2.3. Methodology for Calculating Ecological Building Potential
The potential for vegetation restoration in the Kuye River Basin is estimated based on the assumption that vegetation cover should be relatively uniform under similar habitat conditions. The potential for terrace restoration is assessed through an analysis of topographic features and soil layer thickness, while the potential for silt dam construction is evaluated by considering various hydrological and geomorphological factors. Based on these assessments, the overall potential for soil erosion control in the watershed is synthesized, providing a comprehensive understanding of target areas for ecological restoration within the Kuye River Basin (
Figure 2).
- (1)
Calculation of vegetation restoration potential
Vegetation distribution is influenced by various factors, including soil type, topography, and climate. Consequently, the Kuye River Basin was initially categorized into two distinct zones: the wind–sand zone and the loess hills and gullies zone. Each of these zones was further subdivided based on topographic features and precipitation patterns.
The Kuye River Basin, located in an arid and semi-arid zone, exhibits a strong correlation between precipitation and NDVI. Pearson correlation coefficients for precipitation and the NDVI were calculated for each meteorological station, revealing that most coefficients exceeded 0.3. Vegetation recovery within the watershed varied significantly with precipitation levels. In regions receiving less than 375 mm of precipitation, the NDVI change was 9.55%. For precipitation ranging from 375 to 575 mm, the NDVI change averaged 17.18%, with the most substantial change of 19.73% observed between 425 and 450 mm. In areas with precipitation exceeding 575 mm, the rate of vegetation recovery decreased to approximately 8.88%. Moisture conditions are the primary limiting factor for vegetation recovery in the Kuye River Basin. Inadequate moisture often leads to dry soil layers, low survival rates, and smaller tree sizes in areas receiving less than 450 mm of rainfall annually [
15].
Therefore, the annual precipitation levels were classified into 14 categories, as shown in
Table 1.
Terrain factors are categorized into four distinct classes based on slope and aspect, as outlined in
Table 2. On the Loess Plateau, the gully ridge line serves as the boundary. The area above the ridge is referred to as the inter-gully region, while the area below is the gully region. The inter-gully region primarily consists of slopes, typically with gradients of less than 15°, whereas the gully region is mainly composed of channels, with slopes generally exceeding 15°. Therefore, 15° was chosen as the threshold for slope delineation.
To generate a four-digit elevation code that integrates the encoded precipitation and terrain factor layers, the terrain factor code is first multiplied by 100, followed by the addition of the precipitation code. This method encodes the terrain characteristics in the thousands and hundreds digits, while the precipitation information is captured in the tens and units digits of the four-digit code. As a result, a single code effectively represents both raster features. Subsequently, vegetation cover statistics were computed for each zone and stand code, including the mean, 75th percentile, 90th percentile, and maximum values.
In habitats with similar conditions, vegetation cover should be relatively consistent [
16]. For instance, if the current maximum vegetation cover in a particular zone—characterized by uniform soil types, aridity index, and topographic features—is 0.9, it is reasonable to assume that the potential for vegetation restoration in this area also reaches 0.9. This suggests that all other areas within the zone where vegetation cover is less than 0.9 have the potential to achieve the maximum cover of 0.9. To mitigate potential statistical bias, the 90th percentile of vegetation cover was used as the estimate for vegetation restoration potential under specific land conditions. For cultivated lands, water bodies, and built-up areas with slopes of 0–5°, the vegetation indices were maintained as is. In cases where the vegetation cover exceeded the 90th percentile under current conditions, the existing value was utilized.
- (2)
Calculation method of terrace construction potential
The positioning and dimensions of terraces are predominantly influenced by topographical characteristics and soil strata thickness [
17,
18]. In the Kuye River Basin, the primary forms of erosion are gully erosion and wind-blown sand, with topography being the most significant factor due to the depth of the soil layer in the loess hill and gully areas. The Soil and Water Conservation Law of the People’s Republic of China stipulates that terracing is allowed on sloped farmland below 25°, with other regional standards of China also providing guidelines for terracing based on slope classifications of 5°, 10°, and 15°. Thus, this study categorized the terrace deployment potential into five distinct zones based on ground slope and land use conditions: The Level 1 potential areas are the hilly gully zone of the Kuye River Basin, with a ground slope of 0–5° and a land use type of dryland. The Level 2 potential areas are also the hilly gully zone of the Kuye River Basin, with a ground slope of 5–10° and a land use type of dryland. The Level 3 potential area is the hilly gully zone of the Kuye River Basin, with a ground slope of 10–15° and a land use type of dryland. The Level 4 potential area is the wind–sand area of the Kuye River Basin, with a ground slope of 0–5° and a land use type of dryland. The Level 5 potential area is the hilly gully area of the Kuye River Basin, with a ground slope of 15–25° and a land use type of dryland.
The identification of potential terrace zones is conducted using the overlay analysis module of ArcGIS 10.2 software. This analysis integrates inputs from a land use map, a slope classification map, and a governance zoning map. The overlay analysis is performed based on the specified analytical concepts, resulting in the final delineation of terrace potential zones.
