Spatiotemporal Trends and Variations in Rainfall Erosivity in the East Qinling Mountains and the Environmental Impacts

Abstract

A better understanding of the spatiotemporal variation characteristics of rainfall erosivity and effects of extreme rainfall events on soil erosion is the basis for improved water resource planning, protection, and ecological restoration in the Qinling Mountains. Using long-term daily precipitation data from 19 national standard meteorological stations from 1957 to 2018, the spatiotemporal variation trend of rainfall erosivity was explored. A linear regression analysis method was used to detect trends in rainfall erosivity. The spatial pattern of rainfall erosivity, which is based on annual, seasonal, and extreme rainfall indices, was analyzed via a geospatial interpolation method. Effects of natural factors and human activities on soil erosion at different stages were examined via the double cumulative curve method. The average annual rainfall erosivity in the Shangluo area is 2306 MJ mm ha⁻¹ h⁻¹ year⁻¹ and generally displays a gradual decreasing trend from southeast to northwest. Over the last 60 years, the annual R exhibited a nonsignificant increasing trend (p > 0.05). Overall, rainfall erosivity showed a phased trend with an increasing trend after 2000. Rainfall erosivity from June to September accounts for 78.5% of the annual total, while the annual R is mainly determined by a few rainfall events during the year. RX1d and RX5d account for 20–40% and 60–80%, respectively, of the total annual R and are likely to result in severe soil erosion in sloping cultivated land areas, agricultural lands, and dirt roads with continued climate change. Implementation of the National Natural Forest Protection Project and the ‘Grain for Green’ Project significantly reduced the intensity and scope of soil erosion in the area. This study aids in understanding the ecohydrological processes and soil erosion and sediment transport characteristics in the Qinling Mountains and promotes water resource protection and management along the middle route of the South-to-North Water Diversion Project.

1. Introduction

With global warming and an increased frequency of disaster events, global and regional climate change and the associated environmental impacts have become globally important scientific issues of concern [1,2,3,4,5,6,7,8]. Under the dual effects of climate change and anthropogenic activities, regional ecological and environmental problems, such as soil erosion, soil and water loss, reduced river runoff, frequent droughts and floods, soil stress, reduced biodiversity, and intensified ecological crises, have become increasingly prominent, and agricultural and food security problems have become increasingly serious [9,10,11,12,13]. To effectively address these prominent crises and achieve the coordinated development of land and environmental preservation, it is important to conduct climate change-related research. These efforts are conducive to promoting scientific understanding and mechanism research, establishing effective measures to mitigate the adverse effects of climate change, and to ensure global environmental health and ecological security and thereby promote sustainable social and economic development.

Studies of rainfall erosivity are critical in hydrological cycle science and highly important for predicting soil loss and making soil and water conservation decisions [14,15]. Rainfall erosivity refers to the potential for soil erosion caused by rainfall. This reflects the destructive effect of rainfall when it reaches the soil, and it is mainly related to the rainfall intensity, rainfall amount, rainfall duration, and kinetic energy of rain drops and other factors. It is also affected by factors such as the rain pattern, topography, soil type, and vegetation coverage. Interactions among these factors complicate analyses of rainfall erosivity [16,17,18,19]. Heavy rainfall often causes soil loss, ecological degradation, reduced agricultural production, reservoir siltation, and infrastructure damage [20,21]. Rainfall erosivity directly affects the extent of soil loss and is an essential indicator used in soil erosion prediction and prevention [4,22]. Accurate assessments of rainfall erosivity can provide a scientific basis for preventing and controlling soil erosion. Additionally, strengthening vegetation restoration, adjusting agricultural production methods, and promoting the construction of water and soil conservation projects are effective measures for reducing rainfall erosivity and soil loss [12,14,23,24,25,26,27,28,29].

