Laboratory-Scaled Investigation into Combined Impacts of Temporal Rainfall Patterns and Intensive Tillage on Soil and Water Loss

Abstract

Many studies have focused on the impacts of rainfall duration and intensity, while overlooking the role of rainfall patterns on intensive tillage erosion in hilly agricultural landscapes. The objective of this study was to determine the combined effects of rainfall patterns and tillage erosion on surface runoff and soil loss on sloping farmland in the purple soil area of China. Five simulated rainfall patterns (constant, rising, falling, rising–falling, and falling–rising) with the same total precipitation were designed, and the intensive tillage treatment (IT) and no-tillage treatment (NT) were subjected to simulated rainfall using rectangular steel tanks (2 m × 5 m) with a slope of 15°. To analyse the differences in the hydrological characteristics induced by tillage erosion, we calculated the flow velocity (V), Reynolds number (Re), Froude number (Fr), and Darcy–Weisbach resistance coefficient (f). The results indicate that significant differences in surface runoff and sediment yield were found among different rainfall patterns and rainfall stages (p < 0.05). The falling pattern and falling–rising pattern had a shorter time gap between the rainfall initiation and runoff occurrence as well as a larger sediment yield than those of the other rainfall patterns. The value of f varied from 0.30 to 9.05 for the IT and 0.48 to 11.57 for the NT and exhibited an approximately inverse trend to V and Fr over the course of the rainfall events. Compared with the NT, the mean sediment yield rates from the IT increased the dynamic range of 8.34–16.21% among the different rainfall patterns. The net contributions of the IT ranged from 2.77% to 46.39% in terms of surface runoff and 10.14–78.95% in terms of sediment yield on sloping farmland. The surface runoff and sediment yield were positively correlated with rainfall intensity, V, and Fr, but negatively correlated with f irrespective of tillage operation (p < 0.05). The results showed that the tillage erosion effects on soil and water loss were closely related to rainfall patterns in hilly agricultural landscapes. Our study not only sheds light on the mechanism of tillage erosion and rainfall erosion but also provides useful insights for developing tillage water erosion prediction models to evaluate soil and water loss on cultivated hillslopes.

1. Introduction

Soil and water loss continues to be a serious threat in many cultivated agricultural lands, and it results in reduced crop production and environmental degradation. Natural rainfall is a crucial driving force that governs surface runoff and soil loss processes. The properties of rainfall, such as its intensity, interval, and duration, are critical factors that impact the processes of surface runoff and soil erosion [1,2,3]. The intensity of rainfall fluctuates greatly over time, and the maximum rates of precipitation can surpass the average rate by up to ten times [4,5]. During natural rainstorms, a series of field experiments by Mohamadi and Kavian [6] revealed that storms with increasing rainfall intensity produced the most soil and water loss. For the typical black soil region of Northeast China, the soil loss for the rising–falling pattern was 1.20, 1.63, 1.78, and 1.80 times higher than those of the falling–rising, falling, constant, and rising patterns, respectively [7]. An et al. [8] compared four different rainfall patterns in cinnamon soil and concluded that surface runoff was ranked in the following order for various storm patterns: falling–rising > falling > rising–falling > rising patterns. Different rainfall patterns and rainfall stages have significant differences in soil and water loss [2,4,9]. These studies demonstrate that different rainfall patterns have essential influences on surface runoff and sediment yield on cultivated hillslopes.

Although variable rainfall intensity is a common phenomenon during natural rainfall events, there are serious inadequacies in the study of the effect of variable rainfall intensity combinations (rainfall patterns) on the soil loss process in hilly croplands, especially under the conditions of intensive tillage erosion. In agricultural fields, the influence of different rainfall intensities on the processes of soil and water loss is largely related to tillage operations [10]. Previous studies have demonstrated that tillage erosion results in soil translocation on hillslopes, and the processes of tillage operations result in soil movement downslope, leading to soil accumulation in lower-slope positions and decreased soil in higher slope positions [11,12]. Tillage-induced progressive soil redistribution significantly affects soil hydrological properties and rainfall–runoff processes, as it alters soil profile properties, such as soil structure and layer thickness, within the landscape [13,14].

The thickness of the soil layer is an important factor that strongly affects runoff and hydrological processes in agricultural landscapes. A thin soil layer can result in higher erosion rates due to the direct exposure of underlying rock and soil, thus increasing soil susceptibility to mechanical stresses and weathering processes [15,16]. In addition, a thin soil layer can decrease water infiltration and retention, which then enhances runoff and sediment yield [17,18]. Studies have shown that soil thickness is a key factor controlling soil and water loss, as it can strongly influence soil properties and hydrological processes. Soil thickness will produce an important effect on water-holding capacity and the soil infiltration rate. It is worth noting that tillage operations and subsequent rainfall patterns of sloped farmland may contribute to extreme soil erosion. However, there is still limited research on the impact of intensive tillage erosion on soil and water loss, especially under different rainfall conditions.