- (3)
Calculation method for the construction potential of check dams
The primary factor influencing the construction of check dams is the soil erosion modulus. The calculation of this modulus is based on the Revised Universal Soil Loss Equation (RUSLE), with adjustments made to the relevant factors in the formula. It is further combined with calculations derived from ArcGIS software (
Figure 3). The calculation formula is as follows:
where A is the average annual soil loss, t×/(hm
2 × a); R is the rainfall erosivity factor, MJ × mm/(hm
2 × h × a); K is the soil erodibility factor, t × hm
2 × h/(hm
2 × MJ × mm); S is the slope factor; L is the slope length factor; C is the crop cover-management factor; and P is the factor for soil and water conservation measures.
The rainfall erosivity factor (R) was determined using the empirical formula proposed by Wischmeier et al. [
19,
20], which calculates the multi-year average rainfall erosivity based on monthly rainfall data. While this method is intended to be applied across the United States, many scholars have also used this formula to conduct relevant studies in the Loess Plateau region in recent years [
21,
22,
23,
24].
where
and
are the average annual and monthly rainfall, mm, respectively.
The multi-year average rainfall erosivity (R) value was derived from the collected monthly rainfall data for the study area using the appropriate formulae. This R value was then simulated using the semi-variance function in GS+ 7.0, where the Gaussian model was identified as the optimal fit. Subsequently, the rainfall erosivity factor for the Kuye River Basin was obtained by applying the Gaussian model with Kriging interpolation within the Geostatistics Module of ArcGIS.
The soil erodibility factor (K) was estimated using the methodology described in the Estimation of Soil Erosion and Productivity Impacts (EPIC) model, established by Williams [
25]. Yao used the EPIC model, along with the Shirazi and Torri formulas, to estimate and compare the K values for soil erodibility in typical small watersheds of the Loess Plateau [
26]. Yao found that the K values from the EPIC model fell within the range of observed values, suggesting that the EPIC model may be more suitable for typical small watersheds in the Loess Plateau. In recent years, many scholars have also applied this formula to the Loess Plateau [
27,
28]. This method takes into account soil organic matter and particle composition:
where SAN is the sand content, %; SIL is the silt content, %; CLA is the clay content, %; C is the organic carbon content, %; and SN
1 = 1 − SAN/100. Based on the soil type of the Kuye River Basin and its attribute data, the soil erodibility K value was calculated and obtained.
The Slope and Length Factor (LS) is calculated using the formula proposed by McCool et al. within the Revised Universal Soil Loss Equation (RUSLE):
where λ is the horizontal projected slope length, m; m is the variable slope length index; and θ is the slope gradient, °.
The Kuye River Basin was initially subdivided into eight sub-regions using the LS factor calculation tool developed by Zhang et al. [
29], based on 30 m DEM data. The LS factor values for each sub-region were calculated individually and then aggregated to derive the LS factor values for the entire Kuye River Basin.
The crop cover management factor (C) was defined for gently sloping arable land (below 5°) in the Cave Wild River area, where the predominant crops are maize and wheat, with a C value of 0.25 based on the studies by Zhang et al. [
30]. For sloping arable land with gradients above 5°, where legumes, potatoes, and grains are the primary crops, the C value is set at 0.40. The C values for paddy fields, water bodies, and built-up areas are assigned as zero, while unused land is assigned a C value of one. Jiao et al. [
31] and Zhang et al. [
32], through their research, concluded that when the effective vegetation cover in the Loess Plateau region exceeds 60%, soil erosion is significantly reduced. Based on this understanding, and drawing on the results from the RUSLE manual as well as the studies by Zhang et al. [
30], and Wang et al. [
33], the C values for various vegetation coverages were determined, as shown in
Table 3.
The factor representing soil and water conservation measures (P) has been reported to be as high as 0.12 for horizontal terraces in the Kuye River Basin, where it has been demonstrated that sand reduction benefits can reach up to 88%. In the absence of specific evidence for other land types, the value of P for these areas has been assigned a default value of 1.
This study integrates the relevant specifications for check dam construction with field research findings to determine the density of key check dams across various soil erosion moduli [
34,
35,
36]. The results are as follows: For a soil erosion modulus exceeding 15,000 t/(km
2 × a), the control area for key check dams is typically 3 km
2. For a soil erosion modulus ranging from 12,000 to 15,000 t/(km
2 × a), the control area is 4 km
2. For a soil erosion modulus between 10,000 and 12,000 t/(km
2 × a), the control area is 5 km
2. For a modulus of 8000 to 10,000 t/(km
2 × a), the control area is 6 km
2; for a modulus of 6000 to 8000 t/(km
2 × a), the control area is 7 km
2; and for a modulus below 6000 t/(km
2 × a), the control area is 8 km
2. The potential number of key check dams in each subarea can be calculated by dividing the area of each soil erosion zone by the respective key check dam control area.