The Qinling Mountains, which are known as China’s “Central Water Tower”, are among the most important ecological barriers in China [30]. Most cultivated land is sloping farmland, and soil erosion is severe because of the influence of concentrated heavy rainfall in summer. Since 2000, major ecological projects, such as the “Returning Farmland to Forests and Grassland Project” and the “Natural Forest Protection Project”, have been implemented in the Danjiang River Basin. As an essential water source for the middle route of the South-to-North Water Diversion Project, this area supplies clean water to Beijing and Tianjin [31,32,33]. Changes in the amount of water resources in water source areas influence resource and ecological security in water-receiving areas, water source areas, and the middle and lower reaches of the Han River. Shangluo has a transitional climate type from north to south. Thus, this geographical location is a key area for studying climate change. Changes in runoff and sediment transport caused by rainfall erosivity in the Shangluo area also reflect the evolution of the ecological functions of the Qinling Mountains. In the past, there has been some research conducted on changes in rainfall erosivity and its environmental impacts in the Qinling Mountains and surrounding areas [3,7,28,33,34,35,36,37,38,39,40,41]. Li et al. [40] reported that the average annual rainfall erosivity in the north–south transition zone of China is 4197.85 MJ mm ha⁻² h⁻¹ year⁻¹ with a spatial variation trend that is greater in the southeast and lower in the northwest. Wang and Su [36] evaluated soil erosion in the Qinba Mountains of southern Shaanxi Province and reported that areas with slopes greater than 35 degrees and central mountainous areas (800–2000 m) are characterized by the highest erosion intensities. In recent years, the runoff regime in the Danjiang River Basin has significantly changed. The contribution of anthropogenic activities to the reduction in runoff was reported to range from 133.66 to 147.50%, and the contribution rate of climate change to the decrease in runoff ranged from −33.66 to 47.50% [33].

Although theoretical and practical research on RE is highly important for understanding the current ecological environment, soil erosion characteristics, and environmental impacts in the Qingling Mountains, there are still some deficiencies in the current research on RE. Specifically, there have been few studies on rainfall erosivity in the Qinling Mountains. The spatiotemporal variation characteristics of rainfall erosivity were analyzed with data from a few meteorological stations. There have been few analyses of the contributions of extreme rainfall events to RE and the corresponding impacts on soil erosion. Notably, potential effects of climate change and human activities on RE may have not been fully considered in existing studies, which would result in inaccurate assessments of RE in the study area. Additionally, from the perspective of national strategies, the impacts of soil and water conservation and ecological protection on reducing the area and intensity of soil erosion in the Qinling Mountains are poorly understood. Additionally, the practical significance of this area in supporting the stable implementation of the National South-to-North Water Diversion Project and realizing the protection of high-quality water sources to provide clean water to water-scarce areas in northern China has not been clarified. Considering the current soil erosion situation in the study area, combined with the national strategy, there is an urgent need to implement effective ecological protection measures and scientific management in areas with a potentially high incidence of soil erosion according to local conditions.

To accurately assess the trends of rainfall erosivity and the corresponding environmental impact in the Qingling Mountains as well as support water source protection along the middle route of the South-to-North Water Diversion Project, this study focuses on the Shangluo area. We explore trends in rainfall erosivity and corresponding environmental impacts based on long-term time-series data. The purposes of this study include the following: (1) clarify the interannual and intra-annual variation characteristics of rainfall erosivity in the study area, (2) clarify the relationship between rainfall erosivity and regional climate change, (3) assess the contribution of extreme rainfall events to rainfall erosivity and phase change characteristics, and (4) analyze the impact of rainfall erosivity on regional agricultural production and management. In general, this study helps enrich research on rainfall erosivity in the Qinling Mountains, provides a deep understanding of the rainfall erosivity trend and the corresponding impact on soil erosion and runoff and sediment transport, provides a scientific basis for water resource evaluation and management at the southern foot of the Qinling Mountains in the context of a changing environment, and promotes the protection of the ecological environment in water source areas.