In the hilly areas of Sichuan, sloping fields as the dominant cultivated land are characterized by short slope segments and a narrow slope width. There has been a long history of tillage operations due to the high population density and land deficiency. Farmers traditionally till their fields twice a year to loosen the surface soil, change the soil’s physical properties, and increase land productivity. Furthermore, the rainfall intensity in this region is relatively high, and the variation process of rainfall is complex. As a result, the combination of intensive tillage erosion and rainfall erosion results in extremely severe soil and water loss. Moreover, studies have proven that tillage erosion and rainfall erosion are the main causes of soil loss in the hilly areas of the Sichuan Basin [12]. Although there are many descriptive comments and viewpoints on the relationship between tillage operations and soil loss in sloping fields, how the combination of rainfall patterns and intensive tillage affect surface runoff and soil erosion remains unresolved. Against this background, the objectives of this study were to (1) analyse the changes in the surface runoff and sediment yield rate for different tillage erosion intensities with different rainfall patterns; (2) determine the interactions of varied rainfall intensities, stages, and intensity sequences under intensive tillage and no-tillage operations; and (3) examine the hydrologic mechanisms between different tillage intensities under different rainfall patterns in the purple soil region.

2. Materials and Methods

2.1. Study Sites and Experimental Design

Soil samples were gathered from a representative area of the purple soil region located in Mingshan District, Ya’an city, Sichuan Province, Southwest China (29°57′36″–30°16′15″ N, 103°2′40″–103°24′01″ E). This area is dominated by a subtropical humid monsoon climate. The study area experiences a subtropical monsoon humid climate, with a mean annual temperature of 16 °C and approximately 1500 mm of precipitation that is distributed unevenly throughout the year. Most rainstorms occur from May to October, when a heavy rainfall intensity greater than 100 mm h⁻¹ is usual. The soils found in this area, originating from purple mudstone deposits from the Jurassic Age, have been classified as Orthents [19]. The farmland had been cultivated successively for over 50 years, thereby resulting in a shallow soil depth due to long-term tillage operations and water erosion effects. The corrosion resistance of the soils is poor.

Table 1. The measured properties of the soils in the study.

Soil Property	Minimum	Maximum	Mean	SD	CV (%)
Sand (%)	24.54	34.71	29.05	4.23	14.56
Silt (%)	21.04	36.57	28.33	6.41	22.63
Clay (%)	37.13	46.53	42.62	4.10	9.63
Bulk density (g cm−3)	1.21	1.58	1.44	0.16	11.04
Soil depth (m)	0.36	0.48	0.43	0.05	11.86
Total porosity	0.44	0.51	0.47	0.03	6.73
Soil water content (%)	14.27	20.75	17.58	2.66	15.14
pH	7.81	8.40	8.11	0.25	3.04
SOC (g kg−1)	5.73	7.57	6.21	0.91	14.58
Total N (g kg−1)	0.82	1.15	0.93	0.15	16.16
CaCO3 (g kg−1)	83.6	103.76	93.97	8.24	8.77

Note: SOC: soil organic carbon. Total N: total nitrogen. CaCO₃: calcium carbonate, SD: standard deviation. CV: coefficient of variation.

provides a summary of the soil properties, and Liu [20] provides a detailed description of the sampling and determination methods used. The soil tested in this experiment had a bulk density of 1.44 g cm⁻³ with an average of 42.62% clay (<0.002 mm), 28.33% silt (0.002–0.02 mm), and 29.05% sand (0.02–2 mm). The soil organic carbon (SOC) content was 10.71 g kg⁻¹.

The soil samples (0–0.30 m depth) collected in the field were air-dried, and the roots, stones, and other debris were removed. Then, the soil blocks were crushed into fine soil particles and filled into two neighbouring steel tanks (2 m in width, 0.45 m in depth, and 5 m in length) through a 0.01 m sieve with an infilling depth of 0.40 m. Before infilling the soil, an approximately 0.02 m layer of permeable board was laid at the bottom of the steel tank, and the permeable board was provided with a permeable hole with a diameter of 0.01 m and an interval of 0.01 m. A layer of cotton gauze was laid above the board to ensure the permeability and prevent soil particles from leaking from the permeable board.

The previous literature based on field investigations has revealed that the soil profile at the upslope boundary has completely disappeared, leading to bedrock exposure due to intensive tillage [17,21,22]. In the hilly areas of the Sichuan Basin, Southwestern China, intense tillage erosion often results in shallow soil depths, and in some cases, bedrock is even exposed on the summits of agricultural land. Based on field surveys and previous studies, it is pretty common knowledge that intensive tillage will lead to the exposure of parent materials or rock at the ridge top of steep slopes. Hence, we covered a piece of plastic sheet (1 m long and 2 m wide) in the soil surface of one steel tank at the summit position (1–2 m) to simulate bedrock exposure; this scenario represented the intensive tillage erosion treatment (IT), and the erosion rate reached 129.60 Mg ha⁻¹. In contrast, another steel tank without a cover was considered the no-tillage treatment (NT, Figure 1a).

