*Article* **Water Supply Increases N Acquisition and N Resorption from Old Branches in the Leafless Shrub** *Calligonum caput-medusae* **at the Taklimakan Desert Margin**

**Caibian Huang 1,2,3,\* , Fanjiang Zeng 1,2,3, Bo Zhang 1,2,3 , Jie Xue 1,2,3 and Shaomin Zhang 4,\***


**\*** Correspondence: huangcaibian@ms.xjb.ac.cn (C.H.); zhangshaomin8698@126.com (S.Z.)

**Abstract:** Irrigation is the main strategy deployed to improve vegetation establishment, but the effects of increasing water availability on N use strategies in desert shrub species have received little attention. Pot experiments with drought-tolerant shrub *Calligonum caput-medusae* supplied with water at five field capacities in the range of 30–85% were conducted using local soil at the southern margin of the Taklimakan Desert. We examined the changes in plant biomass, soil N status, and plant N traits, and addressed the relationships between them in four- and seven-month-old saplings and mature shrubs after 28 months. Results showed that the growth of *C. caput-medusae* was highly responsive to increased soil moisture supply, and strongly depleted the soil available inorganic N pools from 16.7 mg kg−<sup>1</sup> to an average of 1.9 mg kg−<sup>1</sup> , although the total soil N pool increased in all treatments. Enhancement of biomass production by increasing water supply was closely linked to increasing total plant N pool, N use efficiency (NUE), N resorption efficiency (NRE), and proficiency (NRP) in four-month saplings, but that to total plant N pool, NRE, and NRP after 28 months. The well-watered plants had lower N concentrations in senesced branches compared to their counterparts experiencing the two lowest water inputs. The mature shrubs had higher NRE and NRP than saplings and the world mean levels, suggesting a higher N conservation. Structural equation models showed that NRE was largely controlled by senesced branch N concentrations, and indirectly affected by water supply, whereas NRP was mainly determined by water supply. Our results indicated that increasing water availability increased the total N uptake and N resorption from old branches to satisfy the N requirement of *C. caput-medusae*. The findings lay important groundwork for vegetation establishment in desert ecosystems.

**Keywords:** *Calligonum caput-medusae*; N resorption; water addition; soil inorganic N; biomass

## **1. Introduction**

Approximately 10% of drylands undergoes desertification, whereas occurring areas occupy approximately 20% of the dryland population [1]. Establishing vegetation is an important tool for controlling desertification and reducing erosion in desert ecosystems [2–4]. Irrigation is the primary intervention to improve the success of vegetation establishment in desert ecosystems with low precipitation and high evapotranspiration rates [5,6]. For example, shrub planting has been a crucial strategy in the Taklimakan desert highway shelterbelt project, which crosses the largest mobile desert in China and uses drip irrigation to support vegetation to reduce wind-blown sand that blocks the road [7]. In addition to water scarcity,

**Citation:** Huang, C.; Zeng, F.; Zhang, B.; Xue, J.; Zhang, S. Water Supply Increases N Acquisition and N Resorption from Old Branches in the Leafless Shrub *Calligonum caput-medusae* at the Taklimakan Desert Margin. *Water* **2021**, *13*, 3288. https://doi.org/10.3390/w13223288

Academic Editor: Guido D'Urso

Received: 27 September 2021 Accepted: 18 November 2021 Published: 20 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Urumqi 830091, China

nitrogen (N) limitation is another primary factor controlling plant growth in desert ecosystems [8], and they generally interact and affect plant growth. Therefore, understanding the N use strategies of artificial vegetation is crucial for the success of vegetation establishment and continuous irrigation programs in extremely arid land.

Soil moisture plays an important role in regulating N mineralization and soil N availability. Reports showed that increasing water availability increased N mineralization rate and N uptake and subsequently promoted plant growth, but reduced moisture and specific soil properties (such as high soil alkalinity), thereby limiting soil N availability [9–11]. Some studies have reported that low N deposition (<6 g Nm−<sup>2</sup> ·year−<sup>1</sup> ) improved the plant productivity under drought stress, but high N deposition did not [12,13]. Moreover, increases in soil N availability after N supplementation could improve plant growth and alleviate the negative effects of drought stress in arid land [14]. However, plants became mildly N constrained under sufficient moisture in the desert [15]. The latest research has shown that the annual N deposition was 0.4 g Nm−<sup>2</sup> ·year−<sup>1</sup> in desert ecosystems of northwest China [16]. However, whether N deposition is a potential approach to mitigate N limitation for the irrigated plants in the desert is uncertain. A number of studies have reported that nutrient resorption, which is the nutrient movement from senescing tissues back to surviving tissues [17], is important especially for plants growing in infertile soils [18–20]. Generally, two approaches can be used to assess nutrient resorption, namely, resorption efficiency and proficiency. Nutrient resorption efficiency quantifies the percent of conserved nutrients in young foliage or other live parts that are translocated from senesced tissues, and resorption proficiency measures the extent to which a nutrient is withdrawn from senescing tissues [19]. Through this nutrient resorption, plants are less dependent on soil nutrient pools to maintain or increase biomass and photosynthesis [20,21]. For example, N resorption in annual plants can provide approximately 31% of N demand [22]. In forests, 45–68% of growth may depend upon resorption [23]. Furthermore, N resorption impacts litter decomposition, nutrient cycling, and resource use efficiency [24–26], thereby affecting plant productivity and nutrient cycling processes. Therefore, clarifying the potential function of NR in artificial vegetation can evaluate their potential fitness and suitability for establishment in infertile and harsh environments such as in the margins of deserts.

The relationship between N resorption and soil fertility is complex given that negative, and positive correlations with soil nutrient availability have been reported [27,28]. The role of water availability on N resorption is also under debate because some studies found that N resorption efficiency (NRE) decreases with increasing soil water availability due to the enhancement of soil nutrient release [29,30]. However, drought can cause early onset of senescence [31], potentially increasing the importance of nutrient resorption [32]. However, nutrient resorption may be sensitive to drought limitation [33,34] due to the reduced nutrient retranslocation in the phloem and water recycling in the xylem [35]. Therefore, whether N resorption in shrubs in arid environments depends on water availability and whether growth stimulation by watering increases N resorption to satisfy increased plant N demand remains unclear.

Seedling establishment of woody species in harsh and extreme environmental conditions is the most vulnerable stage in vegetation establishment [36,37]. Several studies have reported that N limitation at the seedling stage restricted vegetative growth [38], and subsequent vigorous seedling establishment [39]. Moreover, mature trees tend to be more efficient in N-recycling than younger ones [23]. Therefore, N resorption, as an important N recycling strategy, is reported to change over time [40], and its resorption efficiency is likely to increase when plant-available N is limited [21]. Thus, we hypothesize that the seedlings of woody species would have a lower N resorption than adults in a nutrient-poor environment.

We investigated N uptake and utilization including resorption in the drought-tolerant shrub *Calligonum caput-medusae* under different irrigation conditions during seedling establishment in the southern margin of the Taklimakan Desert, Xinjiang, China. Soils in this area are highly weathered and strongly leached, leading to very low nutrient concentrations [41]. *C. caput-medusae* is a perennial C4 plant belonging to Polygonaceae. Its foliage is reduced, making the assimilating green branches the primary photosynthetic organs. It continues to produce new green branches and shed old branches throughout the entire growing season. In addition, the older parts of the green branches of its seedlings become lignified, but more biomasses are allocated to the stem wood as the plant grows. We analyze how seedlings and 28-month-old well-established *C. caput-medusae* plants satisfy their N demand under different water regimes. We compare the effects of water addition on plant growth and N use characteristics to determine the relationships between water availability and N absorption as well as the utilization in seedling and established plants with senescent and green branches of the woody leafless shrub.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The study was carried out in Cele Oasis which is located at the southern margin of the Taklimakan Desert in southern Xinjiang, China (Figure 1). The oasis has a typical arid continental climate with an annual mean temperature of 11.9 ◦C, mean annual precipitation of less than 40 mm (mainly occurring in May and July), and evaporation of approximately 2600 mm. Temperature ranges from 42 ◦C in summer to −24 ◦C in winter. The average annual wind speed is 1.9 m·s −1 , and the maximum speeds in excess of 20.0 m·s <sup>−</sup><sup>1</sup> occur on more than 40 days per year. The frost-free period lasts 209 days per year. The soil is classified as aeolian sandy soil and irrigated desert soil according to the Chinese Soil Taxonomy; they are equivalent to Entisols and Inceptisols in the U.S. Soil Taxonomy, respectively. The Cele Oasis is surrounded by a 5 to 10 km belt of sparse vegetation (5–20% coverage) dominated by *Alhagi sparsifolia*, *Karelinia caspica* and *Tamarix ramosissima*. *Water* **2021**, *13*, 3288 4 of 16

**Figure 1.** Study site at the southern margin of the Taklimakan Desert in southern Xinjiang, China. **Figure 1.** Study site at the southern margin of the Taklimakan Desert in southern Xinjiang, China.

