand-dust event on 19 April of 2004, and on the following day. Twenty-seven representative sites were selected for the discriminant analysis (Fig. 1), and located by GPS. At each site 1–4 quadrats were used to collect the characteristic parameters of plant distribution, using quadrats of 10 10 m for communities of shrubs, 20 20 m for communities of low trees, and 3 quadrats, each of 1 1 m for herbs. The ‘herb’ quadrats were arranged along a diagonal, nested within the shrub and low tree quadrats. The species presence, frequency and abundance (herbs), vegetation cover, plant height and crown diameter were recorded. Within each quadrat, soil samples from the surface layer (0–10 cm) were collected for physical and chemical analyses. Topsoil water-content (SM), texture, organic matter (OM), nutrients, salts (TS), and pH were measured using routine methods (Nanjing Institute of Petrology, 1978). The particle sizes of the soil samples were determined by Malvern Laser Analyzer, and the parameters were computed based on the formula of Folk and Ward (Shanbei Team of Chengdu Institute of Geology, 1976). Since we could not accurately determine what kind of influences the ground-surface conditions of a source region were having on sand-dust event occurrence using physical experiments and mathematical simulation, CDA (that analyzes the relationship between ground-surface conditions and sand-dust events), was used, based on observational results and professional knowledge. A large number of mathematical statistics for plant distribution parameters and CDA of ground-surface characteristics for these sand-dust source regions were processed by Excel Spreadsheets and the SPSS10.0 software package. Meteorological data from the Xinjiang Meteorological Bureau were used in the analyses.
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3. Results and analyses 3.1. Sand-dust events and landscape characteristics According to the frequencies of the mean annual sand-dust events (Table 1), Jinghe County, as a representative of the Aibi Lake region, has a high frequency of sand-dust events, the Gurbantunggut Desert has a medium frequency of sand-dust events, and the agricultural development region of Kelamayi has a low frequency of sand-dust events. The Aibi Lake region exhibits the strongest and most frequent sand-dust events of the three regions. In Jinghe County drifting dust is the dominant form of sand-dust event. In the Gurbantunggut Desert the dominant form of event is blowing sand, and in Kelamayi the frequencies of the different kinds of sand-dust events are almost equal. The types and frequencies of the sand-dust events do not correlate well with the number of days of gales (Table 1). However, the different landscape types (Table 2) appeared to influence the frequency of sand-dust events. On the alluvial-fluvial plain of the Aibi Lake region, with fine gray-brown desert soil and gray desert soil, the agricultural exploitation is intensive, and the area of bare land cultivated in spring seasons continues to expand. These bare, cultivated lands as well as the dry clay lakebed are the sources of frequent sand-dust events. The contrasts with the lower sand-dust events of the Gurbantunggut Desert, where coarser, stable and semi-stable aeolian sandy soils are absolutely dominant and exhibit surface crusts, and where there is a high percentage of vegetation cover with good biodiversity. Before the land in the agricultural region in Kelamayi was developed, it had a strong erosion resistance with a high vegetation cover and compacted soil. Although the Kelamayi region has a large number of gale-days, sand-dust events did not often occur. According to the researches of Arya (cited by Liu and Dong, 2003) and Jia et al. (1999) a complex aerodynamic surface roughness at a landscape scale (Z0/Z0ef) influences the frequency and strength of sand-dust events. Such complex surface roughness is achieved by the amount of ground-surface vegetation cover and its ‘patchiness’—the distribution and distance between those patches of vegetation that create the roughness effect. Information from sand-dust events shows that the amount of dust generation is not related only to the area of bare lands (such as in the bare spring farmlands in the study regions), nor to only desert lands with low vegetation covers, but to the distribution of the vegetation. In our investigations, we observed that if the bare land or desert land with low vegetation cover (Co) was present as several patches in the landscape or is divided by shelter belt forestry around farmland planted to block the chief wind direction, then the ground-surface roughness can be increased. That is, the larger the separation or fragmentation of bare Table 1 Types and frequencies of sand-dust events (d a1, 1961–2001)
Gale (X17 m s1) Drifting dust Blowing sand Sand-dust storm Total
Aibi Lake region (Jinghe County)