2.2. Rainfall Simulation

Artificial rainfall events were performed at Sichuan Agricultural University located in Ya’an city, Sichuan Province, China. The rainfall simulator (Model: NLJY-10-03) is a portable fully automatic simulator with a series of stainless-steel pipes and down-sprayers made by Nanlin Electronics Technology Incorporation. The rainfall simulation device used was similar to the one described by Dai et al. [23], whose effective rainfall area was 10 m × 6 m and which had a sprayer at a height that was 6 m above the ground (Figure 1b). Therefore, the rainfall area was sufficient to cover the area of the two neighbouring steel tanks in the experiment. It was possible to manually adjust the rainfall intensity within a range of 15 to 240 mm h⁻¹, with a raindrop distribution uniformity of over 85%. A hole with a 0.04 m aperture was located at the bottom edge of the steel tank to ensure free drainage during rainfall. The slope steepness of the steel tank could be adjusted within a range of 0–30°; however, for our tests, we chose a slope of 15°, as the experimental region has a threshold gradient that determines the conversion of farmland to grassland or forestland.

To minimize experimental errors caused by the variable rainfall intensities during the rainfall simulation and to represent natural conditions, we designed five different rainfall patterns based on field surveys and a literature analysis [2,24]: a constant pattern (rainfall intensity distribution with 90 mm h⁻¹, CR), rising pattern (rainfall intensity sequence as 30–60–90–120–150 mm h⁻¹, RR), falling pattern (rainfall intensity sequence as 150–120–90–60–30 mm h⁻¹, FR), rising–falling pattern (rainfall intensity sequence as 30–90–150–90–30 mm h⁻¹, RFR), and falling–rising pattern (rainfall intensity sequence as 150–90–30–90–150 mm h⁻¹, FRR). Each rainfall pattern was carried out with the same total rainfall of 90 mm and was divided into different rainfall stages (RS, Figure 2a–e), each corresponding to a specific rainfall intensity.

To ensure that the soil moisture content was consistent before each rainfall event, pre-rainfall was carried out before each rainfall event, and the soil moisture content was measured with a portable soil moisture analyser to control the soil moisture content between 25% and 30%. At the beginning of the simulated rainfall, the runoff initiation time was recorded when the runoff began to flow from the outlet of the steel tank. We continuously collected surface runoff and sediment samples in pre-weighed plastic buckets every 2 min. The excess water was poured away after 24 h of precipitation, and the sediment was poured into an aluminium box and dried in an oven at 105 °C. The samples were then weighed again to calculate the runoff amount, sediment discharge, and cumulative infiltration volume. Surface flow velocity was measured using K₂MnO₄ colouration for each rainfall stage on the upper-, middle-, and lower-slope positions. The upper slope was from the top to shoulder positions (0–1 m), the middle slope was from the shoulder to middle positions (2–3 m), and the lower slope was from the middle to the outlet (4–5 m). The mean flow velocity was obtained by multiplying the surface flow velocity with a correction coefficient of 0.67 according to a previous study [25]. The time taken for the tracer to travel a marked distance of 1 m was determined by analysing the propagation of the colour front using a stop-match method. To analyse the hydraulic characteristics of the flow, calculations were performed to determine the Reynolds number (Re), Froude number (Fr), and Darcy–Weisbach resistance coefficient (f). The formulas for calculating these parameters can be expressed as [26]:

$$Re=\frac{Vh}{u}$$

(1)

$$Fr=\frac{V}{\sqrt{gh}}$$

(2)

$$f=\frac{8ghJ}{V^2}$$

(3)

where V is mean flow velocity (cm s⁻¹), $u$ is kinematical viscosity (cm² s⁻¹), h is flow depth (cm), g is acceleration of gravity (cm s⁻²), and $J$ is surface slope (m m⁻¹).

Flow depth is a critical factor of surface flow. Yet it is very difficult to be measured due to erosion process on plot surface. Assuming slope flow is uniform, mean h was calculated using the following formula:

$$h=\frac{q}{U}=\frac{Q}{UBt}$$

(4)

where q is unit discharge (cm² s⁻¹), t is interval time to collect runoff samples (min), Q is runoff volume during t time (mL), and B is width of water-crossing section (cm).

2.3. Statistical Analysis

A paired t-test was conducted using SPSS 26.0 software to examine statistically significant differences in the surface runoff and sediment yield between the IT and NT treatments under different rainfall patterns. SigmaPlot 14.0 software was used to fit the equations of runoff rate and sediment yield rate. Meanwhile, AMOS 21.0 software was used to analyse the correlations between the rainfall intensity, surface runoff rate, hydraulic parameters, and sediment yield rate under the IT and NT treatments using a structural equation model (SEM). Model estimation was achieved based on the maximum likelihood method, and the fit statistics included the goodness of fit index (CFI), degree of freedom (df), the p-value of the model (p), chi square (χ²), and root mean square error of approximation (RMSEA). If p < 0.05, then no paths were missing, and the model was a very good fit. Redundancy analysis (RDA) using Canoco 5 software was used to check the effects of different hydraulic characteristic variables on runoff amount and sediment yield.