2. Materials and Methods

2.1. Study Site

The Shangluo area is located in southeastern Shaanxi Province and is part of the hinterland of the Qinling Mountains (108°34′20″~110°01′25″ E, 33°02′30″~34°24′40″ N) (Figure 1). It spans the Yangtze River and the Yellow River and is mainly located in the Yangtze River Basin. It is an important water conservation area along the middle line of the National South-to-North Water Transfer Project [33,38]. The main rivers in the Shangluo area are the Danjiang River, Luohe River, Jinqian River, Qianyou River, and Xunhe River. The terrain is characterized by a mountainous canyon-like landscape that is high in the northwest and low in the southeast. The Shangluo area has a mild climate and receives abundant rainfall; overall, it has a warm temperate semi-humid mountainous monsoon climate with average annual precipitation totaling approximately 758 mm, an average annual temperature of 13.5 °C, and a forest coverage rate of 69.56%. The vegetation types are mainly broad-leaved evergreen forest, broad-leaved deciduous forestland, and mixed coniferous forest and broad-leaved forest. Owing to differences in geographical features, monsoons, and other factors, the temporal and spatial differences in precipitation in the Shangluo area are obvious, and more than 80% of the rainfall is concentrated in summer and autumn. Soil erosion is most severe in the gullies and tableland zones in the study area. Common weather hazards include drought, heavy rainfall, continuous rainfall, hail, frost, strong winds, and cold waves. The main soil types are brown soils, yellow-brown soils, and brown loam soils, which are characterized by horizontal zonality, vertical zonality, and regional characteristics [35,38].

2.2. Data Sources and Processing

In this work, the monthly, seasonal, and annual rainfall erosivity values were calculated using daily precipitation data from 19 national standard meteorological stations [18]. The seasons in the paper were divided into general meteorological periods as follows: March to May is spring, June to August is summer, September to November is autumn, and December to February of the following year is winter [34].

The annual runoff and sediment data were obtained from the runoff and sediment dataset for soil and water conservation in Shaanxi province.

In this work, a trend analysis of annual rainfall erosivity data from 1957 to 2018 at 19 meteorological stations in the study area was carried out via linear regression analysis based on traditional mathematical and statistical methods [42]. Using a normal Q–Q plot, an exploratory spatial data analysis tool, and Eviews 7 software, the relationships between rainfall erosivity and seasonal, flood season, non-flood season, annual scale, and extreme rainfall indices were tested for normality and checked for spatial autocorrelation to determine whether spatial interpolation could be carried out via the kriging method. The two extreme precipitation indices selected in this study are shown in

Table 1. Indices of extreme precipitation selected in this study.

Index Type	Number	Index	Description	Definition	Units
Intensity	1	RX1d	Maximum 1-day precipitation	Annual maximum for 1-day precipitation	mm
	2	RX5d	Maximum 5-day precipitation	Annual maximum for 5-day precipitation	mm

. The annual rainfall erosivity data from 19 meteorological stations in Shangluo and adjacent areas were spatially interpolated and analyzed via the kriging interpolation method in ArcGIS 10.2 software. The R data were analyzed via Origin 8.0 and SPSS software (significance level α = 0.05).

2.3. Methods

2.3.1. Calculation of Rainfall Erosivity

In this study, the R values corresponding to daily, semimonthly, monthly, seasonal, flood season, nonflood season, annual scale, and extreme rainfall indices (RX1d and RX5d) were calculated via the classical rainfall erosivity calculation method using daily rainfall data from 19 meteorological stations in the study area and its adjoining districts [18,37].

The formulas are as follows:

$$\overline{R}={\displaystyle \sum _{k=1}^{24}{\overline{R}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}}$$

(1)

$${\overline{R}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}={\displaystyle \frac{1}{N}}{\displaystyle \sum _{\mathrm{i}=1}^N{\displaystyle \sum _{j=0}^m\left(\alpha \times P_{i,j,k}^{1.7265}\right)}}$$

(2)

$${\overline{WR}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}={\displaystyle \frac{{\overline{R}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}}{\overline{R}}}$$