Southern Gurbantunggut desert (Caijiahu)
Kelamayi
26.3 28.5 16.3 4.4 49.2
15.2 3.0 12.7 3.6 19.3
67.0 2.0 2.1 1.2 5.3
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Table 2 Main types of landforms, vegetation covers and soils Regions
Number of samples
Geomorphologic landscape
Types and covers of vegetation
Types of soils
Aibi Lake region
27
Alluvial/fluvial fan
Kochia prostrata and Artemisia spp. community and Haloxyon ammodendron community, vegetation cover of 10–15% Seriphidium borotalense and ephemeral plants community, vegetation cover of 15% Limonium suffruticosum, Halocnemum strobilaceum and Kalidium capsicum community, vegetation cover of about 30%; Nitraria sibirica sand dune and Tamarix sand dune, vegetation cover of 7–50% Tamarix sand dune, vegetation cover of 7%; Haloxyon persicum community, vegetation cover of 20% Halostachys caspica, Phragmites communis community, vegetation cover of over 70%; H. persicum and Calligonum leucocladum community, vegetation cover of over 30%; Seriphidium santolinum and ephemeral plants community, vegetation cover of about 35% Bare cultivated land
Gray-brown desert soil
Alluvial plain
Lakeshore/dried lakebed, shrub sand dunes
Mobile/semi-mobile sand dunes
Lands of interdunes
New cultivated land on alluvial plain New cultivated land on interdunes Oasis cultivated land Gurbantunggut 21 desert
Bare cultivated land, original vegetation is Phragmites communis meadow Bare cultivated land
Fluvial-alluvial plain Ceratoides lateens, Salsola in the northern desert arbuscula and Stipa glareosa, vegetation cover of over 45% Great longitudinal H. persicum, Ephedra distachya, dunes in the northern C. leucocladum and herb plants, desert vegetation cover of about 25–35% Complex longitudinal E. distachya, Seriphidium dunes santolina and ephemeral plants community, vegetation cover of about 40–60% Mobile belt on the top H. persicum, Artemisia arenaria of sanddunes and ephemeral plants community, shrub cover over 30%
Gray desert soil
Solonchak
Aeolian sandy soil
Saline soil, aeolian sandy soil, gray desert soil
Gray desert soil Meadow soil
Irrigated-cultivated soil Gray-brown desert soil
Aeolian sandy soil
Aeolian sandy soil
Aeolian sandy soil
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Table 2 (continued ) Regions
Number of samples
Geomorphologic landscape
Types and covers of vegetation
H. persicum and ephemeral plants community, vegetation cover of about 35–70% H. ammodendron, Reaumuria soongorica and annual halophytes, vegetation cover of 30–80% Lands of interdunes E. distachya and ephemeral plants community, vegetation cover of over 30%, soil crust developed Alluvial-fluvial plain in H. ammodendron and Reaumuria the northern desert soongorica, vegetation cover of 32%
Aeolian sandy soil
New cultivated land
Residual meadow soil/ Salinized dry boggy soil Dry boggy soil
Stable sanddunes of checkerboard-shaped form Transitional belt between desert and oasis
Kelamayi
14
Types of soils
Bare cultivated land
H. ammodendron community, vegetation cover of about 45% Lands of interdunes H. ammodendron, Nitraria sibirica, R. soongorica and ephemeral plants community, vegetation cover of over 40% Land eroded by wind Anabasis aphylla community, vegetation cover of about 35% Mobile sanddunes No vegetation Alluvial-fluvial plain
Salinized gray desert soil
Aeolian sandy soil
Salinized gray desert soil
Solonchak with surface sandy soil
Calcium-accumulated soil of dry salt basin Aeolian sandy soil
cultivated lands and desert lands with low vegetation covers are, the more difficult it is for sand-dust events to occur. Zhou (2003) analyzed the landscape pattern of the Jinghe Basin using the data of Landsat TM, and calculated that the landscape separation of the grasslands and desert lands with low vegetation covers was 0.468 and that for the cultivated lands was 1.761, and the total landscape fragmentation was 0.371. This indicated that the cover for both the desert land with low vegetation in its natural state, and for the broad scale cultivated land (bare land in spring) was not high. This landscape characteristic showed that the aerodynamic roughness coefficient, Z0ef, applied over a large area was falling, providing an underlying surface condition for increasing blowing sand in the Aibi Lake region. A shelter forest belt of 200 m width and 25 km length has been established at the northwestern and southwestern sides of 180 km2 of cleared land in the agricultural development region of Kelamayi. With the addition of an incomplete shelter forest network for farmlands and some woodlands for timber production, the ratio of woodland cover to cleared land reaches 34.2%. However, because these woodlands are planted in large blocks, they could not effectively divide the cultivated land and increase its fragmentation. Hence the aerodynamic roughness of the local underlying surface, Z0, was not increased.
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Stable and semi-stable sand dunes dominate the mid-southern Gurbantunggut Desert, and bare lands without vegetation cover are only found on the tops of mobile sand dunes in some sections of limited area. Although the vegetation covers among the microlandforms exhibit some differences, the macro-distribution of the vegetation still shows a high homogeneity. Differing from bare cultivated lands and desert lands with low vegetation cover, areas with patterns of small separation between vegetation, or with fragmentation of sandy lands with medium-high vegetation covers (over 20%) have a higher chance of decreasing outbreaks of drifting sand, due to their higher regional Z0. 3.2. Discrimination to ground-surface characteristics of sand-dust events In this discriminant analysis, the characteristics of vegetation structure, the internal factors of soil erodibility, and topsoil water-content were taken as chief ground-surface parameters that affect the process of blowing sand and dust. According to previous studies, P 3 indices were chosen: (1) Simpson ecological dominance C, where, C ¼ ðn2i =N 2 Þ. In the formula ni is the importance value of i species and N is the summation of all species’ importance values community; (2) Herbert diversity index Pin the PIE, where PIE ¼ N=ðN 1Þ 1 ð1=N 2 Þ n2i . In this formula ni is the number of i species and N is the summation of all species’ numbers in the community; and (3) vegetation cover describing the composition and structure of vegetation communities (Qian et al., 2004c). The factors influencing the soil’s internal stability include the mean particle size (Mz) and sorting index (s), (reflecting soil texture), and soil organic matter, total salts and pH. These nine variables were used to discriminate the ground-surface conditions susceptible to sand-dust event occurrence (Table 3). In the total data sets from the field investigations, several quadrats that were affected by other occasional factors were eliminated. Then, based on previous researches (Qian et al., 2004a, c), bare farmlands and desert lands of low vegetation cover in the region with high frequent sand-dust events, and the lands of sand dunes in the region with medium frequency sand-dust events were selected as the sampling sites, and 27 quadrats were used as the representatives of the regions for the discriminating analysis. Through CDA, the ground-surface conditions of the Aibi Lake region (with highfrequency storm-dust events) and the Gurbantunggut Desert (with low-frequency stormdust events) were compared to identify disadvantageous or advantageous threshold values of ground-surface conditions. Because a large area of the agricultural development in Kelamayi started in 2001 and disturbed the original ground-surface conditions, this region was used to compare actual conditions with those predicted by CDA. The canonical discriminant function coefficient was compared with the original data and the results of statistical tests are given below (Table 4). The w2 test is significant (Po0:001) and 100% of original grouped cases were correctly classified, showing that this canonical discriminant functions is effective. P The formula of discriminant function from original data: F ¼ ki¼1 ai xi þ Co: In the formula, F is the discriminant function, ai the coefficient of discriminant function, xi a variable, Co a constant term of the discriminant function, and k the number of variables (k ¼ 9). Here, the threshold value, F, between the regions of high- and lowfrequent storm-dust events is zero, that is, the region of F40 has the ground-surface conditions of high-frequency sand-dust event occurrences, and the region of Fo0 has that of low-frequency sand-dust event occurrences (Fig. 2).