3. Results

3.1. Responses of Runoff Processes to Tillage Erosion and Rainfall Patterns

The time gaps between the rainfall initiation and runoff occurrence in the IT and NT treatments for the different rainfall patterns are depicted in Figure 3. During the rainfall event with a total of 90 mm of rainfall, a noticeable difference in the runoff rates was observed among the different rainfall patterns. Additionally, the time gap between the runoff occurrence and rainfall initiation varied mainly depending on the rainfall pattern. As shown in Figure 3, the runoff-initiating time for the IT was earlier than that for the NT under the same rainfall patterns. The longest time gap between the rainfall initiation and runoff occurrence was observed in the NT, whereas the shortest time gap was found in the IT for the same rainfall pattern. In both tillage treatments, the order of the time to runoff initiation was RR > RFR > CR > FRR > FR. The FR and FRR patterns had faster runoff-initiating times than those of the other rainfall patterns. Compared with the NT, the time gap between the rainfall initiation and runoff occurrence for the IT increased by 10.03%, 33.68%, 16.42%, 6.32%, and 33.82% for the RR, FR, CR, RFR, and FRR patterns, respectively. The runoff generation from the FR and FRR patterns was faster than that of other rainfall patterns. These results indicated that surface runoff was more likely to occur on the intensively tilled soils, and the presence of soil thickness induced by tillage operation seemed to be the main factor that delayed the initial runoff generation time.

As shown in Figure 4, the surface runoff rate for the two tillage treatments had a similar trend under different rainfall patterns. The change in the trend of the surface runoff rate in each stage of the rainfall pattern was consistent with that of the rainfall intensity for the different rainfall patterns, yet the surface runoff rate under the NT first increased sharply and then stabilized gradually as the rainfall duration increased. Lower runoff rates were found in the RFR patterns than in the other rainfall patterns for the two treatments. Figure 4 also shows the peak runoff rates for the different rainfall patterns, which were statistically 1.55 to 1.60 and 1.67 to 1.78 times greater than the peak runoff rate under the CR pattern for the NT and IT, respectively. Regardless of tillage intensity, the study found that the peak runoff rates during rainfall with varying intensities were significantly higher than those observed during storms with a constant intensity. These results suggest that the process of runoff occurrence was not only affected by rainfall pattern but also was influenced by tillage intensity on the slope.

Significant differences by the paired t-test were found in the cumulative runoff volume for the rainfall patterns between the NT and IT (p = 0.005), implying that the significant influence of different rainfall patterns on surface runoff was closely related to tillage intensity. The cumulative runoff volume varied from 551.89 to 642.16 L and 641.35 to 706.72 L for the NT and IT during the rainfall event, respectively. The cumulative runoff volume for the two tillage treatments increased as follows: FR < RFR < RR < CR < FRR, and the cumulative runoff volume resulting from the different rainfall patterns in the IT treatment was higher than that in the NT treatment. These results suggest that the cumulative runoff volume was impacted by the different rainfall patterns and tillage intensities.

3.2. Changes in Soil Loss and Sediment Yield under Different Rainfall Patterns

Figure 5 illustrates the temporal changes in sediment yield rates under the different rainfall patterns. Generally, the sediment yield rates exhibit changes based on the rainfall intensity during the rainfall event. The temporal trend of sediment yield varied significantly across all rainfall patterns and tillage intensities. For the CR pattern, the sediment yield rate for both the IT and NT increased sharply at the beginning and then decreased gradually, remaining stable until the end of the rainfall event. However, for the other four rainfall patterns, the sediment yield rates were inconsistent at different stages, despite having the same average rainfall intensity per storm. The sediment yield rates for the IT ranged from 1.41 to 21.42 L min⁻¹, 2.61 to 21.40 L min⁻¹, 0.05 to 20.69 L min⁻¹, 1.68 to 22.04 L min⁻¹, and 1.63 to 12.40 L min⁻¹ for the RR, FR, RFR, FRR, and CR patterns, respectively. A similar range of sediment yield rates was observed in the different rainfall patterns for the NT. Compared to that for the NT, the mean sediment yield rates for the IT increased by 8.34–16.21%, with an average of 11.59% under different rainfall patterns. These findings suggest that the sediment yield rate displayed a rapid and obvious response to rainfall patterns and tillage intensities.

The cumulative sediment production is ranked as FRR > FR > RFR > RR > CR and FRR > FR > RFR> CR > RR for the NT and IT, respectively. Regardless of the NT and IT treatments, the cumulative sediment production under the FR and FRR patterns was higher than that from the other rainfall patterns in both the NT and IT, showing that the early stages of the short high-intensity rainfall events had a critical impact on soil loss on steep slopes. There was a significant difference (p = 0.008) in the cumulative sediment production between the NT and IT treatments. The cumulative sediment production for the NT treatment reached 8.57, 13.34, 7.33, 9.54, and 13.20 kg for the RR, FR, CR, RFR, and FRR patterns, respectively. Compared with the NT, the cumulative sediment production for the IT increased by 21.69%, 35.03%, 83.95%, 24.85%, and 40.70% for the RR, FR, CR, RFR, and FRR patterns, respectively, indicating that the sediment yield significantly improved after intensive tillage in a hilly landscape.

3.3. Impacts of Rainfall Patterns and Tillage Erosion on Hydraulic Characteristics

The hydraulic parameters, such as V, Re, Fr, and f, for the different rainfall patterns and tillage intensities during the rainfall events are presented in

Table 2. Hydraulic parameters under different rainfall patterns and stages.