(3)

where $\overline{R}$ indicates the mean annual rainfall erosivity (MJ mm ha⁻¹ h⁻¹ yr⁻¹); $\mathrm{k}$ = 1, 2, …, 24 indicates 24-half months in a year; ${\overline{R}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}$ is the rainfall erosivity estimated for the k-th half month (MJ mm ha⁻¹ h⁻¹ yr⁻¹); $\mathrm{i}$ = 1, 2, …, N represents the time nodes 1957–2018; $\mathrm{j}$ = 0, …, m, where m denotes the days with erosive precipitation in a half month in year $\mathrm{i}$ (days of erosive precipitation refers to daily precipitation ≥ 12 mm); and $P_{i,j,k}$ is the total erosive daily precipitation (mm) on the j-th day in the k-th half month of year $\mathrm{i}$. When no erosive precipitation occurred in a certain half month of a year, i.e., $\mathrm{j}$ = 0, then $P_{i,j,k}$ = 0; $\alpha$ is a parameter that has a seasonality feature (in the warm season of May–September, $\alpha$ is 0.3937; in the cold season of January–April and October–December, $\alpha$ is 0.3101); and ${\overline{WR}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}$ refers to the ratio of the mean rainfall erosivity of the $\mathrm{k}$-th half month (${\overline{R}}_{\mathrm{half}-\mathrm{month} \mathrm{k}}$) to the mean annual rainfall erosivity ($\overline{R}$).

2.3.2. Linear Regression Analysis Method

Linear regression is a type of regression analysis that models the relationship between one or more independent and dependent variables via a least square function called a linear regression equation.

The climate trend reflects the changes in climate factors over time and can generally be expressed with a one-way linear regression equation.

$$y=a\mathrm{t}+\mathrm{b}$$

(4)

where y is the series of meteorological elements (temperature, precipitation, or other variables), t represents the time series (1957–2018 in this case), b is the intercept, and a is the regression coefficient. When a > 0, the element y increases linearly in the computational period t and vice versa. The rate of climate change is determined and used for the quantitative linear analysis of climate elements [42].

2.3.3. Cumulative Anomaly Method

A cumulative anomaly is based on the cumulative value of the difference between a feature value and the corresponding multiyear average value [43,44]. The main idea is to judge the degree of discreteness between values in a dataset and the corresponding mean. If the curve exhibits an upward trend, then the cumulative anomaly increases, and the discrete data are greater than the mean. If the curve displays a downward trend, then the cumulative anomaly decreases, and the discrete data are less than the mean. The cumulative anomaly of a sequence is represented by the following formula.

$$\overline{x_t}={\displaystyle \sum _{i=1}^t(x_i-\overline{x})}, t=1,2,\cdots ,n$$

(5)

$$\overline{x}={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n{x}_i}, i=1,2,\cdots ,n$$

where $\overline{x_t}$ is the cumulative anomaly, $x_i$ is the observed value at a certain point in a time series, and $\overline{x}$ is the mean of all observations.

2.3.4. Double Cumulative Curve Method

The double cumulative curve method is currently the simplest, most intuitive, and most widely used method for analyzing the consistency or long-term evolution trend of hydrometeorological elements. The double accumulation curve plots the consecutive cumulative values of two variables over the same period in a rectangular coordinate system, and it can be used to test the consistency of hydrometeorological elements, interpolate missing values, perform data correction, and analyze the trend and intensity of changes in hydrological elements. The effects of climate change and human activities on hydrological evolution were quantified by examining the characteristics of periodic changes in hydrological series [45,46,47].

2.3.5. Spatial Interpolation Method

In this study, the coordinate systems of the meteorological stations were converted into a consistent format, and then the basic meteorological station information (the station name, longitude and latitude coordinates, elevation, and precipitation data) was loaded into ArcGIS to create a point shape file with attribute information. The rainfall erosivity data for the 19 meteorological stations passed the normal distribution test. Therefore, the ordinary kriging interpolation method was used for spatial interpolation. The missing values of daily rainfall erosivity at these meteorological stations were interpolated and extended to 1957 via the regression analysis method and the ordinary kriging interpolation method. Simultaneously, anomaly values were detected and eliminated. The regional rainfall erosivity estimated from the 19 meteorological stations for each year and the long-term average values in the Shangluo area from 1957 to 2018 were obtained via the ordinary kriging interpolation method [34,37].

