The Qilian Mountains is located in the northeast fringe of Tibetan Plateau of northwest China. It is the important source of water for the famous Hexi Green Corridor, one of the most important agricultural regions in northwest China. The Qilian Mountains belong to the semiarid-arid region with the annual mean precipitation less than 400 mm and evaporation higher than 800 mm, the parental materials of soils usually contain aeolian loess with high CaCO3 content  which may be not leached out completely from soils under the semiarid-arid climate. CaCO3 quantity and illuviation in soil are generally dominated by precipitation  -  and are also influenced by other factors, such as topography, land use type, soil organisms and microbes      . Thus, understanding CaCO3 can disclose the characteristics of regional climate, landscapes, soil genesis and evolution as well as soil other properties   -  .
The altitude sequence is often used in soil studies in the hilly and mountainous regions    , and is potential to study soil CaCO3 characteristics in the Qilian Mountains whose elevation spans from 2800 m to 5808 m and shows obvious vertical differences in climatic condition and landscape   . So far, a lot of soil information in altitude sequence is available in the Qilian Mountains, for example, soil organic matter, total nitrogen, water retention and microbe      , but little reference is found on soil CaCO3 there. Therefore, this paper mainly aims at: 1) select a typical watershed in the Qilian Mountains; 2) survey and sample typical soil profiles in an altitude sequence; 3) disclose CaCO3 content, density, illuviation mode and depth of CaCO3, and their relation with the terrain attributes.
2. Methods and Materials
2.1. Study Area
The Hulugou Watershed (38˚12'N - 38˚17'N, 99˚50'E - 99˚54'E) is regarded as the most typical watershed in the arid and semi-arid Qilian Mountains   , it is located at the Qilian Alpine Ecology and Hydrology Research Station of the Cold and Arid Regions Environmental and Engineering Research Institute (CAS), occupies 23.1 km2 in area, spans from 2960 m to 4820 m in altitude, and belongs to alpine periglacial landform influenced by the continental alpine climate with the annual temperature from −18.4˚C to 19.0˚C with a mean of 0.2˚C and annual precipitation from 300 mm to 400 mm. The aeolian silty loess is the dominant parental materials of soils, mixed with various glacial debris. Vegetation is mainly alpine shrubs and grasses. The dominant herb species of the profiles are Carex melantha, Kobresia capillifolia and Polygonum viviparum. Soils are relative young and mostly poorly developed, Entisols, Inceptisols and Mollisols are the dominant soil orders  , Gelisols only appear above 3600 m and Histosols are sporadically distributed in depressions  .
2.2. Typical Profile, Survey and Sampling, Analytical Methods
The watershed DEM map (Figure 1) was formed by using the 90 m resolution SRTM3 DEM data, assisted with the information of the parental material, vegetation, terrain and foot accessibility. Eighteen typical profiles of alpine grassland with the same parental material were selected in an altitude sequence (red points in Figure 1).
Sampling was conducted from June to July in 2013. The longitude and latitude of each profile was obtained by portable GPS (Mode: Garmin GPS map 629sc). Each profile was dug to the appearance of parental rocks. Profile thickness and gravel (>2 mm in diameter) volumetric percentage (%) were estimated visually in the field. Soil samples of all genetic horizons were collected and ground to pass 0.149 mm diameter sieve for measuring CaCO3 content by the calcimeter method. The cutting ring samples of 17 horizons with no or little gravels or grass roots were collected to measure bulk density by the oven drying method  , bulk densities of other 37 horizons without cutting ring due to dense gravels or roots were inferred from the correlation between bulk density (y) and organic matter content (x) of 50 genetic horizon samples sampled within and near the watershed (, , p < 0.01, n = 50).
The weighted mean content of CaCO3 (X) of each profile was calculated as:
where Hi is the thickness of i horizon, Xi is the measured CaCO3 content (g・kg−1)
Figure 1. DEM and typical profile sites of the Hulugou Watershed in the Qilian Mountains.
of i horizon, H is the profile thickness.