Rainfall Intensity (mm h−1)	Rainfall Pattern	Rainfall Stage	V (cm s−1)		Re		Fr		f
			NT	IT	NT	IT	NT	IT	NT	IT
30	RR	1	5.45	6.50	93.50	127.47	0.45	0.50	10.45	8.37
	FR	5	5.58	6.48	110.57	127.15	0.42	0.49	11.51	8.44
	RFR	1	5.54	6.03	62.95	78.35	0.56	0.57	6.70	6.47
	RFR	5	5.63	6.44	62.95	78.36	0.57	0.62	6.39	5.31
	FRR	3	5.77	6.49	122.79	137.05	0.42	0.48	11.57	9.05
60	RR	2	6.82	7.60	93.53	127.50	0.62	0.63	5.32	5.26
	FR	4	7.12	7.97	110.61	127.19	0.61	0.67	5.54	4.54
90	RR	3	8.48	9.81	93.55	127.54	0.86	0.92	2.77	2.44
	FR	3	8.92	10.62	110.64	127.24	0.86	1.04	2.82	1.92
	RFR	2	8.24	10.11	62.97	78.39	1.01	1.23	2.03	1.37
	RFR	4	9.14	9.71	62.95	78.38	1.06	1.18	1.85	1.49
	FRR	2	9.42	10.14	122.88	137.15	0.88	0.93	2.65	2.38
	FRR	4	9.82	10.95	122.89	137.16	0.94	1.05	2.34	1.89
	CR	1	8.80	10.93	97.72	101.52	0.89	1.21	2.59	1.40
	CR	2	9.34	12.96	97.73	101.53	0.98	1.57	2.16	0.84
	CR	3	8.58	13.14	97.72	101.53	0.86	1.60	2.80	0.81
	CR	4	8.87	12.52	97.72	101.53	0.90	1.49	2.53	0.93
	CR	5	9.15	11.45	97.72	101.52	0.95	1.30	2.30	1.22
120	RR	4	11.53	13.90	93.57	127.59	1.37	1.55	1.10	0.86
	FR	2	11.99	13.70	110.67	127.27	1.33	1.52	1.16	0.89
150	RR	5	14.41	18.56	93.59	127.61	1.91	2.40	0.57	0.36
	FR	1	14.19	18.01	110.69	127.30	1.72	2.29	0.70	0.39
	RFR	3	13.37	16.83	62.99	78.41	2.09	2.64	0.48	0.30
	FRR	1	13.06	16.11	122.92	137.21	1.44	1.87	1.00	0.59
	FRR	5	14.69	19.36	122.93	137.23	1.72	2.46	0.70	0.34

Note: V, mean flow velocity; Re, Reynold number; Fr, Froude number; and f, Darcy–Weisbach friction coefficient.

. The mean V, Re, and Fr values ranged from 6.03 to 19.36 cm s⁻¹ (5.45 to 14.69 cm s⁻¹), 78.35 to 137.23 (62.95 to 122.93), and 0.48 to 2.64 (0.42 to 2.09) for storms under the IT (NT) treatment, respectively. The trends of V and Fr showed a more pronounced increase over time in comparison to the rainfall pattern experiment, indicating that the rainfall stage had a stronger impact on V and Fr. However, this trend was weaker in Re. f varied from 0.30 to 9.05 for the IT and from 0.48 to 11.57 for the NT and exhibited an approximately inverse trend to V and Fr over the course of the rainfall experiments. The hydraulic parameters showed a general increase with increasing rainfall intensity, but no significant differences were found in V, Re, and Fr among the different rainfall stages and patterns at each rainfall intensity. A significant difference in hydraulic parameters was found between the IT and NT for each rainfall event (p < 0.01). In addition, Table 2 shows that the V, Re, and Fr resulting from the different rainfall patterns and stages in the IT were greater than those in the NT. The results indicate that intensive tillage had an important effect on the hydraulic parameters under the same rainfall conditions.

The RDA biplot illustrates how surface runoff and sediment yield correlate with rainfall intensity and hydraulic parameters (Figure 6). Under the IT treatment, the runoff volume and sediment yield accounted for 50.70% and 3% of the total variance, respectively. The first axis, accounting for 61.58% in the NT and 59.30% in the IT of the variation, was primarily associated with rainfall intensity, Fr, V, and f, while the second axis (1.70% in the NT and 3.05% in the IT) was driven only by RS and Re. Although the angle of the arrow was sharp among Fr, V, rainfall intensity, runoff volume, and sediment yield, the angle between f and the other variables was greater than 90°. In addition, the length of the arrow for Re and RS was obviously shorter than that of the other variables and lies on the second axis. The NT treatment followed a similar pattern to the IT treatment between the different variables. The correlation analysis further indicated that the runoff volume and sediment yield were extremely significantly correlated with the rainfall intensity and hydraulic parameters (V, Fr, and f) for both the IT and NT (p < 0.01), in which there was a positive correlation between surface runoff and sediment yield with V, Fr, and rainfall intensity, but a negative correlation was observed with f (Figure 6). The results suggested that V, Fr, and rainfall intensity increased with increasing soil and water loss, while f decreased with increasing soil and water loss, irrespective of the IT and NT. It is worth noting that both RS and Re were not significantly correlated with runoff and sediment yield (p > 0.05). Overall, our analysis showed that the impacts of RS on soil and water loss depended mainly on rainfall intensities and tillage intensities.