3. Results

3.1. Interannual Variations in Rainfall Erosivity

As shown in Figure 2a, the total annual erosive rainfall was higher in the south than in the north, decreasing from the highest value of 569 mm in the south to 434 mm in the north, and the average annual amount of erosive rainfall was 498 mm. The maximum value was 778.8 mm, the minimum value was 312.2 mm, and the interannual fluctuations were significant, with the maximum value reaching 2.49 times the minimum value (Figure 2b). The annual erosive rainfall has exhibited a nonsignificant increasing trend over the past 60 years (p > 0.05).

Figure 2c shows that the spatial difference in the average annual R in the Shangluo area is noticeable, and the maximum value is approximately 1.5 times the minimum value. High-value regions occurred in the southeastern and southwestern parts of the study area, and low-value regions appeared near the meteorological station in Shangzhou. Over the past 60 years, the average annual rainfall erosivity in the Shangluo area reached 2306 MJ mm ha⁻¹ h⁻¹ year⁻¹. During this period, the annual R exhibited a nonsignificant increasing trend (p > 0.05). The maximum R value was 4495 MJ mm ha⁻¹ h⁻¹ year⁻¹ and the minimum value was 1480 MJ mm ha⁻¹ h⁻¹ year⁻¹ with considerable fluctuations from year to year; overall, the maximum value was 3.04 times greater than the minimum value (Figure 2d).

During the study period, except in a few areas in the north, the annual rainfall erosivity in the Shangluo area was spatially characterized by an increasing trend, but the growth was slow in most areas (Figure 2e). The coefficient of variation of annual rainfall erosivity displayed a decreasing trend from southeast to northwest. The maximum coefficient of variation was 50.4% at the Shangnan meteorological station, and the minimum value was 36.3% in the northern part of the Shangluo area (Figure 2f).

According to

Table 2. The stage variation in annual rainfall erosivity (MJ mm ha⁻¹ h⁻¹ year⁻¹) in the Shangluo area from 1957 to 2018.

Period	Mean	Maximum	Minimum	${\mathit{C}}_{\mathit{v}}$
Ⅰ (1957–1969)	2269	4387	1480	0.37
Ⅱ (1970–1979)	2123	2454	1673	0.14
Ⅲ (1980–1989)	2595	4495	1753	0.30
Ⅳ (1990–1999)	2018	3378	1621	0.25
Ⅴ (2000–2009)	2386	3150	1568	0.22
Ⅵ (2010–2018)	2587	3882	1572	0.31

, the annual R values of the six stages over the past 60 years in the Shangluo area have generally showed an increasing trend (p > 0.05), and the trend from stages Ⅰ to Ⅳ decreased while stages Ⅴ to Ⅵ increased. The maximum annual R in the six stages showed a nonsignificant decreasing trend (p > 0.05). In contrast, the minimum value showed an increasing trend, and the coefficient of variation exhibited a decreasing trend (p > 0.05).

3.2. Seasonal Variations in Rainfall Erosivity

The distribution of monthly rainfall erosivity in the Shangluo area was uneven and displayed a clear single-peak pattern. Rainfall erosivity in July reached an annual maximum, and rainfall erosivity from June to September accounted for 78.5% of the annual total (Figure 3 and

Table 3. Rainfall erosivity (MJ mm ha⁻¹ h⁻¹ year⁻¹) and its proportion in different seasons in the Shangluo area.

Name of Periods	Range of Periods	Rainfall Erosivity	Proportion of Rainfall Erosivity (%)
Spring	March to May	339.5	14.5
Summer	June to August	1452.5	61.8
Autumn	September to November	536.1	23.3
Winter	December to the following February	9.8	0.4
Wet season	May to October	2172.5	93.0
Dry season	November to the following April	165.3	7.0
June–September	June to September	1833.2	78.5
Annual	January to December	2337.8	100

). The rate of increase in rainfall erosivity from March to July was notably lower than the rate of decrease from July to October. The most significant monthly changes were observed in the periods from June to July and September to October (Figure 3).

Differences in rainfall erosivity in different seasons were noticeable in the Shangluo area. R was lowest in winter, highest in summer, moderately high in autumn, and moderately low in spring (Figure 4).