CaCO3 density (CaCO3D) of each profile was calculated as:
where Bi is the bulk density of i horizon, Gi is the volumetric percentage of gravels (>2 mm in diameter), k is the horizon number of a profile.
2.3. Data of Terrain Attributes
According to the information the longitude and latitude of each profile, the values of the terrain attributes including the altitude, slope, aspect, plane curvature, profile curvature and terrain wetness index (TWI) were obtained from the 90 m resolution SRTM3 DEM data on ArcGIS 9.3 platform (Table 1).
2.4. Statistical Analysis
IBM SPSS Statistics 20 was used to test for curve estimation. Origin Lab 8.0 was used to depicted the illuviation mode of CaCO3.
3. Results and Discussion
3.1. Terrain Attributes
Results of Curve Estimation between terrain attributes showed that a significant quadratic correlation existed between slope (y), TWI and altitude (x)
Table 1. Information of terrain attributes of the profiles in the Hulugou Watershed.
(, R2 = 0.429, p < 0.05;
, R2 = 0.564, p < 0.01;
, R2 = 0.355, p < 0.05), significant exponential correlation between slope and TWI (, R2 = 0.623, p < 0.01). The above results indicated the complex and irregular spatial distribution pattern of terrain attributes in the Hulugou Watershed.
3.2. Profile Thickness (PT), Grave Volumetric Percentage GV(%) and Bulk Density (BD)
PT, GV(%) and BD are the three parameters in calculating CaCO3 density. Table 2 showed the statistical information of the three parameters of the 18 profiles. PT ranged from 10 cm to 135 cm with a mean of 62 cm, GV(%) ranged from 5% to 95% with a mean of 40%, and BD ranged from 0.30 g/cm3 to 1.73 g/cm3 with a mean of 1.02 g/cm3.
Results of Curve Estimation between PT, GV(%), BD and terrain attributes were presented in Table 3, which indicated that altitude, gradient and aspect can be used to predict the PT, GV(%) and BD, while altitude had a higher prediction efficiency in comparison with gradient and aspect. The significant correlation with altitude can mainly be due to the geological erosion process caused by water, gravity and wind. All the 18 profiles actually are located on the hillsides with
Table 2. Statistic information of PT, GV(%) and BD of the profiles in the Hulugou Watershed.
slope ranged from 8˚ to 31˚ (Table 1), thus, erosion is inevitable even with dense grass coverage, besides, comparatively soil particles are easier to be eroded and move fast from the high place to low place than the gravels.
3.3. Illuviation Mode and Depth of CaCO3 in Profiles
The illuviation depth of CaCO3 in the profiles were ranged from 12 cm to 54 cm in the profiles (20 cm, 12 cm, 15 cm, 38 cm, 40 cm, 28 cm, 54 cm and 35 cm for P8, P9, P11, P13, P15, P16, P17 and P18, respectively, Figure 2(a) and Figure 2(b)). These 18 profiles can be categorized into three kinds of illuviation modes of CaCO3 according to CaCO3 contents of different horizons (Figure 2): 1) No illuviation mode, for examples of the profiles above 3600 m in altitude only had one horizon (P1 to P7) or profiles where CaCO3 leached out intensively from the soil (
Table 3. Correlation between PT, GV(%), BD and terrain attributes in profiles of the Hulugou Watershed.
(a) Middle illuviation mode (b) Down illuviation mode
Figure 2. Illuviation mode of CaCO3 in the profiles in the Hulugou Watershed.
content increased with the increase of profile depth (P8, P11, P15, P16 and P18), the initial depth of illuviation was 20 cm, 15 cm, 40 cm, 28 cm and 18 cm for P8, P11, P15, P16 and P18, respectively, CaCO3 content was about 9 - 29.5 times as much as the up horizons.