#### *2.2. Experimental Design 2.2. Experimental Design*

The pot experiment was initiated in April 2011 in an isolated and enclosed natural site to avoid disturbance. The experimental soil (0–40 cm) was collected from an oasis-desert ecotone, was air-dried, and passed through a 2 mm sieve. The The pot experiment was initiated in April 2011 in an isolated and enclosed natural site to avoid disturbance. The experimental soil (0–40 cm) was collected from an oasisdesert ecotone, was air-dried, and passed through a 2 mm sieve. The characteristics of

characteristics of the experimental soil are shown in Table 1. A total of 85 kg soil was placed in each of the 150 plastic pots (40 cm inner diameter at the bottom, 50 cm inner

each pot and two layers of nylon mesh (0.25 mm) were placed over the holes to prevent root growth out of the pot, but aeration and drainage were. The pots were set in soil to provide thermal buffering with the top edge of each pot extending 3 cm above the ground. A plastic plate was placed on the bottom of each pot to eliminate water transfer into the surrounding soil. Healthy seeds of *C*. *caput-medusae* with similar sizes were collected in the autumn of 2010, and eight seeds were sown into each pot, and each seed was placed in 2 cm deep holes 5 cm apart on 5 April 2011. For the initial seedling establishment, all pots were well watered (soil moisture was approximately 80% FC) to ensure seed germination. When the one-month seedlings were thinned to one plant per pot, and different water treatments were then initiated. The shoots and roots of the 20 thinned seedlings were harvested and oven-dried at 75 °C, and then weighed to obtain

the total biomass of the one-month-old seedlings with 2.4 g plant−<sup>1</sup> on average.

the experimental soil are shown in Table 1. A total of 85 kg soil was placed in each of the 150 plastic pots (40 cm inner diameter at the bottom, 50 cm inner diameter at the top edge, and 60 cm tall), to a bulk density of 1.4 g·cm−<sup>3</sup> . The soil field capacity (FC) in the pots was 18%. Seven uniform holes had been drilled in the bottom of each pot and two layers of nylon mesh (0.25 mm) were placed over the holes to prevent root growth out of the pot, but aeration and drainage were. The pots were set in soil to provide thermal buffering with the top edge of each pot extending 3 cm above the ground. A plastic plate was placed on the bottom of each pot to eliminate water transfer into the surrounding soil. Healthy seeds of *C*. *caput-medusae* with similar sizes were collected in the autumn of 2010, and eight seeds were sown into each pot, and each seed was placed in 2 cm deep holes 5 cm apart on 5 April 2011. For the initial seedling establishment, all pots were well watered (soil moisture was approximately 80% FC) to ensure seed germination. When the one-month seedlings were thinned to one plant per pot, and different water treatments were then initiated. The shoots and roots of the 20 thinned seedlings were harvested and oven-dried at 75 ◦C, and then weighed to obtain the total biomass of the one-month-old seedlings with 2.4 g plant−<sup>1</sup> on average. *Water* **2021**, *13*, 3288 5 of 16

**Table 1.** Basic soil characteristics before the start of the experiment. **Table 1.** Basic soil characteristics before the start of the experiment.


The pots were completely randomized and allocated to five water treatments (Figure 2), namely, water-stressed (30% and 40% FC), moderately-watered (50% and 60% FC), and wellwatered (85% FC) with 30 replicates for each water level (10 per harvest). During the experimental period, volumetric soil water contents in four randomly selected replicate pots of each water level were measured using a moisture meter type TDR 300 (soil moisture equipment, Santa Barbara, CA, USA) every other day at 20:00. The amount of water to add to each pot was calculated according to the average of four measured pots under each water treatment and enabled the soil volumetric water content to be maintained at 5.4%, 7.2%, 9.0%, 10.8%, and 15.3%, and under 30%, 40%, 50%, 60%, and 85% FC water supply regimes, respectively. The water added to the plants was obtained from the local well without purification. We ignored the N effect from the well water because it contained little nitrate-N (2.92 mg·L −1 ) and no ammonium-N. The water treatments were stopped at the end of the growing season (mid-November) and were re-started at the beginning of the next growing season (mid-April). The experiment was conducted for three years. (Figure 2), namely, water-stressed (30% and 40% FC), moderately-watered (50% and 60% FC), and well-watered (85% FC) with 30 replicates for each water level (10 per harvest). During the experimental period, volumetric soil water contents in four randomly selected replicate pots of each water level were measured using a moisture meter type TDR 300 (soil moisture equipment, Santa Barbara, CA, USA) every other day at 20:00. The amount of water to add to each pot was calculated according to the average of four measured pots under each water treatment and enabled the soil volumetric water content to be maintained at 5.4%, 7.2%, 9.0%, 10.8%, and 15.3%, and under 30%, 40%, 50%, 60%, and 85% FC water supply regimes, respectively. The water added to the plants was obtained from the local well without purification. We ignored the N effect from the well water because it contained little nitrate-N (2.92 mg·L−1) and no ammonium-N. The water treatments were stopped at the end of the growing season (mid-November) and were re-started at the beginning of the next growing season (mid-April). The experiment was conducted for three years.

**Figure 2.** Experimental pots and growth status of *C. caput-medusae* after 2 months of water treatment. **Figure 2.** Experimental pots and growth status of *C. caput-medusae* after 2 months of water treatment.

harvest time, four individual plants of each water treatment were randomly selected and separated to stem, branches, and roots. For each individual plant, all senesced but still attached branches were collected from each individual and combined into one sample per pot. All plant samples were oven-dried at 75 °C for 48 h and then weighed. Soil samples from 0–15, 15–30, and 30–45 cm depths in each pot were collected after the plant sampling. In each pot, three 2 cm-diameter soil cores were sampled by hand-auger and combined as a single composite sample. All fresh soil samples were sieved through a 2 mm mesh sieve to remove roots and stones and then divided into two subsamples.

*2.3. Sample Collection*

#### *2.3. Sample Collection*

Plants were harvested three times from seedling to mature stages (Table 2). At each harvest time, four individual plants of each water treatment were randomly selected and separated to stem, branches, and roots. For each individual plant, all senesced but still attached branches were collected from each individual and combined into one sample per pot. All plant samples were oven-dried at 75 ◦C for 48 h and then weighed. Soil samples from 0–15, 15–30, and 30–45 cm depths in each pot were collected after the plant sampling. In each pot, three 2 cm-diameter soil cores were sampled by hand-auger and combined as a single composite sample. All fresh soil samples were sieved through a 2 mm mesh sieve to remove roots and stones and then divided into two subsamples.



#### *2.4. Laboratory Analysis*

All plant samples were ground with a ball mill and then analyzed for the total N concentration by the Kjeldahl acid-digestion method [42] with an Alpkem auto analyzer (Kjeltec System 8400 distilling unit, Foss, Copenhagen, Denmark). The N concentrations were expressed on a mass basis. One soil subsample (10 g) was freshly extracted with 50 mL of 0.01 M CaCl and analyzed for NO<sup>3</sup> − and NH<sup>4</sup> <sup>+</sup> with a continuous flow analysis system (SEAL Analytical, Norderstedt, Germany). The other soil subsample was air-dried, passed through a 0.25 mm mesh, and then analyzed for soil characteristics. Soil total N concentration was determined by the semimicro Kjeldahl method [43]. Soil pH was determined with a 1:5 soil/water suspension; soil bulk density was measured by the soil core method; soil organic matter was determined by wet oxidation; the total phosphorus (P) was determined after digestion with spectrophotometer detection; soil-available P was extracted with 0.5 M NaHCO<sup>3</sup> solution and measured by colorimetric detection; soil total potassium (K) and available K were determined using a flame photometer [44].

#### *2.5. Calculation*

Individual plant biomass was calculated as the sum of the biomass for each organ (stem, branches, and roots). The total N uptake of each pot was calculated as the sum of individual organ N pools, where individual organ N pool was calculated by multiplying biomass (g·plant−<sup>1</sup> ) and its N concentration (mg·g −1 ). The N use efficiency (NUE) was calculated as the ratio of total biomass to total N mass in the whole plant [45]. Nitrogen resorption efficiency (NRE) was calculated as NRE = (1 − Nsenesced/Ngreen) × 100%, where Nsenesced and Ngreen are the N concentrations in senesced and green branches, respectively [19]. We used the reciprocal of Nsenesced to calculate N resorption proficiency (NRP), where a lower N concentration in senesced tissue corresponds to a higher proficiency [19].

Relative growth rate (RGR) was calculated as the increase in biomass over time: RGR = (log10M<sup>f</sup> − log10M<sup>i</sup> )/(t<sup>f</sup> − t<sup>i</sup> ), where M<sup>i</sup> and M<sup>f</sup> are the total individual biomass at the end of the fourth month and the first month, the seventh month and the fourth month, and the twenty-eighth month and the seventh month.