Figure 4a shows that the spatial distribution of rainfall erosivity in spring was generally homogeneous in the study area and ranged between 200 and 400 MJ mm ha⁻¹ h⁻¹ year⁻¹. The rainfall erosivity in spring accounted for 14.5% of the annual total (Table 3). The spatial difference in rainfall erosivity in summer is significant and decreases from nearly 2000 MJ mm ha⁻¹ h⁻¹ year⁻¹ in Southeast China to approximately 1000 MJ mm ha⁻¹ h⁻¹ year⁻¹ in Northwest China (Figure 4b). The rainfall erosivity in summer accounted for 61.8% of the annual total (Table 3). The rainfall erosivity in autumn was mainly between 400 and 600 MJ mm ha⁻¹ h⁻¹ year⁻¹ (Figure 4c) and accounted for 23.3% of the annual total (Table 3). The rainfall erosivity in winter in the whole region was generally lower than 200 MJ mm ha⁻¹ h⁻¹ year⁻¹ (Figure 4d) and accounted for 0.4% of the annual total (Table 3). The rainfall erosivity in the wet season was generally high in the southern region and low in the northern region, with the second-highest values observed in the northernmost part of the study area (Figure 4e). The rainfall erosivity in the wet season accounted for 93.0% of the annual total (Table 3). The spatial difference in rainfall erosivity in the dry season was not significant and was lower than 200 MJ mm ha⁻¹ h⁻¹ year⁻¹ in most areas (Figure 4f), which accounted for 7.0% of the annual total (Table 3).

3.3. Relationship between Rainfall Erosivity and Climate Change

Regional climate change profoundly affects the spatiotemporal distribution characteristics of rainfall erosivity and the corresponding quantitative relationships. Figure 5a shows that annual precipitation and annual rainfall erosivity have been significantly and positively correlated in the Shangluo area over the last 60 years (p < 0.05). Furthermore, the correlation between annual erosive rainfall and annual rainfall erosivity reached a highly significant level (p < 0.01), which indicates that annual rainfall erosivity is influenced mainly by a few rainfall events during the year (Figure 5b,c). In the past 60 years, the number of days of erosive rainfall in the study area each year has not significantly increased, while the intensity of erosive rainfall has displayed a more obvious increasing trend (Figure 5c), which suggests that rainfall erosion may cause severe soil erosion threats to sloping farmland and other erosion-prone areas in the future.

3.4. Variations in Rainfall Erosivity during Extreme Rainfall Events

From the perspective of spatial distribution, RX1d in the Shangluo area over the past 60 years has ranged from 412.7 to 600 MJ mm ha⁻¹ h⁻¹ year⁻¹ and only exceeded 600 MJ mm ha⁻¹ h⁻¹ year⁻¹ in parts of the southeastern and southwestern regions. Correspondingly, RX1d accounted for 20–40% of the total annual R value (Figure 6a,b).

The spatial distribution of RX5d was heterogeneous with a gradual decreasing trend from southeast to northwest, and primarily occurred at a rate of between 1200 and 1725.6 MJ mm ha⁻¹ h⁻¹ year⁻¹. Accordingly, RX5d in most areas of the Shangluo area accounted for 60–80% of the total annual R value, although it reached only 40–60% in the southeastern region (Figure 6c,d).

From a time series perspective, RX1d and the proportion of RX1d to the total annual R in the Shangluo area were characterized by nonsignificant increases (p > 0.05). Additionally, RX5d and the proportion of RX5d to the total annual R did not significantly increase (p > 0.05). RX1d and RX5d increased significantly faster than the corresponding proportions of these values to the total annual R, which indicated that the extreme characteristics of precipitation in the study area were prominent (Figure 6e,f).

3.5. Effects of Rainfall Erosivity on Agricultural Management

In the land use classification of the Shangluo area, woodland areas accounts for the most significant proportion at 81.62%. Cultivated land, accounting for 10.24%, is distributed mainly in the Shangdan Basin, Luonan Basin, river valleys and terraces. The proportion of grassland is 2.93%, while water area accounts for 1.29%, and other land use types account for 3.92% (Figure 7a). Correspondingly, abundant forestlands with high coverage are associated with reduced soil erosion intensity, which reduces the overall water erosion intensity in the study area. The strong, very strong, and severe water erosion zones are spatially limited and mainly concentrated in the agricultural areas in the Luonan Basin and Danjiang River, Jinqian River, and Qianyou River valleys. These areas are densely populated and affected by high intensity and continuous human activities; soil erosion and water loss issues are relatively severe in these areas (Figure 7b).