For the three kinds of illuviation modes of CaCO3, the results of one-way ANOVA LSD tests (Table 4) showed that a significant difference existed only in profile curvature between no illuviation mode and down illuviation mode of CaCO3 (p < 0.05), which indicated that it was difficult to predict the illuviation mode of CaCO3 by terrain attributes in the Hulugou Watershed.
Results of Curve Estimation between the illuviation depth and terrain attributes were presented in Table 5, which indicated that altitude and TWI can be used to predicate the illuviation depth of CaCO3 in the profiles in this area, while TWI is better than altitude and aspect in the accuracy of prediction.
CaCO3 illuviation depth significantly correlated with altitude and TWI, which can be attributed to the significant correlation between altitude with precipitation and TWI. Three models are available to contact CaCO3 illuviation depth (D, cm) with precipitation (P, mm), i.e., Retallack model  : p = 137.240 + 6.450D + 0.013D2 (R = 0.52, n = 807), Zhao model  : p = 3.057D + 168.5 (R = 0.96, n = 15), and Pan and Huang model  : p = 68.4990 + 12.063D − 0.0693D2 (R = 0.725, n = 48). Because no measured precipitation data is available for the 18 profiles, we used the rough information of the lowest and highest precipitation (300 mm and 400 mm) in the Hulugou Watershed to calculate the theoretical CaCO3 illuviation depth, which was 24 cm - 37 cm, 43 cm - 76 cm, and 22 cm - 34 cm for Retallack model, Zhao model and Pan and Huang model, respectively. Compared with real 12 cm - 54 cm of CaCO3 illuviation depth of the our profiles, the Retallack model and Pan and Huang model can be regarded more feasible than the Zhao Model which overestimated CaCO3 illuviation depth. We
Table 4. Differences in terrain attributes between different illuviation modes.
*No illuviation: P10, P12 and P14; Middle illuviation: P9, P13 and P17; Down illuviation: P8, P11, P15, P16 and P18; Std.: standard deviation, A, AB and B stands for different significance.
Table 5. Correlation between illuviation depth of CaCO3 with terrain attributes of the Hulugou Watershed.
attributed it to the least or insufficient number of soil profiles used in Zhao model.
Zhao  pointed out when two, three layers or unusually thick CaCO3 illuvial horizon exists under the same paleosol or weathering profile, it indicates that there are two or more soil-forming periods and corresponding climate change at that time, but so far, there is no information or model of terrain attributes available on the CaCO3 illuviation mode in soil profile. Our study also found it is hard to indicate CaCO3 illuviation mode by the terrain attributes in the irregular and complex alpine region.
3.4. Weighted Content and Density of CaCO3
For 18 profiles, CaCO3 mainly exists in the form of mottles and powders in the profiles. No pseudomycelium or concretion was found, which means it is hard for CaCO3 to crystallize under the semiarid-arid climate of the Qilian Mountains. Table 6 showed that CaCO3 weighted content of the profiles ranged from 1.30 g・kg−1 to 93.09 g・kg−1 with mean of 23.53 g・kg−1 and standard deviation of 24.19 g・kg−1, while CaCO3 density ranged from 0.05 kg/m2 to 75.69 kg/m2 with a mean of 9.79 kg/m2 and standard deviation of 18.05 kg/m2. Results of Curve Estimation between the weighted content and density of CaCO3 with terrain attributes were presented in Table 7, which revealed that slope, plane curvature, profile curvature and TWI can be used to predicate weighted content of CaCO3 in these profiles. Besides, altitude, aspect, gradient and TWI can be used to predicate the
Table 6. CaCO3 weighted content and density of profiles in the Hulugou Watershed.
Table 7. Correlation between CaCO3 weighted content, density and terrain attributes in the Hulugou watershed.
CaCO3 density. Obviously, TWI had the highest prediction efficiency in comparison with other terrain attributes.
CaCO3 weighted content had significant negative correlation with profile curvature, it is because the higher of profile curvature, the higher of the change rate of gradient, which may cause more CaCO3 to be moved away from soil through water.