#### *2.6. Statistical Analysis*

One-way analysis of variance (ANOVA) was used to assess the effects of different water treatments on plant biomass, RGR, total N pool, NRE, NUE, NRP, N concentration, and soil N. Means were compared by Duncan's tests where ANOVA showed a significant difference. Two-way ANOVA was used to test the effects of plant age and water addition on soil N and plant N parameters. Regression analyses were used to determine the relationships among plant biomass, soil inorganic N, and plant N traits within each growth

age. All the analyses were carried out with SPSS 16.0 (SPSS Inc., Chicago, IL, USA). We used the package "piecewise structural equation modeling" (piecewise SEM) to analyze the direct and indirect influences of water and plantation age on N concentrations in green and senesced branches, soil inorganic N, and plant biomass with R software (version 4.0.3) [46]. D-separation test of piecewise SEM was used to verify whether the causal model has important links, and *p* > 0.05 indicates the fitness of the model [47]. *Water* **2021**, *13*, 3288 7 of 16 **3. Results and Discussion**

#### **3. Results and Discussion** *3.1. Soil N status* Average soil inorganic N concentration decreased from 16.71 mg·kg−<sup>1</sup> to 3.43, 2.55

#### *3.1. Soil N status* and 1.9 mg·kg−<sup>1</sup> after water treatment for four months, seven months, and twenty-eight

Average soil inorganic N concentration decreased from 16.71 mg·kg−<sup>1</sup> to 3.43, 2.55 and 1.9 mg·kg−<sup>1</sup> after water treatment for four months, seven months, and twenty-eight months, respectively (Figure 3a). Soil inorganic N concentrations under four- and seven-month treatments decreased with increasing water addition and reached the low values at 28 months. However, no relationship was found between water addition rate and soil inorganic N concentration (Table 3). This finding is consistent with several previous findings that soil N availability decreased as juvenile stands begin to mature [23,48]. However, soil total N concentration increased with plantation age from 0.09 g·kg−<sup>1</sup> (initial value) to an average of 0.15 g·kg−<sup>1</sup> (28 months) (Figure 3b). Water addition significantly increased soil total N concentration under 50% FC and 60% FC water treatments at 28 months, but no effects were found in four- and seven-month treatments. Therefore, some studies reported that the decline in soil inorganic N may be related to the slow release of available N through litter decomposition, N mineralization, and nitrification [49,50]. months, respectively (Figure 3a). Soil inorganic N concentrations under four- and seven-month treatments decreased with increasing water addition and reached the low values at 28 months. However, no relationship was found between water addition rate and soil inorganic N concentration (Table 3). This finding is consistent with several previous findings that soil N availability decreased as juvenile stands begin to mature [23,48]. However, soil total N concentration increased with plantation age from 0.09 g·kg−<sup>1</sup> (initial value) to an average of 0.15 g·kg−<sup>1</sup> (28 months) (Figure 3b). Water addition significantly increased soil total N concentration under 50% FC and 60% FC water treatments at 28 months, but no effects were found in four- and seven-month treatments. Therefore, some studies reported that the decline in soil inorganic N may be related to the slow release of available N through litter decomposition, N mineralization, and nitrification [49,50].

**Figure 3.** Soil inorganic N (**a**) and total N (**b**) concentrations in response to different water treatments at three growth stages (4 months, 7 months and 28months). Values are shown as the means ± se (*n* = 4). Bars with different lowercase letters indicate significant differences among treatments at the same growth stage at *p* < 0.05. **Figure 3.** Soil inorganic N (**a**) and total N (**b**) concentrations in response to different water treatments at three growth stages (4 months, 7 months and 28months). Values are shown as the means ± se (*n* = 4). Bars with different lowercase letters indicate significant differences among treatments at the same growth stage at *p* < 0.05.



N use efficiency 0.491 \* 0.514 \* −0.879 \*\*

N resorption efficiency 0.766 \*\* 0.053 0.665 \*\* Notes: \* *p* < 0.05; \*\* *p* < 0.01.

Notes: \* *p* < 0.05; \*\* *p* < 0.01.

#### *3.2. Plant Biomass and Relative Growth Rate* Individual plant biomass and RGR were significantly affected by plant age, water

*3.2. Plant Biomass and Relative Growth Rate*

Individual plant biomass and RGR were significantly affected by plant age, water treatments, and their interaction (Table 4). The biomass increased significantly with ages and increasing water supply markedly enhanced this trend at three growth stages (Figure 4a), suggesting that *C. caput-medusae* showed strong adaptability to the decline in soil available N. This finding was consistent with the results in arid and semi-arid areas, indicating that water availability was positively correlated with productivity [30,51]. Increased water availability could directly stimulate plant physiological processes, and consequently increase net carbon uptake [51,52], resulting in high biomass accumulation. Nonetheless, the RGR decreased with plant age, at 0.35, 0.22, and 0.05 for four-, seven-, and 28-month-old saplings, respectively (Figure 4b). The RGR was also improved with an increasing water supply at three growth stages. However, the fourth- and seven-month-old plants grew much faster than the 28-month-old plants. treatments, and their interaction (Table 4). The biomass increased significantly with ages and increasing water supply markedly enhanced this trend at three growth stages (Figure 4a), suggesting that *C. caput-medusae* showed strong adaptability to the decline in soil available N. This finding was consistent with the results in arid and semi-arid areas, indicating that water availability was positively correlated with productivity [30,51]. Increased water availability could directly stimulate plant physiological processes, and consequently increase net carbon uptake [51,52], resulting in high biomass accumulation. Nonetheless, the RGR decreased with plant age, at 0.35, 0.22, and 0.05 for four-, seven-, and 28-month-old saplings, respectively (Figure 4b). The RGR was also improved with an increasing water supply at three growth stages. However, the fourth- and seven-month-old plants grew much faster than the 28-month-old plants. **Table 4.** Results (*F* value) of two-way ANOVA on the effects of water supply (W), plantation ages (A) and their

**Table 4.** Results (*F* value) of two-way ANOVA on the effects of water supply (W), plantation ages (A) and their interactions on soil N, plant biomass and plant N traits. interactions on soil N, plant biomass and plant N traits. **Treatments Ninorganic Ntotal Biomass RGR N pool Ngreen Nsenesced NRE NUE**

*Water* **2021**, *13*, 3288 8 of 16


Notes: Ninorganic and Ntotal correspond to soil inorganic and total N concentrations; RGR and N pool correspond to relative growth rate and whole plant N pool; Ngreen and Nsenesced correspond to N concentrations in green and senesced branches; NRE, and NUE correspond to N resorption efficiency and N use efficiency; \*\*\* *p* < 0.001. NRE, and NUE correspond to N resorption efficiency and N use efficiency; \*\*\* *p* < 0.001.

**Figure 4.** Changes in individual plant biomass (**a**) and relative growth rate (**b**) of *Calligonum caput-medusae* under different water treatments at three growth stages (4 months, 7 months, and 28 months). Values are shown as the means ± se (*n* = 4). Bars with different lowercase letters indicate significant differences among treatments at the same growth **Figure 4.** Changes in individual plant biomass (**a**) and relative growth rate (**b**) of *Calligonum caput-medusae* under different water treatments at three growth stages (4 months, 7 months, and 28 months). Values are shown as the means ± se (*n* = 4). Bars with different lowercase letters indicate significant differences among treatments at the same growth stage at *p* < 0.05.

#### *3.3. Plant N Status*

stage at *p* < 0.05.

*3.3. Plant N Status* Water addition, plant age, and their interactions had significant effects on N concentrations in green and senesced branches (Table 4, Figure 5b,c). Plant age was the dominant factor in determining N concentrations. Average green branch N concentration among different water treatments decreased from 14.7 mg·g−<sup>1</sup> at 4 months old to 11.5 mg·g−<sup>1</sup> at 28 months old; these findings are lower than the average N concentration of terrestrial plant species (18.6 g·kg−1) based on a global study [53]. Senesced branch N concentrations also decreased with plant age, with mean values of Water addition, plant age, and their interactions had significant effects on N concentrations in green and senesced branches (Table 4, Figure 5b,c). Plant age was the dominant factor in determining N concentrations. Average green branch N concentration among different water treatments decreased from 14.7 mg·g <sup>−</sup><sup>1</sup> at 4 months old to 11.5 mg·<sup>g</sup> <sup>−</sup><sup>1</sup> at 28 months old; these findings are lower than the average N concentration of terrestrial plant species (18.6 g·kg−<sup>1</sup> ) based on a global study [53]. Senesced branch N concentrations also decreased with plant age, with mean values of 5.3, 4.9, and 2.6 mg·g <sup>−</sup><sup>1</sup> at four-, sevenand 28-month-old, respectively. These results were lower than the critical value (7 mg·g −1 ) reported by Killingbeck [19], implying that the senesced branch N was resorbed almost completely which leads to low litterfall N return to the soil. N was the limiting nutrient for

the growth of *C. caput-medusae*, and the limitation even became severe for mature plants, considering our results. limitation even became severe for mature plants, considering our results.

5.3, 4.9, and 2.6 mg·g−<sup>1</sup> at four-, seven- and 28-month-old, respectively. These results

the senesced branch N was resorbed almost completely which leads to low litterfall N return to the soil. N was the limiting nutrient for the growth of *C. caput-medusae*, and the

) reported by Killingbeck [19], implying that

*Water* **2021**, *13*, 3288 9 of 16

were lower than the critical value (7 mg·g−<sup>1</sup>

**Figure 5.** Nitrogen pools of total plant (**a**), N concentration in green (**b**) and senesced (**c**) branches, and N resorption efficiency (**d**) and N use efficiency (**e**) of *Calligonum caput-medusae* in response to different water treatments at three growth stages (4-month-old, 7-month-old, and 28-month-old). Values are shown as the means ± se (*n* = 4). Bars with different lowercase letters indicate significant differences among treatments at the same growth stage at *p* < 0.05. **Figure 5.** Nitrogen pools of total plant (**a**), N concentration in green (**b**) and senesced (**c**) branches, and N resorption efficiency (**d**) and N use efficiency (**e**) of *Calligonum caput-medusae* in response to different water treatments at three growth stages (4-month-old, 7-month-old, and 28-month-old). Values are shown as the means ± se (*n* = 4). Bars with different lowercase letters indicate significant differences among treatments at the same growth stage at *p* < 0.05.