In the study area, summer rainfall is concentrated with high-intensity and long-duration events, and the frequency of extreme rainstorm events is high. The R from June to September accounted for 78.5% of the annual total R (Figure 3 and Table 3). The grain crops in the Shangluo area are mainly winter wheat and summer corn. Crop ripening and cultivation seasons (harvesting winter wheat in June and then planting summer corn), the low surface coverage of cultivated land in June and July, and disturbances to the surface soil caused by agricultural production activities have led to severe soil erosion in sloping cultivated land and low-vegetation-coverage agrarian land areas and along dirt roads. Therefore, these areas require agrarian production in periods of concentrated rainfall. Notably, active agrarian management practices are practically important for reducing the intensity and area of soil erosion and the threat of soil erosion in the study area.

4. Discussion

4.1. Effects of Rainfall Erosivity on Soil Erosion

The rainfall erosivity results of this study in the East Qinling Mountains are basically consistent with those of Yue et al. [48] and generally display a gradual decreasing trend from southeast to northwest. The fluctuating characteristics of annual rainfall erosivity at different stages in the study area (Table 2 and Figure 8) indicate that the instability of monsoon strength results in significant interannual variability in erosion trends. Owing to rapid population growth and the high frequency of human activities since the founding of the People’s Republic of China, vegetation coverage in the Qinling Mountains area has dramatically decreased and soil erosion resistance has weakened. High rainfall in the 1980s resulted in the highest levels of annual runoff and sediment transport since the Jingziguan Hydrology Station began operation. However, from 2010 to 2018, although R was the same as that in the 1980s, annual runoff and sediment transport rates remained relatively low (Figure 8), which indicates that soil and water conservation measures for ecological protection in the Qinling Mountains over the past 20 years have been effective.

At the monthly scale, rainfall erosivity in the study area is mainly concentrated in the wet season due to the influence of the summer monsoon, especially from June to September when the rainfall distribution is the most concentrated and accounts for 78.5% of the annual rainfall erosivity. Owing to this concentrated rainfall distribution, the increased frequency and intensity of extreme rainfall events (e.g., RX1d and RX5d) (Figure 5c), increased rainfall erosivity, adverse effects of agriculture (e.g., reclamation of steep slopes and planting of low-coverage crops), industrial and mining activities, infrastructure construction, and other activities that influence surface coverage and topsoil structure, soil erosion and water loss are high in many areas [49]. From September to October, extensive rainfall and high rainfall erosivity lead to greater river runoff and sediment transport. Notably, the influence of continuous rainfall events associated with autumn rains in West China is prominent.

Recently, dramatic climate fluctuations in the study area have led to increases in the frequency and intensity of extreme rainfall events and have increased the risk of soil erosion and the extent of disaster damage [6,50]. Human activities in the Qinling Mountains have severely disturbed and damaged the subsurface ecological environment. Generally, the soil structure has been destroyed, which has resulted in reduced water storage and soil preservation ability and amplified the potential impact of rainfall erosivity on soil erosion [51]. Runoff from excess infiltration occurs at the surface in the Qinling Mountain area, and shallow landslides and soil erosion are particularly serious in the river channel and gully slope areas.