CaCO3 density had significant negative correlation with gradient, usually the increase of gradient accelerates the movement of moisture, which promotes the eluviation of CaCO3. In general, precipitation is higher in the shady slope than the sunny slope, so CaCO3 density in shady slope should be lower than that of sunny slope. But our study showed a negative correlation between CaCO3 and aspect, which means that the shady slope contains higher CaCO3 than the sunny slope. To some extent it can be attributed to the significant negative correlation between the profile thickness and aspect. Significant nonlinear correlation existed between CaCO3 density and altitude, which is not only attributed to PT, GV(%) and BD, but also to the changes of precipitation and TWI with altitude.
Liu et al.  showed that CaCO3 density of 1 m depth profile ranged from 1.0 kg/m2 to 137.6 kg/m2 in the alpine grassland of Qinghai Province (the same province as the Hulugou Watershed). Zhang  showed CaCO3 density in 1 m depth profiles in the Loess Plateau ranged from 8.3 kg/m2 to 291.7 kg/m2. CaCO3 density in our profiles ranged from 0.05 kg/m2 to 75.69 kg/m2, which can be regarded within the above ranges.
CaCO3 illuviation depth is related to altitude and TWI. CaCO3 weighted content is related to slope, plane curvature, profile curvature and TWI, CaCO3 density related to altitude, gradient, aspect and TWI, which indicated the data of the terrain attributes are feasible in predicting CaCO3 content and density in the alpine region. But almost all these correlations are nonlinear and some even with low value of R2. This can be attributed to the irregular and complex spatial distribution of terrain attributes. In general, the factors influencing CaCO3 behaviors in soil is complex, beside precipitation, other factors include topography, parental materials, etc.     . Usually precipitation increases with altitude increases, but Niu et al.  found that in the north slope of the Qilian Mountains (similar as the Hulugou watershed), the average annual rainfall increases about 17.41 mm from 1700 m to 3300 m in altitude; while decreases about 30.21 mm when the altitude increases 100 m from 3300 m to 3800 mm. It is due to the increase of the wind speed and the decrease of the temperature which cause the rainfall mainly in solid phase, thus, the precipitation actually reduces. Thus using only precipitation it is difficult to predict CaCO3 characteristics in the alpine region because of the irregular and complex spatial distribution of terrain attributes.
Moreover, the CaCO3 data of each profile in our study only represent a pedon in 90 m × 90 m pixel of SRTM3 DEM. Actually a 90 m × 90 m pixel usually contains many pedons with different terrain units, the extracted value of a terrain factor used for a profile actually is the mean value of the different pedons in the pixel, this is not the strict one-to-one correspondence and will weaken the accuracy of the study consequences, so DEM data with higher resolution should be found and used in further study.
The large span of altitude and loess parental material of soils in the arid and semiarid-arid Qilian Mountain enable altitude sequence to be a potential way in studying CaCO3 characteristics. Our study of 18 typical profiles in an altitude sequence in the Hulugou Watershed showed that CaCO3 weighted content, density and illuviation depth had significant nonlinear correlations with the some terrain attributes, which on one hand indicated that the data of terrain attributes extracted from DEM are a potential to be used in predicating CaCO3 characteristics in the alpine region because the terrain factor influences precipitation and its redistribution in soil, but on the other hand it meant that for the irregular and complex alpine region, further attention should focus on how to set up models based on precipitation combined with terrain attributes in order to predicate CaCO3 characteristics with high accuracy.
This study was supported by projects of the National Natural Science Foundation of China (Grant No.s 41371224, 41130530, 913253002) and the National S&T Basic Special Foundation (Grant No. 2014FY110200). We would like to express thanks to the contribution from all the participants in field soil survey, laboratory work and data processing. We are also grateful to Prof. & Dr. Bruce Velde, Laboratoire de Géologie of Ecole Normal Supérieurey for his linguistic correction.