The SEM analyses indicated that the impact of RI and RP on RA and SL was mediated through hydraulic parameters (V, Re, Fr, and f) under the NT and IT, and each had a good fitting value (the CFI was 0.81 and 0.78 in the NT and IT, respectively; Figure 7). RI had significant positive impacts on V and Fr in both tillage treatments (path coefficient of V = 0.98 in the NT and 0.96 in the IT; Fr = 0.95 in the NT and 0.93 in the IT). Yet RI had significant negative effects on the f of both tillage treatments with a path coefficient of −0.87 in the NT (Figure 7a) and −0.86 in the IT (Figure 7b). Both V and f had a significantly negative impact on RA (path coefficient of V = −1.33 in the NT and −1.07 in the IT; f = −0.25 in the NT and −0.17 in the IT). RI showed the largest significant impacts on SL in both tillage treatments with a path coefficient of 1.46 in the NT and 1.74 in the IT. RP had a slight impact on RA (path coefficient of 0.02 and 0.05 in the NT and IT, respectively). These results showed that the RI and RP directly affected V, Re, and Fr and negatively affected f for both the NT and IT. In addition, V, Re, and f directly affected SL, irrespective of tillage treatments.

To examine this result further, the surface runoff and sediment yield produced by the same rainfall intensity among different rainfall patterns are compared in

Table 3. Comparison of runoff amount and soil loss for the same rainfall intensity with different rainfall patterns and stages.

Rainfall Intensity (mm h−1)	Rainfall Pattern	Rainfall Stage	Surface Runoff (L)			Sediment Yield (kg)
			NT	IT	Contribution Rate (%) 1	NT	IT	Contribution Rate (%) 2
30	RR	1	16.15	20.61	21.64	0.04	0.19	78.95
	FR	5	19.91	34.58	42.42	0.19	0.63	69.84
	RFR	1	13.51	25.20	46.39	0.03	0.05	40.00
	RFR	5	23.67	32.14	26.35	0.06	0.12	50.00
	FRR	3	10.56	17.90	41.01	0.08	0.12	33.33
60	RR	2	55.97	70.48	20.59	0.41	0.80	48.75
	FR	4	62.44	76.01	17.85	0.68	1.43	52.45
90	RR	3	118.08	133.08	11.27	1.37	1.76	22.16
	FR	3	112.72	133.34	15.46	2.12	3.11	31.83
	RFR	2	130.67	146.20	10.62	2.85	3.84	25.78
	RFR	4	133.60	156.83	14.81	1.04	1.51	31.13
	FRR	2	109.10	115.80	5.79	2.09	2.71	22.88
	FRR	4	132.11	136.45	3.18	1.74	2.17	19.82
	CR	1	96.75	112.33	13.87	2.59	5.56	53.32
	CR	2	130.40	135.67	3.88	1.46	3.23	54.84
	CR	3	127.46	141.36	9.83	1.12	1.76	36.39
	CR	4	129.30	138.55	6.67	1.08	1.51	28.62
	CR	5	129.69	136.88	5.25	1.08	1.43	24.40
120	RR	4	162.40	182.47	11.00	2.76	3.24	14.81
	FR	2	168.20	182.23	7.70	3.70	4.91	24.64
150	RR	5	239.31	254.74	6.06	3.99	4.44	10.14
	FR	1	188.63	215.20	12.35	6.64	7.92	16.16
	RFR	3	274.09	281.90	2.77	5.55	6.40	13.28
	FRR	1	196.51	231.65	15.17	5.56	8.84	37.10
	FRR	5	193.88	204.91	5.38	3.72	4.73	21.35

Note: ¹ Contribution rate is the ratio of (Runoff_IT–Runoff_NT) to Runoff_IT. ² Contribution rate is the ratio of (Soil loss_IT–Soil loss_NT) to Soil loss_NT.

. According to Table 3, the runoff volume from the different rainfall intensities was in the range of 17.90–281.90 L, with an average of 132.66 L, and 10.56–274.09 L, with an average of 119 L, for the IT and NT, respectively. The sediment yield produced ranged from 0.05 to 8.84 kg for the IT and from 0.03 to 6.64 kg for the NT. These data demonstrate that intensive tillage operations play a major role in increasing soil and water loss on landscapes with steep slopes. When the rainfall intensity increased from 30 to 150 mm h⁻¹, the average surface runoff and sediment yield increased for both the NT and IT regardless of the rainfall stage or pattern, while no clear trend was found among the different rainfall patterns. It was observed that the sediment yield from the same rainfall intensity was significantly impacted by the different rainfall stages and patterns. The highest sediment yields were 8.84 and 6.64 kg for the IT and NT, respectively, and these were produced when the 150 mm h⁻¹ intensity occurred at stage 1 in the FRR pattern for the IT and at stage 1 in the FR pattern for the NT. When comparing sediment yield values in stage 1, it can be seen that in all rainfall patterns, the sediment yield was enhanced with the decreasing rainfall intensity. This finding suggests that the impacts of rainfall stage on soil and water loss depended mainly on rainfall intensity.

A contribution rate, defined as the ratio of surface runoff to sediment yield, was employed in this study to evaluate the net contributions of intensive tillage on soil and water loss for each rainfall intensity and stage (Table 3). From these findings, it is clear that the net influence of tillage erosion on surface runoff and sediment yield decreased with increasing rainfall intensity on sloping farmland. The contributions of intensive tillage to surface runoff and sediment yield were approximately 15.09% (range of 2.77–46.39%) and 34.48% (range of 10.14–78.95%), respectively, among the different rainfall patterns. The results displayed a decreasing trend of the average contribution rate for soil loss among the five intensities, with 54.42% for 30 mm h⁻¹, 50.60% for 60 mm h⁻¹, 31.92% for 90 mm h⁻¹, 19.73% for 120 mm h⁻¹, and 19.61% for 150 mm h⁻¹, and the change tendency of the surface runoff was much more similar to that of the sediment yield. This result indicates that intensive tillage produces more serious soil loss than no-tillage operations.

3.4. The Relationship between the Sediment Yield Rate and Surface Runoff Rate under Different Rainfall Patterns

The sediment yield rate (Y) was a function of the surface runoff rate (X) for the varying-intensity rainfall patterns, and their relationship can be well described with the following linear equation:

Y = a X + b

(5)

where Y is the sediment yield rate (g m⁻² min⁻¹), X is the runoff rate (L min⁻¹), a is a regression coefficient (g m⁻² L⁻¹) describing soil erodibility, and b is also a regression coefficient (g m⁻² min⁻¹).

The analysis showed that the sediment yield rate was significantly and linearly correlated with the surface runoff rate for each rainfall event (p < 0.05), yet there was no significant linear relationship between the sediment yield rate and runoff rate under the CR pattern for both the NT and IT (p > 0.05). The descriptive coefficients a and b of the regression equations showed different variation features of soil and water loss between the NT and IT under different rainfall patterns (

Table 4. Relationship between the surface runoff rate and the sediment yield rate under different rainfall patterns.

Treatment	Rainfall Pattern	Function Equation	R2	p
NT	RR	Y = 1.8518 X − 3.9866	0.9398	<0.01
	FR	Y = 3.3914 X − 8.9665	0.6984	<0.01
	CR	Y = 0.6694 X + 5.2242	0.0.024	>0.05
	RFR	Y = 2.1043 X − 3.6960	0.9353	<0.01
	FRR	Y = 2.6704 X − 5.4911	0.6389	<0.01
IT	RR	Y = 1.8797 X − 3.3415	0.9391	<0.01
	FR	Y = 3.8256 X − 10.8770	0.7786	<0.01
	CR	Y = 0.5210 X + 16.8220	0.0042	>0.05
	RFR	Y = 2.4398 X − 5.6022	0.8704	<0.01
	FRR	Y = 3.4213 X − 10.4310	0.7602	<0.01

Note: X is the surface runoff rate (L min⁻¹) and Y is the sediment yield rate (g m⁻² min⁻¹), which is calculated for the total area of the steel tanks.

), implying that the significant impact of the rainfall pattern on soil and water loss was influenced by different tillage intensities. The values of coefficient a were larger for the IT treatment than for the NT treatment under the same rainfall patterns. This result shows that tillage operation exerts a critical effect on surface runoff and sediment yield in sloping landscapes.

4. Discussion

The FR and FRR exhibited more cumulative sediment production and greater time gaps between rainfall initiation and runoff occurrence during an event compared to other patterns. This finding is consistent with the conclusions of Wang et al. [2], who reported that earlier high-intensity precipitation sequences produced resulted in larger soil and water loss. One explanation for this trend is that the heavy rainfall intensity of 150 mm h⁻¹ lasting for 12 min and 10.60 min, respectively for the FR and FRR, quickly generated surface runoff in the initial stage of rainfall. Soil moisture content rapidly reaches saturation when heavy rainfall occurs at the beginning, and most rainfall does not have enough time to infiltrate the soil. Additionally, rainfall mixes with loose soil particles to form a slurry that blocks soil air from exiting the void, which substantially reduces the infiltration rate. Steep slopes’ soil loss can be exacerbated by water accumulation, downslope rainfall flow, and soil particle movement when infiltration is limited during the rainfall event [27]. Moreover, the surface of bare soils is very susceptible to forming soil crusts or seals due to fine particles splashed by raindrops, which block soil pores under high-intensity rainfall events [28]. Besides, soil loss was found to be greater on crusted surfaces due to both a higher runoff volume and lower hydraulic resistance, regardless of the specific scenario. Fox et al. [29] and Marzen et al. [30] similarly noted that intensive tillage can significantly increase erosion by bringing loose sediments to the surface. In this context, soils caused by tillage that accumulate at lower-slope positions can be easily carried away during high-intensity rainfall events, as there may be no bank to retain them.

A linear equation modeled the relationship between the sediment yield rate and surface runoff rate for varying-intensity rainfall patterns. Our findings support those of Wang et al. [31], who observed a positive correlation between the sediment yield rate and runoff rate on bare soil. However, these results are inconsistent with previous research that found a negative relationship between the sediment yield rate and runoff rate on shrub land and grass plots [26,32]. In our study, sediment production increased with increasing runoff volume, which may be attributed to the increased transport capacity for carrying detached sediment as the runoff volume increases on bare soils, regardless of the NT and IT, due to the lack of a barrier that can stop surface runoff on slopes. Previous research has shown that vegetation coverage can effectively decrease soil and water loss on hillslopes [33]. Additionally, the correlation between the runoff rate and sediment yield rate is often used as an indicator of soil erodibility, with the slope of the regression equation (the regression coefficient (a)) considered a critical parameter of soil erodibility [31]. The values of a between the NT and IT treatments were different, with larger values for the IT treatment and lower values for the NT treatment. This result is consistent with those of previous research indicating that increased tillage erosion leads to an increase in soil erodibility [17,34]. This difference primarily results from the distinctly different soil texture and thickness of soil profiles on the upper-slope positions between the two tillage treatments. Tillage erosion is a gradual process of downslope transport resulting from consecutive and intense tillage that can cause notable changes in soil properties, adversely influencing soil structure [12]. Under these conditions of tillage-caused finer fragmentation, the intensity of tillage erosion is directly linked to soil erodibility on sloping farmland [35,36].

The time gap between the rainfall initiation and runoff occurrence after the IT was shorter than that for the NT under the same rainfall pattern. This happened is because only a small amount of rainfall was absorbed due to the lack of soil profiles at the upper-slope positions. Due to the thin soil profiles, bedrock became the impeding layer to infiltrating water, which had a faster flow velocity and stronger water conductivity. This made vertical infiltrating water being turned to the downslope when it reached the soil–bedrock interface, forming soil interflow that moved along the line of the slope [12]. This condition (a thin soil layer caused by tillage erosion at summit positions) can significantly exaggerate soil and water loss on hillslopes. Moreover, intensive tillage influences soil structure, overland water flow, and hydrological processes by decreasing the infiltration rate and water-holding capacity [12,37]. This process has been demonstrated as the dominant soil hydrological process in areas with a shallow soil depth [38,39], and the process triggers greater soil and water loss at lower-slope positions than in the thick soil layers at upper-slope positions. If the soil infiltration and water-holding capacity were limited, then the water accumulation and flow downslope would aggravate the extent of water loss during the rainfall event.

Compared with the no-tillage operation, intensive tillage erosion can significantly accelerate soil loss by carrying surface runoff, decreasing infiltration rates, and intercepting sediment matter. The explanation for these findings is that severe erosion by tillage can completely remove the Ap horizons, especially in the upper-slope positions. The thickness of topsoil horizons decreases significantly with an increase in soil erosion caused by long-term tillage operations, resulting in a reduction in the total depth of the soil profile [17,21]. Intensive tillage erosion can expose bedrock in summit positions, which is an extreme phenomenon that occurs in the cultivated soils of purple regions and has crucial implications for soil and water loss. Soil erosion causes the upper horizons to be shortened and the subsurface horizons to be exposed, resulting in a rapid reduction in organic matter and nutrients, as well as a decline in the physical properties of the soil near the surface [40]. The surface soil plays a crucial role in dividing rainfall into various hydrological components and regulating surface runoff. As a result, the loss of topsoil leads to a reduction in the soil’s capacity to retain water and nutrients. Furthermore, the exposure of subsurface horizons increases the risks of surface runoff and soil loss due to decreased soil structural development and weak anti-erodibility [41,42]. Hence, soil and water loss by rainfall was exaggerated by intensive tillage on sloping farmland.

This study highlights the significant impact of variations in rainfall patterns on the surface runoff and sediment yield occurrence on tilled slopes. However, it is difficult to reliably predict the influences of different topographic conditions (e.g., slope gradient, length, and shape) on the soil erodibility and hydrological properties due to our limited experiments. Further research is necessary to examine the correlation between tillage erosion processes and rainfall patterns in mechanized agriculture areas. Relevant gaps in knowledge are needed to be filled in future studies, so as to have general applicability to other topographic conditions and tillage implements. This information will be crucial in identifying ecological hazards and developing effective management strategies to address the significant threat that soil erosion poses to our land resources.

5. Conclusions

To examine the impacts of rainfall patterns on surface runoff and sediment yield and quantify the net contributions of intensive tillage on soil and water loss in the purple soil area of China, artificial rainfall events with the same total rainfall of 90 mm but different sequences of rainfall intensities were applied to steel tanks (2 m × 5 m). Significant differences in surface runoff and sediment yield were observed across various rainfall patterns and stages. The sediment yield by intensive tillage erosion exhibited obvious differences among the five different rainfall patterns in this study, with the following order: FRR > FR > RFR > CR > RR, and the result largely depended on the heavy rainfall sequences during an event, showing that intensive tillage erosion’s influences on soil and water loss are closely related to the rainfall patterns during an event. On average, intensive tillage contributed to 15.09% of surface runoff and 34.48% of sediment yield. Compared with no-tillage treatment, cumulative sediment yield by intensive tillage erosion increased by an average of 41.24% (with a range of 21.69–83.95%) under the different rainfall patterns. Surface runoff and sediment yield were positively correlated with V and Fr but negatively correlated with f, regardless of tillage operation. The results suggest that soil loss due to rainfall events is exaggerated by tillage erosion on steep slopes. Specifically, the thin soil layers caused by intensive tillage erosion at upper-slope positions may increase overland water flow, leading to water erosion and potential soil loss.