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Table 3 The parameters for describing the ground-surface characteristics Site
PIE
C
Vegetation cover
Topsoil water-content
Mz
s
Organic matter
Soil salts
pH
F
04A6-1 04A6-2 04A9-1 04A9-2 04A9-3 04A10-2 04A11-1 04A11-2 04A11-3
0.70 0.00 0.02 0.30 0.00 0.60 0.65 0.53 0.00
0.33 0.00 0.59 0.50 0.00 0.49 0.51 0.44 0.00
16.09 0.00 20.80 73.03 0.00 25.14 21.13 20.01 0.00
1.73 3.27 0.50 1.58 8.64 0.42 0.32 0.32 0.96
0.05 0.03 0.28 0.09 0.06 0.08 0.14 0.15 0.03
2.84 2.47 0.87 1.59 1.82 2.03 0.46 0.57 1.97
0.54 0.72 0.09 0.55 1.10 0.07 0.13 0.13 2.04
0.06 0.11 0.14 1.69 1.94 0.06 0.06 0.05 0.11
9.23 9.86 9.10 9.08 9.08 9.33 9.61 8.38 8.54
2.07 3.02 2.90 2.83 2.04 2.42 2.58 0.42 2.71
02G12 02G15 03G6-1 03G6-2 03G6-3 03G10-1 03G10-2 03G10-3 03G10-4 03G3-1 03G3-2 03G3-3 03G3-4
0.38 0.75 0.66 0.71 0.68 0.47 0.51 0.70 0.73 0.73 0.46 0.65 0.56
0.16 0.16 0.20 0.15 0.12 0.30 0.25 0.20 0.12 0.32 0.18 0.17 0.18
18.67 29.80 27.72 53.87 63.74 31.83 35.03 58.55 23.53 26.49 27.14 35.72 90.52
2.45 3.97 1.08 1.22 0.72 1.21 0.55 1.28 0.71 2.03 2.36 2.22 2.71
0.20 0.12 0.15 0.16 0.19 0.13 0.15 0.19 0.16 0.18 0.18 0.25 0.21
0.32 0.57 0.73 0.63 0.60 1.11 0.72 0.54 0.74 1.89 1.54 1.44 1.52
0.04 0.18 0.16 0.10 0.11 0.14 0.12 0.05 0.09 0.13 0.39 0.11 0.19
0.02 0.03 0.03 0.02 0.02 0.03 0.03 0.01 0.02 0.03 0.04 0.02 0.02
7.69 8.10 8.33 7.69 8.27 8.08 8.29 7.46 7.56 8.30 8.31 7.40 7.50
2.37 1.89 1.35 3.09 2.49 0.67 0.92 3.38 3.35 0.88 0.78 3.52 3.09
K12 K13 K14-2 K15 K17
0.00 0.47 0.47 0.00 0.35
0.00 0.31 0.45 0.00 0.51
0.00 37.55 34.71 0.00 31.33
2.31 0.87 1.21 1.84 2.04
0.02 0.07 0.03 0.02 0.01
2.44 1.93 2.39 1.87 1.45
1.14 0.52 0.44 0.91 0.74
0.99 0.15 1.07 2.88 0.45
8.14 8.20 8.18 8.39 8.27
0.75 0.42 1.23 0.44 2.62
Table 4 Canonical discriminant function coefficients No. for variable (xi)
Discriminant function coefficient of original data (ai)
Contributing ratio of a variable in discriminant formula (%)
PIE C Vegetation cover Topsoil water content Mz s Organic matter Total salt pH Constant term
1.654 7.410 0.819 0.035 5.585 0.002 1.944 0.218 1.361 12.127
7.7 10.5 2.6 1.0 1.7 0.0 25.7 3.3 47.5
Wilks’ l ¼ 0:148, w2 ¼ 29:615, df ¼ 9, Sig. ¼ 0.001.
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58 5.00 4.00 3.00 2.00 F
1.00
Threhold value F = 0
0.00 -1.00 -2.00 -3.00
04
A 6 04 -1 A 6 04 -2 A 9 04 -1 A 9 04 -2 A 04 9-3 A 1 04 0-2 A 1 04 1-1 A 1 04 1-2 A 1 03 1-3 G 3 03 -1 G 3 03 -2 G 3 03 -3 G 3 03 -4 G 6 03 -1 G 6 03 -2 G 6 02 -3 G 1 02 2 G 03 15 G 1 03 0-1 G 1 03 0-2 G 10 03 -3 G 10 K -4 12 K 13 K 14 -2 K 15 K 17
-4.00
Sites
Fig. 2. Distribution of discriminant values of the ground-surface conditions.
The discriminant results for the agricultural development region of Kelamayi show that its ground-surface conditions represent the characteristics of a region with high frequency sand-dust events (Fig. 2). This indicates the danger of future deterioration of this environment. 3.3. Erosion-resistance action and discriminant significance of ground-surface parameters The results of CDA about the ground-surface conditions of Aibi Lake region (with highfrequency sand-dust events) and the Gurbantunggut Desert (with low-frequency sand-dust events) indicated that the region of F40 has the ground-surface conditions of high frequency sand-dust event occurrences, and the region of Fo0 has that of low-frequency sand-dust event occurrences. The parameters with positive coefficients in the discriminant function made F40, so, in primary data presented in this paper (Table 3), they were negative factors for ground-surface stability and favored the occurrence of sand-dust events, whereas the parameters with negative coefficients made Fo0 and have positive effects on ground-surface stability. According to the negative and positive values of the corresponding coefficients and the small and large values of the contributing ratios for these parameters in the discriminant function, the character and weakness and strength of these parameters in predicting ground-surface stability can be determined. The results show that the parameters with negative effects are the ecological dominance of vegetation (C), the organic matter, soil salinity and soil pH (Table 4). Among these, the discriminant meaning of pH values is significant, and the contributing ratio is up to 47%. The values of soil pH are affected by many factors (Qi, 1994; Dai, 1997), particularly the soil content of carbonate ions (mainly CO2 3 /HCO3 ), organic matter and the level of salts in the soil. In a previous study (Qian et al., 2004d), it was noticed that a significant correlation existed 2 between pH, salts (Ca2+, Na+, HCO 3 , CO3 ) and soil organic matter. The values of soil pH from the Aibi Like region showed a direct correlation to the content of CO2 3 (R ¼ 0:76, P40.01). The soil pH of the agricultural development region in Kelamayi was negatively correlated with soil organic matter (Qian et al., 2004d), and according to experimental data from the Xinjiang University of Agriculture (Qian et al., 2003), the pH
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value is closely related to the soil HCO 3 content in this region. Since high soil pH values in these regions correlated with high soil carbonate levels, it is likely that the sandy soil stability in this region is low, similar to the findings of Chepil (1952) and Singer et al. (2003). Ecological dominance of vegetation, with a contributing ratio of 10.5% is also a negative factor, whose influence on ground-surface stability is obvious. In the study regions, the ecological dominance of vegetation is generally high because of the farmland with a single species planting structure and the natural desert land with a low vegetation cover of a single species of shrub or semi-shrub. This lack of biodiversity greatly weakens the erosion-resistance of the topsoil. Soil organic matter content proved to be an important parameter with a significant discriminant effect (contributing ratio ¼ 25.7%). It has both positive and negative effects on the erosion-resistance of soil. Generally, if high organic matter in the topsoil is caused by a well-developed bio-crust or by surface litter, this will undoubtedly increase the erosion-resistance of soil (Li et al., 2001; Eldridge and Leys, 2003). However, if the organic matter in the soil is decomposed, this will make the soil susceptible to erosion (Chepil, 1954; Singer et al., 2003). The type of organic matter existing in these regions is not clear, so the discriminant meaning is a statistical result. A high content of topsoil salts (TS) in the study regions is usually a negative factor because it affects plant growth and adds caustic salts to the material blowing across the soil surface (Qian et al., 2004a). However, Singer et al. (2003) noticed that the dust generation potential of fine silt soil, PM10 containing a high salt content was low when he studied the southern Aral Sea. The reason for this was that salt crystals and fine soil particles form into a stable crust preventing the soil from erosion. The important point here is that erosionresistance of salty soil crusts are sensitive to soil particle sizes. The salty crust of a sandy soil or sandy loam is easily broken so that the soil generates dust (Singer et al., 2003). The integrated effect of salt and soil particle size makes the contributing value of the soil salt-content only 3.3%. The main factors for strengthening the stability of surface soils are their vegetation cover, plant biodiversity and mean soil particle size (Table 4). Vegetation cover and biodiversity increase the aerodynamic roughness Z0 and reduce the wind speed at the ground surface (Zhang et al., 2003). Furthermore, their plant litter can cover the soil surface to avoid wind-erosion (Wiggs et al., 1995; Eldridge and Leys, 2003). Comparison of these two parameters, cover and biodiversity, shows that the plant biodiversity is the more important with a contribution ratio of 7.7%. Diverse herb and woody structures in multi-layers decreases the aerodynamic porosity of vegetation and further reduces the flux of sand-dust (Lancaster, 1998). The soil texture (indicated by mean particle size (Mz)) influences soil stability and has an important effect on discriminating the topsoil erodibility with a contributing ratio of 1.7%. According to the observation, a soil with a high Mz has a strong resistance to wind erosion, whereas areas with top soils of fine silt and clay can be the greatest sources for dust events. Orlovsky et al. (2005) and Singer et al. (2003) presented this result when they studied the Aral Sea of central Asia, which exhibits similar environmental conditions to the Aibi Lake region, and Liu and Dong (2003) also proved the effect of soil texture on wind erosion in a wind tunnel experiment. Many researchers conclude that topsoil water content is the most positive factor in controlling soil erodibility (Wang et al., 2001). However, Wiggs et al. (2004) showed that at certain wind speeds a lower topsoil water content (o4–6%) does not exhibit significantly worse soil erodibility than a soil with a higher soil moisture content. However, since the
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study regions are arid to semi-arid, and the topsoil in the spring seasons of frequent gales is especially dry (with soil water content commonly less than 2%), the question about the impact of the topsoil moisture on sand-dust events in spring seasons in the study regions is largely irrelevant, with a contributing ratio of only 1%. 4. Conclusions The vegetation cover, community biodiversity, degree of ecological dominance, topsoil moisture, soil organic matter, soil texture, soil salts and pH constitute the discriminant factors that are the ground-surface variables impacting the process of wind erosion and blowing sand and dust. These factors can effectively discriminate the differences between the Aibi Lake region with high-frequency sand-dust events and the Gurbantunggut Desert with medium-frequency sand-dust events during the spring seasons of frequent sand-dust event occurrences. In the Aibi Lake region, where gray-brown desert soil and gray desert soil are widely distributed and land clearing for agricultural development is intensive, the negative factors affecting ground-surface stability are mainly high soil pH, soil salinity, soil organic matter and the degree of ecological dominance in the vegetation. In the Gurbantunggut Desert, where stable and semi-stable aeolian sandy soils are dominant and are less disturbed by human exploitation, the erosion-resistance of its topsoil benefits from the higher vegetation cover, plant community diversity and coarser texture. The results of the CDA also show the agricultural development region of Kelamayi has the ground-surface characteristics similar to the regions with high-frequency sand-dust events. The results of this study indicate that the Kelamayi development area is under threat of serious wind erosion resulting from the pattern of land clearing and development. However, the risk of damaging wind erosion can be reduced by returning some cultivated land to grassland or woodland, and by rationally developing a network of forest shelterbelts throughout the farmland to increase the landscape fragmentation. Such fragmentation of cultivated land (or desert land with low vegetation cover) can raise the aerodynamic roughness of the ground surface, thereby reducing wind speed and the probability of sand-dust event occurrences. Acknowledgments This research was supported by the Xinjiang Uygur Autonomous Region’s Natural Science Foundation Project and the especial support project from the Director’s fund of the Xinjiang Institute of Ecology and Geology. We are grateful to Mr. Adrian Williams for reviewing the English text and to the editor and anonymous referee for improving the manuscript. References Chang, Y.S., Arndt, R.L., Carmichael, G.R., 1996. Mineral base-cation deposition in Asia. Atmospheric Environment 30, 2417–2427. Chepil, W.S., 1952. Factors that influence clod structure and erodibility of soil by wind: I. soil structure. Soil Science 75, 473–483. Chepil, W.S., 1954. Factors that influence clod structure and erodibility of soil by wind: III. calcium carbonate and decomposed organic material. Soil Science 77, 473–480.
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