Effects of water addition on green branch N concentration varied with plant ages. Increasing water addition significantly reduced green branch N concentrations of fourand seven-month-old plants but increased that of the 28-month-old plants. This finding is different from the observation in the shallow-rooted annuals and deep-rooted shrubs in the Gurbantunggut Desert, where water addition showed no effects on their green leaf N concentration [15]. Our study further found that plant N pool increased greatly with water addition rates (Figure 5a), suggesting that enhanced water supply significantly increased

plant N uptake from soil [30]. According to these findings, we speculate that the increase in plant biomass may be higher than that of plant N pool with increasing water addition. This finding leads to the dilution effect of biomass on N content in green branches at the seedling stage, but it was inverse at the mature growth stage. This result was evidenced by the plant N pool at 28-month-age that responded more strongly to water addition than at four- and seven-month-age, showing an increase of 30.6 times at 85% FC water treatment relative to that at 30% FC water treatment. Different from green branches, N concentration in senesced branches was significantly and negatively correlated with water addition rates at all three plant growth ages. On the contrary, the NRP (the reciprocal of senesced branch N concentration) increased with increasing water addition. The decline in senesced branch N concentrations may lead to a decrease in N return to the soil.

#### *3.4. N Resorption and Utilization*

Non-parasitic plants have mainly two pathways for non-parasitic plants to acquire nutrients for new tissue production, as follows: root uptake from the soil, and mobilizing and withdrawing from old organs. The maintenance of N requirements for *C. caput-medusae* seedlings may be achieved through the pathways at the same time, as evidenced by the sharp decline of soil inorganic N and high NRE. The NRE of four- and seven-month-old plants was lower than that of 28-month-old plants, with mean values of 64.4%, 58.1%, and 75.5% (Figure 5d). The NRE of mature plants was higher than the global mean value (62%) [54]. Sun et al. [23] and Han et al. [55] suggested that plants mainly depended on N resorption with increasing limitation of soil available N. Thus, the mature *C. caput-medusae* may have changed its N acquisition process and depended less on root N uptake.

A significant positive relationship was found between NRE and water addition rates for four- and 28-month-old plants, but not for seven-month-old plants (Table 3), resulting in a significant interaction. In addition, the response of NRP to water addition was more significant than that of NRE, further confirming NRP. Thus, N levels in senesced branches were more sensitive for testing plant internal N cycling [27,56]. This finding suggested that enhanced soil water availability could improve plant's dependence on resorption-derived N due to the increasing limitation of soil available N. N use efficiency was also affected by water addition, plant age, and their interaction (Table 4). The NUE increased with plant age, with mean values of 120.5, 189.0, and 318.2 g·g −1 for four-, seven-, and 28-month-old plants, respectively (Figure 5e). Reports showed that plants could use limit N more efficiently with increasing water availability [57,58]. However, our results showed that water addition increased NUE of the four- and seven-month-old plants but decreased that of 28-month-old plants. This finding may be due to the mature plants that changed their N use strategies.

#### *3.5. Controlling Factors of Plant Growth and N Utilization as Well as Corelationships between Them*

Soil inorganic N concentration was positively correlated with green branch N concentrations at all three plant growth ages, but only related to senesced branch N concentrations at four months (Figure 6). Correspondingly, soil inorganic N was negatively correlated with NRE for four-month-old plants, but this finding was not found at other stages. This result may be related to the calculation of NRE based on the percent changes in green and senesced branch N concentrations. Our SEM result showed that green branch N concentrations were mainly determined by soil inorganic N concentration with indirect regulation by plant age and water addition (Figure 7), whereas soil inorganic N concentration and senesced branch N concentrations were mainly determined by water addition. The NRE was largely determined by senesced branch N concentrations, descendingly by green branch N and soil inorganic N concentrations. This finding suggested that the green branch N concentration could directly reflect the response of soil N availability to water addition during the establishment of *C. caput-medusae*. The plant NRE was not always closely related to the changes in green branch N concentrations and soil N availability. The indirect impact of water addition (via changes in senesced branch N concentrations) is an important driver that changes the N resorption of *C. caput-medusae*.

percent changes in green and senesced branch N concentrations. Our SEM result showed that green branch N concentrations were mainly determined by soil inorganic N concentration with indirect regulation by plant age and water addition (Figure 7), whereas soil inorganic N concentration and senesced branch N concentrations were mainly determined by water addition. The NRE was largely determined by senesced branch N concentrations, descendingly by green branch N and soil inorganic N concentrations. This finding suggested that the green branch N concentration could directly reflect the response of soil N availability to water addition during the establishment of *C. caput-medusae*. The plant NRE was not always closely related to the changes in green branch N concentrations and soil N availability. The indirect impact of water addition (via changes in senesced branch N concentrations) is an important driver

that changes the N resorption of *C. caput-medusae*.

**Figure 6.** Relationships between N concentrations in green and senesced branches, N resorption efficiency, N use efficiency, individual plant biomass, and soil inorganic N. There was no correlation between soil inorganic N and individual plant biomass at 28 months old (R<sup>2</sup> = 0.01), and thus not displayed in the plots. Note: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001; ns indicates no **Figure 6.** Relationships between N concentrations in green and senesced branches, N resorption efficiency, N use efficiency, individual plant biomass, and soil inorganic N. There was no correlation between soil inorganic N and individual plant biomass at 28 months old (R<sup>2</sup> = 0.01), and thus not displayed in the plots. Note: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001; ns indicates no significant. *Water* **2021**, *13*, 3288 12 of 16

significant.

**Figure 7.** Controlling factor analysis of N resorption efficiency and individual plant biomass using the structural equation model. Solid and dashed lines indicate significant (*p* < 0.05) and non-significant (*p* > 0.05) regressions. Blue and red arrows represent positive and negative relationships, respectively. W, water; B, individual plant biomass; SIN, soil inorganic N concentration; Ng, N concentrations in green branches; Ns, N concentrations in senesced branches; NRE, N resorption efficiency. (**a**) controlling factor analysis of individual plant biomass; (**b**) **Figure 7.** Controlling factor analysis of N resorption efficiency and individual plant biomass using the structural equation model. Solid and dashed lines indicate significant (*p* < 0.05) and non-significant (*p* > 0.05) regressions. Blue and red arrows represent positive and negative relationships, respectively. W, water; B, individual plant biomass; SIN, soil inorganic N concentration; Ng, N concentrations in green branches; Ns, N concentrations in senesced branches; NRE, N resorption efficiency. (**a**) controlling factor analysis of individual plant biomass; (**b**) controlling factor analysis of N resorption efficiency.

three measured times. Therefore, the plant biomass was directly affected by senesced branch N and soil inorganic N concentrations (Figure 7). Our SEM result also showed that water addition played an important direct role in driving plant biomass during the period of seedling establishment of *C. caput-medusae*. The water supply could—both

Individual plant biomass was significantly and negatively correlated with soil

Accordingly, we found that plant biomass was positively related to NUE for young plants, but negatively related between them for mature plants (Figure 8). However, plant biomass was positively correlated with NRE at four and 28 months but positively related to NRP at three growth stages. This result indicated that plant biomass and N acquisition were maintained by increasing NUE and N resorption at the seedling stage. Water addition also showed positive effects on these processes. Thus, this result also provided evidence for our finding that plant N acquirement depended on soil N and resorbed N for the saplings. The increasing biomass production of mature plants may be closely related to the high N resorption ability, which may explain the decrease in NUE with water supply. This finding was supported by previous studies, where plants with high productivity had high N resorption [23,59]; thus, their dependence on soil-available

controlling factor analysis of N resorption efficiency.

N supply was reduced.

directly and indirectly—regulate plant growth at the same time.

Individual plant biomass was significantly and negatively correlated with soil inorganic N and green branch N concentrations for the four- and seven-month-old plants, but it was not found for 28-month-old plants (Figure 8). Senesced branch N concentration was strongly and negatively related with individual plant biomass at all three measured times. Therefore, the plant biomass was directly affected by senesced branch N and soil inorganic N concentrations (Figure 7). Our SEM result also showed that water addition played an important direct role in driving plant biomass during the period of seedling establishment of *C. caput-medusae*. The water supply could—both directly and indirectly—regulate plant growth at the same time. *Water* **2021**, *13*, 3288 13 of 16

**Figure 8.** Relationships between N concentration in green and senesced branches, N resorption efficiency, N use efficiency, and individual plant biomass of *Calligonum caput-medusae*. Note: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001; ns indicates no significant. **Figure 8.** Relationships between N concentration in green and senesced branches, N resorption efficiency, N use efficiency, and individual plant biomass of *Calligonum caput-medusae*. Note: \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001; ns indicates no significant.

**4. Conclusions** Our results showed that the limitation of soil available N became increasingly serious with plant growth and was exacerbated by water addition. It appears to be an N depletion process for the seedling establishment of *C. caput-medusae*. However, the plants showed strong adaptability to N limitation and satisfied their N requirement by increasing plant N pool, NUE, and N resorption at the seedling stage, but mainly depended on N resorption at the mature stage. Our SEM showed that the individual plant biomass was largely determined by plant age and water addition, and subsequently by soil inorganic N and senesced branch N concentrations which were regulated by plant age and water addition. Enhanced water supply significantly Accordingly, we found that plant biomass was positively related to NUE for young plants, but negatively related between them for mature plants (Figure 8). However, plant biomass was positively correlated with NRE at four and 28 months but positively related to NRP at three growth stages. This result indicated that plant biomass and N acquisition were maintained by increasing NUE and N resorption at the seedling stage. Water addition also showed positive effects on these processes. Thus, this result also provided evidence for our finding that plant N acquirement depended on soil N and resorbed N for the saplings. The increasing biomass production of mature plants may be closely related to the high N resorption ability, which may explain the decrease in NUE with water supply. This finding was supported by previous studies, where plants with high productivity had high N resorption [23,59]; thus, their dependence on soil-available N supply was reduced.

improved plant N uptake from soil and negatively affected soil available N. Water addition mainly promoted NRE by reducing senesced branch N concentrations to

*caput-medusae*. Our findings provide a better insight to understand the N adaptive responses to irrigation and lay the groundwork for the vegetation establishment in the hyper-arid ecosystem. Future research is required to explore whether the resorption-derived process can satisfy plant N requirement for a longer time in the study

area.

#### **4. Conclusions**

Our results showed that the limitation of soil available N became increasingly serious with plant growth and was exacerbated by water addition. It appears to be an N depletion process for the seedling establishment of *C. caput-medusae*. However, the plants showed strong adaptability to N limitation and satisfied their N requirement by increasing plant N pool, NUE, and N resorption at the seedling stage, but mainly depended on N resorption at the mature stage. Our SEM showed that the individual plant biomass was largely determined by plant age and water addition, and subsequently by soil inorganic N and senesced branch N concentrations which were regulated by plant age and water addition. Enhanced water supply significantly improved plant N uptake from soil and negatively affected soil available N. Water addition mainly promoted NRE by reducing senesced branch N concentrations to maintain plant productivity over the period of seedling establishment of *C. caput-medusae*. Our findings provide a better insight to understand the N adaptive responses to irrigation and lay the groundwork for the vegetation establishment in the hyper-arid ecosystem. Future research is required to explore whether the resorption-derived process can satisfy plant N requirement for a longer time in the study area.

**Author Contributions:** Writing—original draft preparation, C.H.; writing—review and editing, C.H., F.Z. and S.Z.; supervision, S.Z.; project administration, C.H. and F.Z.; methodology, S.Z. and B.Z.; formal analysis, J.X.; software and analysis, S.Z. and J.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Key Laboratory Project of Xinjiang Uygur Autonomous Region (E0310113), the Original Innovation Project of the Basic Frontier Scientific Research Program, Chinese Academy of Sciences (ZDBS-LY-DQC031), the National Natural Science Foundation of China (42071259), and Natural Science Foundation of Xinjiang Uygur Autonomous Region (2021D01E01).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data reported here is available from the authors upon request.

**Acknowledgments:** We would like to thank Jonathan R Leake for his helpful suggestions to improve the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Hua Zhang 1,2,\* , Jinping Lei <sup>1</sup> , Hao Wang <sup>3</sup> , Cungang Xu <sup>1</sup> and Yuxin Yin <sup>1</sup>**


**Abstract:** The North and South Mountains of Lanzhou City are the ecological protection barriers and an important part of the ecological system of Lanzhou City. This study takes the North and South Mountains as the study area, calculates the soil erosion modulus of the North and South Mountains of Lanzhou City based on the five major soil erosion factors in the RUSLE model, and analyses the spatial and temporal dynamics of soil erosion in the North and South Mountains of Lanzhou City and the soil erosion characteristics under different environmental factors. The results of the study show that: The intensity of soil erosion is dominated by slight erosion, which was distributed in the northwestern and southeastern parts of the North and South Mountains in 1995, 2000, 2005, 2010, 2015 and 2018. Under different environmental factors, the soil erosion modulus increased with elevation and then decreased; the soil erosion modulus increased with a slope; the average soil erosion modulus of grassland was the largest, followed by forest land, cultivated land, unused land, construction land, and it was the smallest for water; except for bare land, the average soil erosion modulus decreases with the increase of vegetation cover; Soil erosion modulus was the greatest in the pedocal of the North and South Mountains, and the least in the alpine soil.

**Keywords:** soil erosion; RUSLE model; erosion intensity; land desertification; North and South Mountains of Lanzhou

#### **1. Introduction**

Soil erosion is the destruction and loss of soil and water resources and land productivity due to natural forces and human activities, mainly including land surface erosion and water loss, which is the most dominant form of soil degradation [1–3]. Soil erosion will destroy the surface structure, reduce land fertility, raise the riverbed, destroy water conservancy facilities, aggravate flood and drought, and pose a significant threat to agricultural production, river water quality, and the environment. Soil erosion has become one of the world's most extensive and complicated ecological problems. It has become the concern of many disciplines [4], such as soil science, agronomy, hydrology, environmental science, and so on [5–8]. China is one of the countries with the most severe soil erosion [9,10]. In 2018, the soil erosion area in China reached 2.73 <sup>×</sup> <sup>10</sup><sup>6</sup> km<sup>2</sup> , accounting for about 28.80% of the total area in China except Taiwan Province, a large area and a wide distribution [11]. The area of soil erosion in Northwest China is 1.26 <sup>×</sup> <sup>10</sup><sup>6</sup> km<sup>2</sup> , accounting for 40.95% of the total area of Northwest China, and this presents a fundamental environmental problem [12]. In Gansu Province, for instance, the soil erosion area reached 1.86 <sup>×</sup> <sup>10</sup><sup>5</sup> km<sup>2</sup> , accounting for about 40.66% of the total area, which exerted significant pressure on soil and water conservation and the construction of ecological civilization.

Lanzhou City, the capital of Gansu Province, is located in the upper basin of the Yellow River and consists of a pearl-shaped basin formed by the alluvial deposits of the Yellow River. To the north and south of Lanzhou City are the North and South Mountains, a

**Citation:** Zhang, H.; Lei, J.; Wang, H.; Xu, C.; Yin, Y. Study on Dynamic Changes of Soil Erosion in the North and South Mountains of Lanzhou. *Water* **2022**, *14*, 2388. https:// doi.org/10.3390/w14152388

Academic Editors: Ying Zhao, Jianguo Zhang, Jianhua Si, Jie Xue and Zhongju Meng

Received: 29 June 2022 Accepted: 29 July 2022 Published: 1 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

mountain range covered with loess formed by the terraces of the Yellow River. To the north of Lanzhou City is the Tengger Desert; to the west is the Badanjilin Desert, a region with 2.78 <sup>×</sup> 10 km<sup>2</sup> of severely desertified land [13], resulting in frequent sandstorms, serious surface exposure and fragile ecosystems [14–16]. The natural vegetation in the North and South Mountains of Lanzhou is mainly desert vegetation, at present, the area of the environmental greening project in the North and South Mountains of Lanzhou City has reached 413 km<sup>2</sup> , with 1.6 <sup>×</sup> <sup>10</sup><sup>8</sup> trees of various types being established, forming a complete artificial ecosystem and making the North and South Mountains an important ecological barrier [17,18]. Therefore, it is of great significance for soil and water conservation and ecological civilization construction in Lanzhou to reveal the temporal and spatial characteristics of soil erosion and analyze the dynamic changes of soil erosion in Lanzhou.

Soil erosion models are a common method for quantitative soil erosion estimation. USDA and university cooperations saw the establishment of the Revised Universal Soil Loss Equation (RUSLE), based on the Universal Soil Loss Equation (USLE) in 1986 [19], which has become a widely used model for quantitative soil erosion estimation worldwide due to its simplicity, few parameter requirements, and high estimation accuracy compared to other soil erosion models [20–23]. Therefore, this study takes the South and North Mountains of Lanzhou City as the research area, takes soil erosion as the research content, and uses the RULSE model to calculate the soil erosion modulus of the South and North Mountains of Lanzhou City based on soil field sampling data, land use, and precipitation data. The objectives of this study are (1) to reveal the spatial and temporal variation characteristics of soil erosion in the South and North Mountains of Lanzhou City, and (2) to provide scientific reference for the construction of water and soil conservation and ecological civilization.

## **2. Materials and Methods**

#### *2.1. General Situations of the Study Area*

The North and South Mountains of Lanzhou span Anning District, Qilihe District, Chengguan District, Xigu District, Gaolan County, and Yuzhong County within the jurisdiction of Lanzhou City, with geographical coordinates of 35◦440–36◦190 N, 103◦210–103◦590 E. The total area is about 1940.08 km<sup>2</sup> (Figure 1). Among them, the green part accounts for 846.66 km<sup>2</sup> , and the non-green region accounts for 1147.42 km<sup>2</sup> . The geological conditions of this area are involved. The topography is fragmented, and natural disasters are to occur easily. The climate type belongs to the temperate semi-arid continental monsoon climate, with an annual average temperature of 9.1 ◦C and a yearly average rainfall of 327.7 mm, mostly concentrated from July to September, and the average annual potential evaporation is 1468 mm, which is 4.4 times of the precipitation. The vegetation type belongs to the transition type of typical steppe to desert steppe. Currently, most of the existing forests in Lanzhou's North and South Mountains are artificial forests, mostly young and middle-aged. Artificial afforestation is mainly coniferous and broad-leaved mixed forest, arbor shrub mixed forest, and shrub forest. The soil types in this area are primarily grey calcareous soil, mostly dark grey calcareous soil and typically grey calcareous soil in the South Mountains, and light grey calcareous soil and red sandy soil in the northern mountain, with a loose texture and weak anti-erosion ability.

**Figure 1.** Overview of the study area. **Figure 1.** Overview of the study area.

#### *2.2. Data Source 2.2. Data Source*


Using the 1:1 million soil map of the North and South Mountains of Lanzhou City as the base map, about 120 soil sampling points were designed in July–August 2019 according to a uniform distribution method of 4 km × 4 km, The sampling was carried out according to the plan, combining the actual situation with randomly selected 10 m × 10 m sample plots. A total of 130 soil samples were collected. The field sampling operation was carried out using a soil auger to collect soil samples from 0–20 cm of the surface layer at the center of the sample plots and four right-angle points, mixed evenly and placed in self-sealing bags for the determination of soil texture and soil organic carbon. 0–20 cm soil samples of the surface layer at the center and four right corners were collected with a ring knife, put into an aluminum box, and weighed fresh at the sampling site, which was used to determine the soil bulk density. Using GPS positioning, the elevation, longitude, and latitude of the sampling points in the center were recorded and numbered sequentially. (2) Determination of soil samples Using the 1:1 million soil map of the North and South Mountains of Lanzhou City as the base map, about 120 soil sampling points were designed in July–August 2019 according to a uniform distribution method of 4 km × 4 km, The sampling was carried out according to the plan, combining the actual situation with randomly selected 10 m × 10 m sample plots. A total of 130 soil samples were collected. The field sampling operation was carried out using a soil auger to collect soil samples from 0–20 cm of the surface layer at the center of the sample plots and four right-angle points, mixed evenly and placed in self-sealing bags for the determination of soil texture and soil organic carbon. 0–20 cm soil samples of the surface layer at the center and four right corners were collected with a ring knife, put into an aluminum box, and weighed fresh at the sampling site, which was used to determine the soil bulk density. Using GPS positioning, the elevation, longitude, and latitude of the sampling points in the center were recorded and numbered sequentially.

The determination of soil texture was carried out by the Mastersizer2000 laser parti-(2) Determination of soil samples

cle size analyzer (model: MS2000, Zhenxiang Technology Co., LTD, Changsha, China). The soil organic carbon content was determined by the Qiulin method, the soil salinity was determined by the "residue drying-mass method", and the pH value was determined by the "potential method". The determination of soil texture was carried out by the Mastersizer 2000 laser particle size analyzer (model: MS2000, Zhenxiang Technology Co., Ltd., Changsha, China). The soil organic carbon content was determined by the Qiulin method, the soil salinity was determined by the "residue drying-mass method", and the pH value was determined by the "potential method".

#### 2.2.2. Other Data (1) The meteorological data was based on the monthly precipitation data set of 0.5° × 2.2.2. Other Data

0.5° in China from 1995 to 2018 (V2.0), which came from the China Meteorological data sharing Network (http://data.cma.cn/), (accessed on 25 March 2019). (2) The GDEMDEM 30 m spatial resolution digital elevation was derived from the geospatial data cloud (http://www.gscloud.cn/), (accessed on 25 March 2019).. (3) The Landsat TM/OLI image from 1995 to 2018 was selected as the source of the Google Earth Engine cloud platform, to calculate the Normalized Difference Vegetation Index (NDVI) for the study area (Google Earth Engine, GEE) (https://earthengine.google.com/), (accessed on 25 March 2019).. The image was programmed in the platform to preprocess the image. (4) The land use with a spatial resolution of 30 m in 1990, 2000, 2005, 2010, 2015, and 2018 was selected from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/), (accessed on 25 March 2019). (5) National Cryosphere (1) The meteorological data was based on the monthly precipitation data set of 0.5◦ × 0.5◦ in China from 1995 to 2018 (V2.0), which came from the China Meteorological data sharing Network (http://data.cma.cn/), (accessed on 25 March 2019). (2) The GDEM-DEM 30 m spatial resolution digital elevation was derived from the geospatial data cloud (http://www.gscloud.cn/), (accessed on 25 March 2019). (3) The Landsat TM/OLI image from 1995 to 2018 was selected as the source of the Google Earth Engine cloud platform, to calculate the Normalized Difference Vegetation Index (NDVI) for the study area (Google Earth Engine, GEE) (https://earthengine.google.com/), (accessed on 25 March 2019). The image was programmed in the platform to preprocess the image. (4) The land use with a spatial resolution of 30 m in 1990, 2000, 2005, 2010, 2015, and 2018 was selected from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/), (accessed on 25 March 2019). (5) National Cryosphere Desert Data

Center provided the desertification distribution data (http://www.ncdc.ac.cn), (accessed on 25 March 2019).

#### *2.3. Research Methods*

#### 2.3.1. Soil Erosion Model

The study used the RUSLE model to estimate soil erosion in Lanzhou [24–26]. The formula is as follows:

$$A = \mathcal{R} \cdot \mathcal{K} \cdot \mathcal{LS} \cdot \mathcal{C} \cdot P \tag{1}$$

Among them, *A* is the average soil erosion amount per unit area last year, the unit is [t/(km<sup>2</sup> ·a)],and *<sup>R</sup>* is the precipitation erosivity factor, the unit is [MJ mm/(km<sup>2</sup> ·h·a)], *K* is the soil erodibility factor, in units [t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm)], *LS* is the slope length factor (dimensionless); *C* is the vegetation cover and management factor, (dimensionless); *P* is the soil and water conservation and measure factor (dimensionless).

#### 2.3.2. Determination of Factors in the RUSLE Model

#### (1) Determination of *R*-value of precipitation erosivity factor

Precipitation is one of the important exogenous forces causing soil erosion, reflecting the potential impact of annual average or maximum precipitation on soil erosion. This study adopted the method of estimating rainfall erosivity by using yearly and monthly precipitation data proposed by Wischmeier [27]. The formula is as follows:

$$R = \sum\_{i=1}^{12} \left[ 1.735 \times 10^{(1.5 \times \log \frac{P\_i^2}{P} - 0.8188)} \right] \tag{2}$$

In the formula, *P<sup>i</sup>* is monthly precipitation (mm); *P* is annual precipitation (mm). This method has been applied in the western region, and good results have been obtained km [28].

#### (2) K value of soil erodibility factor

The soil erodibility factor refers to the soil loss rate under a given unit of precipitation erosivity measured in a standard plot [29,30]. In this study, Williams' calculation method of soil erodibility factor *K* in the EPIC model was adopted [31]. The formula is as follows:

$$\begin{array}{c} K = & 0.1317 \times \left\{ 0.2 + 0.3 \exp\left[ -0.0256 \text{ s} \, \text{and} \left( 1 - \frac{\text{s} \, \text{l} \, \text{l}}{100} \right) \right] \right\} \times \left[ \frac{\text{s} \, \text{l} \, \text{t}}{\text{clay} + \text{s} \, \text{l} \, \text{t}} \right]^{0.3} \\ & \times \left[ 1 - \frac{0.25 \text{C}}{\text{C} + \exp(3.72 - 2.95 \text{C})} \right] \times \left[ 1 - \frac{0.7 \, \text{s} \, \text{l} \, \text{}}{\text{s} \, \text{t} + \exp(-5.51 + 22.9 \text{ s} \, \text{n} \, \text{l})} \right] \end{array} \tag{3}$$

Among them, *Sand*, *Silt*, and *Clay* represent the percentage of sand, silt, and clay content in soil, respectively (%); *C* is the percentage of soil organic carbon content (%); *Sn*1 = 1 − Sand/100. Generally, a higher value of the soil erodibility factor *K* indicates that the soil is poorly resistant to erosion and susceptible to erosion; conversely, the soil is not susceptible to erosion [32–34].

(3) km LS value of slope length factor

The slope length factor, i.e., the topographic factor, determines the state and direction of movement of surface runoff [35]. The greater the slope and the longer the slope length, the greater the potential energy that surface runoff will acquire, and the more intense the erosive effect on soil. In this study, the slope and slope length factors were extracted by the formulas studied by McCool et al. [36] and Liu et al. [37]. The calculation formulae of slope factors are as follows:

$$\mathbf{S} = \begin{cases} 10.8 \cdot \sin \theta + 0.03 & \theta < 6 \\ 16.8 \cdot \sin \theta - 0.50 & 5 \le \theta < 14 \\ 21.91 \cdot \sin \theta - 0.90 & \theta < 14 \end{cases} \tag{4}$$

where S is the slope factor (dimensionless), and *θ* is the slope value (◦ ), which can be extracted from the DEM data.

The formula for calculating the slope length factor is as follows:

$$L = (\lambda / 22.13)^a \tag{5}$$

$$
\lambda = \text{flowacc} \times \text{cellsize} \tag{6}
$$

$$\mathfrak{a} = \mathfrak{F}/(\mathfrak{1} + \mathfrak{F}) \tag{7}$$

$$\beta = (\sin \theta / 0.089) / [3.0 \times (\sin \theta)^{0.8} + 0.56] \tag{8}$$

Among them, *L* is the slope length factor, and its value is the amount of soil erosion produced on the standard slope of 22.13 m. The *λ* is the slope length, where *flowacc* is the catchment accumulation, *cellsize* is the size of the DEM data grid pixel, and *α* is the slope length, *θ* is the slope value, in units of (◦ ); *β* is the parameter that determines *α*.

#### (4) C value of vegetation cover and management factor

Vegetation can protect the surface soil and slow down the rate of soil erosion [38]. NDVI is the most common data to calculate the C value of vegetation cover and management factor [39]. The NDVI number 9 used in this study is derived from the Google Earth Engine cloud platform, and the formula proposed by VanderKnijff et al. [40] is used to calculate the C value of vegetation cover and management factor. The formula is as follows:

$$\mathcal{C} = \exp\left[-a \times \frac{NDVI}{b - NDVI}\right] \tag{9}$$

Among them, *C* is the vegetation cover and management factor (dimensionless); *a* and *b* are the parameters that determine the NDVI-C relationship curve. Through VanderKnijff experiments, it is found that the most appropriate values are *a* = 2 and *b* = 1. This method has been studied in China and has achieved good results. According to the Formula (9), if the C value is negative, the assignment is 0 for all negative values; if the C value is greater than 1, the assignment is 1 for all values greater than 1. The higher the C value, the worse the vegetation growth; on the contrast, the lower the C value, the better the vegetation growth.

## (5) *p*-value of soil and water conservation measures

The factor of soil and water conservation and measures generally refers to the ratio of the amount of soil loss when certain engineering measures are taken in a certain area to the amount of soil loss without engineering measures under the same conditions. Its value ranges from 0 to 1; 0 means that soil erosion will not occur in this area, and 1 means no soil and water conservation measures have been taken [38,41–43].

#### **3. Results**

#### *3.1. Calculation of Each Factor in the RUSLE Model*

(1) R-value of precipitation erosivity factor

This method has been applied in the western region, and good results have been obtained [28]. The average precipitation erosivity factors in 1995, 2000, 2005, 2010, 2015, and 2018 in Lanzhou were 110.06, 83.20, 71.09, 46.68, 56.97 and 198.61 [MJ·mm/(km<sup>2</sup> ·h·a)] respectively. Spatially, the precipitation erosivity factors of the North and South Mountains decreased from southeast to northwest in 1995, 2000, 2005, and 2010. The precipitation erosivity factors of the North and South Mountains decreased from the west to the east in 2015 and 2018. The precipitation erosivity factor of the west was greater than that of the east. The erosivity factor of precipitation in 2018 was significantly higher than in other years, mainly because 2018 was an abnormally rainy year. The precipitation was higher than that in previous years (Figure 2).

2018 in Lanzhou were 110.06, 83.20, 71.09, 46.68, 56.97 and 198.61 [MJ·mm/(km<sup>2</sup>

spectively. Spatially, the precipitation erosivity factors of the North and South Mountains decreased from southeast to northwest in 1995, 2000, 2005, and 2010. The precipitation erosivity factors of the North and South Mountains decreased from the west to the east in 2015 and 2018. The precipitation erosivity factor of the west was greater than that of the east. The erosivity factor of precipitation in 2018 was significantly higher than in other years, mainly because 2018 was an abnormally rainy year. The precipitation was higher

·h·a)] re-

**Figure 2.** Spatial distribution of rainfall erosivity in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018. **Figure 2.** Spatial distribution of rainfall erosivity in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018.

#### (2) K value of soil erodibility factor (2) K value of soil erodibility factor

than that in previous years (Figure 2).

According to the data of soil texture and soil organic carbon content of the sampling points, the K value was calculated, and ordinary kriging interpolation was performed in ArcGIS 10.4 software. The spatial distribution of soil erodibility factors in the northern and southern mountains of Lanzhou City was calculated according to Formula (3) (Figure 3). The areas with a soil erodibility factor of 0.054–0.061 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in the central and eastern regions, and the areas with ?.soil erodibility factor of 0.045–0.053 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in the western, northwest and southern regions. The areas with a soil erodibility factor of 0.037–0.044 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in parts of the North Mountain. The areas with a soil erodibility factor of 0.018–0.036 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in the western part of the North Mountain. According to the data of soil texture and soil organic carbon content of the sampling points, the K value was calculated, and ordinary kriging interpolation was performed in ArcGIS 10.4 software. The spatial distribution of soil erodibility factors in the northern and southern mountains of Lanzhou City was calculated according to Formula (3) (Figure 3). The areas with a soil erodibility factor of 0.054–0.061 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in the central and eastern regions, and the areas with ? soil erodibility factor of 0.045–0.053 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in the western, northwest and southern regions. The areas with a soil erodibility factor of 0.037–0.044 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in parts of the North Mountain. The areas with a soil erodibility factor of 0.018–0.036 t·km<sup>2</sup> ·h/(km<sup>2</sup> ·MJ·mm) were mainly distributed in the western part of the North Mountain. *Water* **2022**, *14*, x FOR PEER REVIEW 7 of 16

**Figure 3.** Spatial distribution of soil erodibility factor K in the South and North Mountains of Lanzhou. **Figure 3.** Spatial distribution of soil erodibility factor K in the South and North Mountains of Lanzhou.

#### (3) LS value of slope length factor (3) LS value of slope length factor

Mountains of Lanzhou.

The spatial distribution of the slope factor of the North and South Mountains in Lanzhou (Figure 4) showed that the minimum value of the slope factor was 0, the maximum value was 58.98, the average value was 15.52, the minimum value of the slope factor was 0, the maximum value was 9.99, the average value was 4.76, the minimum value of the The spatial distribution of the slope factor of the North and South Mountains in Lanzhou (Figure 4) showed that the minimum value of the slope factor was 0, the maximum value was 58.98, the average value was 15.52, the minimum value of the slope factor was 0, the maximum value was 9.99, the average value was 4.76, the minimum value of the

slope length factor was 0, the maximum value was 5.92, and the average value was 2.22. The minimum value of the slope length factor was 0, the maximum value was 59.19, and

**Figure 4.** Spatial distribution of the gradient slope and slope length factor in the Northand South

Mountain was larger than that of the North Mountain.

(4) C value of vegetation cover and management factor

zhou.

(3) LS value of slope length factor

slope length factor was 0, the maximum value was 5.92, and the average value was 2.22. The minimum value of the slope length factor was 0, the maximum value was 59.19, and the average value was 12.20. The overall upper slope, slope factor, slope length factor, and slope length factor were zonal distributions, and the slope length factor of the South Mountain was larger than that of the North Mountain. 0, the maximum value was 9.99, the average value was 4.76, the minimum value of the slope length factor was 0, the maximum value was 5.92, and the average value was 2.22. The minimum value of the slope length factor was 0, the maximum value was 59.19, and the average value was 12.20. The overall upper slope, slope factor, slope length factor, and slope length factor were zonal distributions, and the slope length factor of the South Mountain was larger than that of the North Mountain.

**Figure 3.** Spatial distribution of soil erodibility factor K in the South and North Mountains of Lan-

The spatial distribution of the slope factor of the North and South Mountains in Lanzhou (Figure 4) showed that the minimum value of the slope factor was 0, the maximum value was 58.98, the average value was 15.52, the minimum value of the slope factor was

*Water* **2022**, *14*, x FOR PEER REVIEW 7 of 16

**Figure 4.** Spatial distribution of the gradient slope and slope length factor in the Northand South Mountains of Lanzhou. **Figure 4.** Spatial distribution of the gradient slope and slope length factor in the Northand South Mountains of Lanzhou. *Water* **2022**, *14*, x FOR PEER REVIEW 8 of 16

> (4) C value of vegetation cover and management factor (4) C value of vegetation cover and management factor

The average values of vegetation cover and management factors in the North and South Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015, and 2018 were 0.34, 0.43, 0.56, 0.50, 0.40 and 0.57, respectively. Overall, the vegetation cover and management factors were the lowest in 1995 and the highest in 2018. The C value of the North Mountain was higher than that of the South Mountain, indicating that the vegetation coverage of the North Mountain was lower than that of the South Mountain (Figure 5). The average values of vegetation cover and management factors in the North and South Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015, and 2018 were 0.34, 0.43, 0.56, 0.50, 0.40 and 0.57, respectively. Overall, the vegetation cover and management factors were the lowest in 1995 and the highest in 2018. The C value of the North Mountain was higher than that of the South Mountain, indicating that the vegetation coverage of the North Mountain was lower than that of the South Mountain (Figure 5).

**Figure 5.** Spatial distribution of vegetation cover and management factors in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018. **Figure 5.** Spatial distribution of vegetation cover and management factors in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018.

In this study, according to Table 1, the land use data of 1995, 2000, 2005, 2010, 2015, and 2018 were assigned, and the spatial distribution of the *p*-value of soil and water con-

change in Lanzhou's North and South Mountains was not obvious, the spatial distribution of soil and water conservation measures in the North and South Mountains was con-

**Figure 6.** Spatial distribution of soil and water conservation measures factors in the South and North

Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018.

(5) *p*-value of soil and water conservation measures

sistent, and the change was not obvious.

(5) *p*-value of soil and water conservation measures

*Water* **2022**, *14*, x FOR PEER REVIEW 8 of 16

North Mountain was lower than that of the South Mountain (Figure 5).

In this study, according to Table 1, the land use data of 1995, 2000, 2005, 2010, 2015, and 2018 were assigned, and the spatial distribution of the *p*-value of soil and water conservation measure factors with 30 m resolution was obtained (Figure 6). As the land-use change in Lanzhou's North and South Mountains was not obvious, the spatial distribution of soil and water conservation measures in the North and South Mountains was consistent, and the change was not obvious. **Figure 5.** Spatial distribution of vegetation cover and management factors in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018. (5) *p*-value of soil and water conservation measures In this study, according to Table 1, the land use data of 1995, 2000, 2005, 2010, 2015,

The average values of vegetation cover and management factors in the North and South Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015, and 2018 were 0.34, 0.43, 0.56, 0.50, 0.40 and 0.57, respectively. Overall, the vegetation cover and management factors were the lowest in 1995 and the highest in 2018. The C value of the North Mountain was higher than that of the South Mountain, indicating that the vegetation coverage of the

**Table 1.** *p* values of different land-use types in the South and North Mountains of Lanzhou. and 2018 were assigned, and the spatial distribution of the *p*-value of soil and water conservation measure factors with 30 m resolution was obtained (Figure 6). As the land-use


**Figure 6.** Spatial distribution of soil and water conservation measures factors in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018. **Figure 6.** Spatial distribution of soil and water conservation measures factors in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018.

#### *3.2. Spatio-Temporal Variation Characteristics of Soil Erosion*

According to the soil erosion modulus in different years, the average soil erosion modulus in the North and South Mountains of Lanzhou City generally showed a first fluctuating downward trend and started to increase after 2016, and in 2018 there was an abrupt change and a sudden increase to 25.83 t/(km<sup>2</sup> ·a). The annual average soil erosion amount was 330.74 <sup>×</sup> <sup>10</sup><sup>4</sup> t (1995), 323.80 <sup>×</sup> <sup>10</sup><sup>4</sup> t (2000), 342.09 <sup>×</sup> <sup>10</sup><sup>4</sup> t (2005), 200.20 <sup>×</sup> <sup>10</sup><sup>4</sup> t (2010), 314.41 <sup>×</sup> <sup>10</sup><sup>4</sup> t (2015), and 515.14 <sup>×</sup> <sup>10</sup><sup>4</sup> t (2018) (Figure 7).

According to the Ministry of Water Resources (SL190-2007) Soil Erosion Classification and Grading Standard [44], the study area is divided into six soil erosion intensity classes according to the soil erosion modulus, namely, slight erosion [5–25 t/(km<sup>2</sup> ·a)], light erosion [5–25 t/(km<sup>2</sup> ·a)], moderate erosion [25–50 t/(km<sup>2</sup> ·a)], strong erosion [50–80 t/(km<sup>2</sup> ·a)], extremely strong erosion [80–150 t/(km<sup>2</sup> ·a)] and severe erosion [150 t/(km<sup>2</sup> ·a)]. The spatial distribution of soil erosion intensity classes for 1995, 2000, 2005, 2010, 2015 and 2018 was obtained for the North and South Mountains of Lanzhou (Figure 8). Soil erosion intensity in the North and South Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015, and 2018 was mainly slight erosion. The amount of soil erosion in 2018 was significantly higher than that in other years, mainly because 2018 was an unusually wet year, and the precipitation was higher than in previous years. It was mainly distributed in the northwest and southeast of the North and South Mountains. The strong, extremely strong, and severe soil erosion was mainly distributed in the middle of the South Mountain and the middle of the North Mountain (Figure 8).

**Table 1.** *p* values of different land-use types in the South and North Mountains of Lanzhou.

According to the soil erosion modulus in different years, the average soil erosion modulus in the North and South Mountains of Lanzhou City generally showed a first fluctuating downward trend and started to increase after 2016, and in 2018 there was an

According to the Ministry of Water Resources (SL190-2007) Soil Erosion Classification and Grading Standard [44], the study area is divided into six soil erosion intensity

t (2000), 342.09 × 10<sup>4</sup>

t (2018) (Figure 7).

·a). The annual average soil erosion

·a)] and severe erosion [150 t/(km<sup>2</sup>

t (2005), 200.20 × 10<sup>4</sup>

·a)], strong erosion [50–80

t

·a)], light

·a)].

**Land-Use Type Cultivated Land Forest Land Grassland Water Area Construction Land Unused Land** *p* 0.35 1.0 1.0 0.0 0.0 1.0

*3.2. Spatio-Temporal Variation Characteristics of Soil Erosion*

abrupt change and a sudden increase to 25.83 t/(km<sup>2</sup>

t (1995), 323.80 × 10<sup>4</sup>

classes according to the soil erosion modulus, namely, slight erosion [5–25 t/(km<sup>2</sup>

·a)], moderate erosion [25–50 t/(km<sup>2</sup>

The spatial distribution of soil erosion intensity classes for 1995, 2000, 2005, 2010, 2015 and 2018 was obtained for the North and South Mountains of Lanzhou (Figure8). Soil erosion intensity in the North and South Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015, and 2018 was mainly slight erosion. The amount of soil erosion in 2018 was significantly higher than that in other years, mainly because 2018 was an unusually wet year, and the precipitation was higher than in previous years. It was mainly distributed in the northwest and southeast of the North and South Mountains. The strong, extremely strong, and severe soil erosion was mainly distributed in the middle of the South Mountain and the

t (2015), and 515.14 × 10<sup>4</sup>

·a)], extremely strong erosion [80–150 t/(km<sup>2</sup>

middle of the North Mountain (Figure 8).

amount was 330.74 × 10<sup>4</sup>

(2010), 314.41 × 10<sup>4</sup>

erosion [5–25 t/(km<sup>2</sup>

t/(km<sup>2</sup>

**Figure 7.** Time change of annual soil erosion modulus in the Northand SouthMountains of Lanzhou from 1995 to 2018. **Figure 7.** Time change of annual soil erosion modulus in the Northand SouthMountains of Lanzhou from 1995 to 2018. *Water* **2022**, *14*, x FOR PEER REVIEW 10 of 16

**Figure 8.** Spatial distribution of soil erosion intensity in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018. **Figure 8.** Spatial distribution of soil erosion intensity in the South and North Mountains of Lanzhou in 1995, 2000, 2005, 2010, 2015 and 2018.

#### *3.3. Area Transfer Characteristics of Soil Erosion Intensity 3.3. Area Transfer Characteristics of Soil Erosion Intensity*

ure 9).

Based on the statistical analysis of the data of different erosion intensity areas, the transfer chord diagram of soil erosion intensity was obtained. From 1995 to 2018, the area of soil slight erosion was the largest, which was 735.92 km<sup>2</sup> , followed by mild, moderate, strong, extremely strong, and the area of severe erosion remained the smallest. Among them, slight soil erosion mainly shifted to mild soil erosion. The mild soil erosion transferred to slight soil erosion, and the area was 215.14 km<sup>2</sup> ; moderate soil erosion mainly shifted to slight and mild soil erosion; strong soil erosion mainly shifted to mild and moderate soil erosion; moderate and strong soil erosion transferred to extremely strong soil erosion; severe soil erosion shifted to strong and extremely strong soil erosion. From 1995 to 2018, the stability rates of slight, mild, moderate, strong, extremely strong and severe soil erosion were 36.91%, 4.64%, 2.74%, 0.79%, 0.77% and 0.08%, respectively (Fig-Based on the statistical analysis of the data of different erosion intensity areas, the transfer chord diagram of soil erosion intensity was obtained. From 1995 to 2018, the area of soil slight erosion was the largest, which was 735.92 km<sup>2</sup> , followed by mild, moderate, strong, extremely strong, and the area of severe erosion remained the smallest. Among them, slight soil erosion mainly shifted to mild soil erosion. The mild soil erosion transferred to slight soil erosion, and the area was 215.14 km<sup>2</sup> ; moderate soil erosion mainly shifted to slight and mild soil erosion; strong soil erosion mainly shifted to mild and moderate soil erosion; moderate and strong soil erosion transferred to extremely strong soil erosion; severe soil erosion shifted to strong and extremely strong soil erosion. From 1995 to 2018, the stability rates of slight, mild, moderate, strong, extremely strong and severe soil erosion were 36.91%, 4.64%, 2.74%, 0.79%, 0.77% and 0.08%, respectively (Figure 9).

**Figure 9.** Chordal graph of soil erosion intensity in South and North Mountains of Lanzhou from 1995 to 2018 (Note: A: slight erosion; B: mild erosion; C: moderate erosion; D: strong erosion; E:

extremely strong erosion; F: severe erosion).

ure 9).

in 1995, 2000, 2005, 2010, 2015 and 2018.

*3.3. Area Transfer Characteristics of Soil Erosion Intensity*

of soil slight erosion was the largest, which was 735.92 km<sup>2</sup>

ferred to slight soil erosion, and the area was 215.14 km<sup>2</sup>

**Figure 8.** Spatial distribution of soil erosion intensity in the South and North Mountains of Lanzhou

strong, extremely strong, and the area of severe erosion remained the smallest. Among them, slight soil erosion mainly shifted to mild soil erosion. The mild soil erosion trans-

shifted to slight and mild soil erosion; strong soil erosion mainly shifted to mild and moderate soil erosion; moderate and strong soil erosion transferred to extremely strong soil erosion; severe soil erosion shifted to strong and extremely strong soil erosion. From

Based on the statistical analysis of the data of different erosion intensity areas, the transfer chord diagram of soil erosion intensity was obtained. From 1995 to 2018, the area

**Figure 9.** Chordal graph of soil erosion intensity in South and North Mountains of Lanzhou from 1995 to 2018 (Note: A: slight erosion; B: mild erosion; C: moderate erosion; D: strong erosion; E: extremely strong erosion; F: severe erosion). **Figure 9.** Chordal graph of soil erosion intensity in South and North Mountains of Lanzhou from 1995 to 2018 (Note: A: slight erosion; B: mild erosion; C: moderate erosion; D: strong erosion; E: extremely strong erosion; F: severe erosion).

, followed by mild, moderate,

; moderate soil erosion mainly