4.2. Responses of River Runoff and Sediment Delivery Due to Rainfall Trends and Human Activities

Figure 8 and Figure 9 show that the annual runoff and sediment transport in the Shangluo area remained high before the 1990s. Although total R decreased in the 1970–1979 period compared with that in the 1958–1969 and 1980–1989 periods, RX1d and RX5d reached maximum values during this period (Figure 6e,f). The low annual runoff during this period resulted in increased sediment transport, which was likely related to human activities (e.g., deforestation, land clearing, mining, and road construction) and extreme rainfall events that led to high-intensity soil erosion, particularly in portions of the Qinling Mountains with limited vegetation [49]. With respect to the contributions of rainfall erosivity and sediment transport changes, previous studies did not consider rainfall patterns, but changes in rainfall patterns have a significant effect on the potential erosive capacity of the soil [38,52]. Therefore, soil protection and increased vegetation coverage, such as that of litter, should be emphasized in production processes, and the implementation of soil and water conservation measures should be strengthened with ongoing supervision [53,54]. The Qinling Mountains, where the ecological environment is relatively fragile, are the water source of the middle route of the National South-to-North Water Diversion Project; therefore, relevant studies are critical and urgently needed. Notably, it must be determined whether the national strategy of “supplying Beijing and Tianjin with clear water from a river” can be implemented in a stable and sustainable manner to benefit the population.

Although rainfall erosivity displayed an increasing trend after the 1990s, annual runoff and sediment transport decreased rapidly after the 1990s (Figure 9) (p < 0.05), which indicates that during this period, the ecological restoration activities and land-use changes related to new policies, such as the National Natural Forest Protection Project and the measures of returning farmland to forest and grassland, significantly improved the ecological environment in the Qinling Mountain area. This lead to the continuous improvement of ecosystem service functions and played a prominent role in reducing soil erosion [51,53,55]. However, the sediment transport from 2010 to 2015 increased more than that from 1990 to 1999 and 2000 to 2009, which indicates that the increase in annual rainfall erosivity led to an increase in sediment transport and suggests that extreme rainfall has a notable impact in areas of high vegetation cover in the Qinling Mountains. In particular, effects on soil erosion and soil loss from agricultural land in gully terrace and shallow mountain areas, as well as in engineering construction areas, should not be ignored (Figure 8). Although the scope and intensity of human activities have gradually increased with rapid socioeconomic development, the ecological and environmental protection measures in the Qinling Mountains have significantly attenuated the actual threat of rainfall erosivity.

4.3. Adaptation of Agricultural Activities to Climate Change

There has been an intensification in global warming and there is a need to effectively cope with an increase in the frequency of extreme rainfall events in the future. According to the seasonal distribution characteristics of rainfall erosivity in the Shangluo area and the objective reality that agriculture is mainly concentrated in the platform and shallow mountain areas in the region (Figure 3 and Figure 7), farmers in areas with relatively severe soil erosion should focus on the optimization and adjustment of farming methods and the scientific prevention and control of soil erosion. The crop planting structure in the study area involves a rotation of wheat and maize, and the R value in July reached the monthly maximum throughout the year (Figure 3). When summer maize is planted on steep slopes, severe soil erosion is likely to occur, which leads to a decrease in land productivity due to the low surface coverage at this time. Therefore, it is necessary to promptly adjust crop types, increase the planting of fallow crops (such as alfalfa and bearded grass), and improve surface coverage to reduce topsoil and nutrient loss and increase the yield of food crops [56]. The planting of low-seedling crops is recommended in summer months with high rainfall erosivity to increase surface coverage and effectively protect topsoil [16,28].

Conservation tillage and no-tillage measures should be implemented in mountainous areas to increase soil surface coverage, protect the soil structure from damage, and mitigate potential threats from soil erosion [57,58,59]. Cultivation is prohibited in areas with steep slopes exceeding 25 degrees to prevent severe soil erosion under the influence of extreme rainfall events, which can lead to increases in river runoff and sediment loads and threaten the security of the National South-to-North Water Diversion Project [36,60]. Planting crops along slopes is not advised, and cross-slope cultivation should be promoted. Soil management capabilities can be improved by building horizontal terraces and strengthening the management of ditches and terraces [61]. Attention should be given to the protection and regular repair of ridges in mountainous agricultural areas to improve land productivity by intercepting surface runoff, reducing slope erosion, increasing in situ rainfall infiltration, and mitigating the loss of soil fertility in plots [62]. In addition, attention should be given to the consolidation of ditches, slope protection and the construction of drainage facilities to improve the retention of water and soil [63,64].

5. