**2.2 Experimental design**

After a complete catchment survey, together with an evaluation of topography and vegetation types, five plots (20 m � 5 m) were constructed at the study areas in July 2009. The vegetation types in the five plots are *Hippophae rhamnoides + Pinus tabuliformis* (I) (PRa), *Hippophae rhamnoides + Pinus tabuliformis* (II) (PRb), *Pinus tabuliformis* (P), *Hippophae rhamnoides* (R), and *Lespedeza davurica + Leymus*


#### **Table 1.**

*The specific conditions of five runoff plots.*

**Figure 1.** *The location of the study area and distribution of the five runoff plots.*

*secalinus* (G) (**Table 1**). During the preliminary stage of establishing the plots, the vegetation, soil, and earth surface were severely degraded by the constructors through trampling and digging. With time, the vegetation and soil conditions in the plots obviously improved. The runoff and sediment yield during PPS and PLR were extremely different. This study uses the data measured in 2009 as of PPS and in 2010–2012 as of PLR for analysis. We ultimately drew conclusions regarding the order of weight for factors affecting runoff and sediment yield during the two different stages. **Figures 2** and **3** show part of the runoff plots during these two stages.

**2.3 Measurement**

**Figure 3.**

*disturbance.*

**2.4 Meteorological data**

maximum rainfall intensity).

using a ring knife (diameter, 5 cm; height, 5 cm).

**2.5 Soil bulk density**

**123**

concrete with a dimension of 1 <sup>1</sup> 1 m3

We monitored the daily runoff and sediment yield during the main rainy season (July–September) from 2009 to 2013. **Table 1** shows the detailed parameters of these runoff plots. At the lower end of each plot, a sump was used to collect runoff and sediment yield during each rainfall-runoff event. The sump was composed of

A simple meteorological field station (HOBO Weather Station, Onset Computer Co., Bourne, MA, USA), including a tilting rain gauge, was installed in the study area to record year-round meteorological data (**Figure 2**). The weather station recorded once 5 min passed. So, we calculated the I5, I10, I15, and I30 values from the

September of each year. Following each rainfall event, three samples of approximately 1.65 L were removed from each sump to estimate the sediment yield.

*Contrast in plot regions and conditions during PPS and a period of land restoration (PLR) after land*

*Soil Erosion Influencing Factors in the Semiarid Area of Northern Shaanxi Province, China*

*DOI: http://dx.doi.org/10.5772/intechopen.92979*

weather station record (I5 = 5-min maximum rainfall intensity; I10 = 10-min maximum rainfall intensity; I15 = 15-min maximum rainfall intensity; I30 = 30-min

Three soil profiles were excavated at the uphill, middle, and downhill areas which are near the runoff plots. Soil samples were collected from each profile at the depths of 0–20, 20–40, 0–60, 60–80, and 80–100 cm. Soil bulk density was tested

. The data were measured from July to

#### **Figure 2.** *The plots and meteorological station conditions during a period of the preliminary stage (PPS) after land disturbance.*

*Soil Erosion Influencing Factors in the Semiarid Area of Northern Shaanxi Province, China DOI: http://dx.doi.org/10.5772/intechopen.92979*

**Figure 3.** *Contrast in plot regions and conditions during PPS and a period of land restoration (PLR) after land disturbance.*

#### **2.3 Measurement**

*secalinus* (G) (**Table 1**). During the preliminary stage of establishing the plots, the vegetation, soil, and earth surface were severely degraded by the constructors through trampling and digging. With time, the vegetation and soil conditions in the plots obviously improved. The runoff and sediment yield during PPS and PLR were extremely different. This study uses the data measured in 2009 as of PPS and in 2010–2012 as of PLR for analysis. We ultimately drew conclusions regarding the order of weight for factors affecting runoff and sediment yield during the two different stages. **Figures 2** and **3** show part of the runoff plots during these two

*The plots and meteorological station conditions during a period of the preliminary stage (PPS) after land*

*The location of the study area and distribution of the five runoff plots.*

stages.

**Figure 2.**

**122**

*disturbance.*

**Figure 1.**

*Soil Moisture Importance*

We monitored the daily runoff and sediment yield during the main rainy season (July–September) from 2009 to 2013. **Table 1** shows the detailed parameters of these runoff plots. At the lower end of each plot, a sump was used to collect runoff and sediment yield during each rainfall-runoff event. The sump was composed of concrete with a dimension of 1 <sup>1</sup> 1 m3 . The data were measured from July to September of each year. Following each rainfall event, three samples of approximately 1.65 L were removed from each sump to estimate the sediment yield.

#### **2.4 Meteorological data**

A simple meteorological field station (HOBO Weather Station, Onset Computer Co., Bourne, MA, USA), including a tilting rain gauge, was installed in the study area to record year-round meteorological data (**Figure 2**). The weather station recorded once 5 min passed. So, we calculated the I5, I10, I15, and I30 values from the weather station record (I5 = 5-min maximum rainfall intensity; I10 = 10-min maximum rainfall intensity; I15 = 15-min maximum rainfall intensity; I30 = 30-min maximum rainfall intensity).

#### **2.5 Soil bulk density**

Three soil profiles were excavated at the uphill, middle, and downhill areas which are near the runoff plots. Soil samples were collected from each profile at the depths of 0–20, 20–40, 0–60, 60–80, and 80–100 cm. Soil bulk density was tested using a ring knife (diameter, 5 cm; height, 5 cm).

#### **2.6 Soil steady infiltration rate**

An instrument for recording the process of water infiltration into the soil was employed, and the depth of infiltration was calculated by the empirical equation:

$$H = 0.19635 \times h \times \cos a \tag{1}$$

**Runoff Sediment yield Correlation Rank Proportion Correlation Rank Proportion**

0.5908 12 0.5393 14

0.7526 2 0.8285 2

0.6455 10 0.6411 10

**PPS PLR GRG Rank Proportion GRG Rank Proportion**

Vegetation Vegetation types 0.5791 13 22.28% 0.5851 13 16.85%

*Soil Erosion Influencing Factors in the Semiarid Area of Northern Shaanxi Province, China*

Rainfall Rainfall amount 0.7685 1 27.85% 0.6922 4 20.74% Rainfall duration 0.704 6 0.6474 8

Soil Soil bulk density 0.6948 8 25.53% 0.8657 1 22.57%

Topography Slope aspect 0.6655 9 24.34% 0.6315 11 18.46% Slope gradient 0.6124 11 0.6009 12 Runoff 0.7134 3 21.38% *Note: I5, 5-min maximum rainfall intensity; I10, 10-min maximum rainfall intensity; I15, 15-min maximum rainfall*

*Gray relational grade between runoff and sediment yield and the factors influencing runoff and sediment yield.*

Vegetation Vegetation type 0.6133 6 0.2476 0.6757 2 0.2594 Vegetation cover 0.6023 7 0.6061 8 Rainfall Rainfall amount 0.6417 3 0.2411 0.6415 3 0.2539 Rainfall duration 0.6303 4 0.7443 1 Average rainfall intensity 0.5835 10 0.5675 12

Soil Soil bulk density 0.5936 9 0.2539 0.5665 13 0.2408 Soil steady infiltration 0.6524 2 0.6231 4 Topography Slope aspect 0.6681 1 0.2574 0.5995 11 0.2459 Slope gradient 0.5955 8 0.6156 6 *Note: I5, 5-min maximum rainfall intensity; I10, 10-min maximum rainfall intensity; I15, 15-min maximum rainfall*

I5 0.5584 12 0.6146 7 I10 0.5662 11 0.6186 5 I15 0.6209 5 0.6032 9 I30 0.5406 13 0.6012 10

I5 0.6994 7 0.643 9 I10 0.7112 5 0.6666 7 I15 0.7358 4 0.687 5 I30 0.7482 3 0.6797 6

Vegetation coverage

*DOI: http://dx.doi.org/10.5772/intechopen.92979*

Average rainfall intensity

Soil steady infiltration rate

**Table 2.**

**Table 3.**

**125**

*intensity; I30, 30-min maximum rainfall intensity; GRG, gray relational grade*

*intensity; I30, 30-min maximum rainfall intensity; GRG, gray relational grade*

*Gray relational grade between runoff and its influential factors.*

where *H* is the depth of infiltration, *h* is the change in the standing water level, and *a* is the slope gradient. At the beginning of the experiment, data were recorded every 10 s for 90 s; then, data were recorded every 30 s for 5 min; at the end, data were recorded once every minute. The experiment was not completed until similar measurements were observed 5–6 times [63].

#### **2.7 Data processing**

We used the Principal Coordinates Analysis (PCoA) method to convert the qualitative variables, such as vegetation and slope aspect, into quantitative variables [64]. To reduce the error of the evaluation, we used PCoA for numerical transformation and calculate the characteristic value of each item, and then we used characteristic value for analysis.

The Chinese scholar Deng Julong first proposed the gray correlation method. This method is based on developmental trends of the degree of similarity or dissimilarity between factors. By comparing a sequence of an established family of curves and a reference sequence curve, using geometric similarity, one can determine the degree of connection between the reference sequence and a comparison sequence set of data. If the shapes are similar, then a greater degree of similarity is identified. The comparison sequence and the reference sequence include both temporal series and nontemporal series. Therefore, the gray correlation method provides a quantitative measurement for the development of a system. This method is highly suitable for the analysis of a dynamic process. The specific method is shown as below [65]:

1. The parameters are standardized using:

$$\mathbf{x}\_i^\circ(\mathbf{k}) = \frac{\mathbf{x}\_i(\mathbf{k}) - \min \mathbf{x}\_i(\mathbf{k})}{\max \mathbf{x}\_i(\mathbf{k}) - \min \mathbf{x}\_i(\mathbf{k})} \tag{2}$$

where *x*, *i* ð Þ k are the new values obtained when the parameters are standardized by Eq. (2) and *x*, *i* ð Þ k are the original parameters.

2. Then, the correlation coefficient is calculated using:

$$\gamma\left(\mathbf{x}\_{0}^{\cdot}(\mathbf{k})\mathbf{k}, \mathbf{x}\_{i}^{\cdot}(\mathbf{k})\right) = \frac{\Delta\mathbf{x}\_{min} + \varepsilon\Delta\mathbf{x}\_{max}}{\Delta\mathbf{x}\_{0i}(k) + \varepsilon\Delta\mathbf{x}\_{max}} \tag{3}$$

$$
\Delta \mathbf{x}\_{\rm min} = \min\_{\forall i \boldsymbol{\epsilon} i} \min\_{\forall k} \left| \mathbf{x}\_0^\circ(\mathbf{k}) - \mathbf{x}\_j^\circ(\mathbf{k}) \right| \tag{4}
$$

$$
\Delta \mathbf{x}\_{\text{max}} = \max\_{\forall i \in i} \max\_{\forall k} \left| \mathbf{x}\_0^\circ(\mathbf{k}) - \mathbf{x}\_j^\circ(\mathbf{k}) \right| \tag{5}
$$

$$
\Delta \mathbf{x}\_{0i}(\mathbf{k}) = \left| \mathbf{x}\_0^\circ(\mathbf{k}) - \mathbf{x}\_i^\circ(\mathbf{k}) \right| \tag{6}
$$

where *Δx*0*<sup>i</sup>*ð Þ k is the absolute value of the difference between the comparison sequence and the reference sequence and *ξ* is the distinguishing coefficient.


*Soil Erosion Influencing Factors in the Semiarid Area of Northern Shaanxi Province, China DOI: http://dx.doi.org/10.5772/intechopen.92979*

*Note: I5, 5-min maximum rainfall intensity; I10, 10-min maximum rainfall intensity; I15, 15-min maximum rainfall intensity; I30, 30-min maximum rainfall intensity; GRG, gray relational grade*

#### **Table 2.**

**2.6 Soil steady infiltration rate**

*Soil Moisture Importance*

**2.7 Data processing**

as below [65]:

where *x*, *i*

**124**

by Eq. (2) and *x*,

*i*

characteristic value for analysis.

measurements were observed 5–6 times [63].

1. The parameters are standardized using:

*x*, *i*

γ *x*,

ð Þ k are the original parameters. 2. Then, the correlation coefficient is calculated using:

> <sup>0</sup>ð Þ <sup>k</sup> k, *<sup>x</sup>*, *i*

*Δxmin* ¼ min

*Δxmax* ¼ max

∀*jϵi*

∀*jϵi*

*<sup>Δ</sup>x*0*<sup>i</sup>*ð Þ¼ <sup>k</sup> *<sup>x</sup>*,

sequence and the reference sequence and *ξ* is the distinguishing coefficient.

An instrument for recording the process of water infiltration into the soil was employed, and the depth of infiltration was calculated by the empirical equation:

where *H* is the depth of infiltration, *h* is the change in the standing water level, and *a* is the slope gradient. At the beginning of the experiment, data were recorded every 10 s for 90 s; then, data were recorded every 30 s for 5 min; at the end, data were recorded once every minute. The experiment was not completed until similar

We used the Principal Coordinates Analysis (PCoA) method to convert the qualitative variables, such as vegetation and slope aspect, into quantitative variables [64]. To reduce the error of the evaluation, we used PCoA for numerical transformation and calculate the characteristic value of each item, and then we used

The Chinese scholar Deng Julong first proposed the gray correlation method. This method is based on developmental trends of the degree of similarity or dissimilarity between factors. By comparing a sequence of an established family of curves and a reference sequence curve, using geometric similarity, one can determine the degree of connection between the reference sequence and a comparison sequence set of data. If the shapes are similar, then a greater degree of similarity is identified. The comparison sequence and the reference sequence include both temporal series and nontemporal series. Therefore, the gray correlation method provides a quantitative measurement for the development of a system. This method is highly suitable for the analysis of a dynamic process. The specific method is shown

ð Þ¼ <sup>k</sup> *xi*ð Þ� <sup>k</sup> *min xi*ð Þ <sup>k</sup>

ð Þ <sup>k</sup> <sup>¼</sup> *<sup>Δ</sup>xmin* <sup>þ</sup> *<sup>ε</sup>Δxmax*

min ∀*k x*,

max ∀*k x*,

where *Δx*0*<sup>i</sup>*ð Þ k is the absolute value of the difference between the comparison

 

> 

<sup>0</sup>ð Þ� <sup>k</sup> *<sup>x</sup>*, *i* ð Þ <sup>k</sup> 

ð Þ k are the new values obtained when the parameters are standardized

*Δx*0*<sup>i</sup>*ð Þþ *k εΔxmax*

<sup>0</sup>ð Þ� <sup>k</sup> *<sup>x</sup>*, *j* ð Þ k

<sup>0</sup>ð Þ� <sup>k</sup> *<sup>x</sup>*, *j* ð Þ k

*max xi*ð Þ� <sup>k</sup> *min xi*ð Þ <sup>k</sup> (2)

 

> 

(3)

(4)

(5)

(6)

*H* ¼ 0*:*19635 � *h* � cos *a* (1)

*Gray relational grade between runoff and sediment yield and the factors influencing runoff and sediment yield.*


*Note: I5, 5-min maximum rainfall intensity; I10, 10-min maximum rainfall intensity; I15, 15-min maximum rainfall intensity; I30, 30-min maximum rainfall intensity; GRG, gray relational grade*

#### **Table 3.**

*Gray relational grade between runoff and its influential factors.*

The value of *ξ* ranges from 0 to 1, but generally *ξ* = 0.5. γ *x*, <sup>0</sup>ð Þ <sup>k</sup> k, *<sup>x</sup>*, *i* ð Þ <sup>k</sup> � � is the correlation coefficient.

3. Lastly, the gray relational grade (GRG, *Γ*) is calculated using:

$$\Gamma = \frac{1}{n} \sum\_{k=1}^{n} \gamma \left( \mathbf{x}\_0^\circ(\mathbf{k}), \mathbf{x}\_i^\circ(\mathbf{k}) \right) \tag{7}$$

types at rainfall event. However, the *Pinus tabuliformis* was more obvious, especially

*Sediment yield trend with rainfall amounts in the study area of Wuqi County, Shaanxi Province, China.*

*Soil Erosion Influencing Factors in the Semiarid Area of Northern Shaanxi Province, China*

*DOI: http://dx.doi.org/10.5772/intechopen.92979*

*rhamnoides* + *Pinus tabuliformis* was still low at low slope gradient when contrasted with other vegetation types in runoff plots; *Hippophae rhamnoides* decreased. Comparing grassland and *Hippophae rhamnoides* + *Pinus tabuliformis* in the same slope gradient, we conclude that grass on a slope with a gradient >25° cannot take the initiative to configure arbors or shrubs and make it natural succession to grow. At same time, we suggest that some shrubs and arbors should be active configuration to enhance the effect of soil and water conservation at low slope gradient less than 25 degrees; and considering that soil and water losses in pure *Pinus tabuliformis* forest were greater in the early stage of afforestation, we especially recommend

**Figures 4** and **5** show that vegetation types and rainfall amount had large effects on runoff and sediment yield; however, the change rule was not obvious. This study demonstrated that runoff and sediment yield are not solely determined by rainfall amount or by any single factor but more likely by a combination of vegetation type, vegetation coverage, rainfall amount, rainfall duration, rainfall intensity (average and for specified time periods), soil bulk density, soil steady infiltration rate, slope aspect, and slope gradient. Therefore, this research used the gray correlation method to comprehensively analyze the factors that influence runoff and sediment

**3.2 The factors affecting runoff and sediment yield based on gray relational**

While selecting runoff and sediment yield as a reference sequence, multiple indicators were used as comparative sequences, including vegetation type, vegetation coverage, rainfall amount, rainfall duration, average rainfall intensity, rainfall intensity for specified times (I5, I10, I15, I30), soil bulk density, soil steady infiltration rate, slope aspect, and slope gradient. Then the gray relational grade was calculated for the reference and comparison sequences (**Table 2**). Scientists generally agree that if the gray relational grade is large, then a close relationship exists between the

in low rainfall intensity and long-duration rainfall events; *Hippophae*

*Hippophae rhamnoides* + *Pinus tabuliformis* mixed forests.

yield from multiple angles.

sequence and reference parameters.

**analysis**

**127**

**Figure 5.**

We selected runoff and sediment yield as the reference sequences; several indicators were used as comparative sequences, including vegetation type, vegetation coverage, rainfall amount, rainfall duration, average rainfall intensity (I5, I10, I15, I30), soil bulk density, soil steady infiltration rate, and slope aspect and gradient. Then, the gray relational grade was calculated for the reference and comparison sequences (**Tables 2** and **3**). Deng pointed out in his book that if the gray relational grade is large, then a close relationship exists between the sequence and reference parameters [65].
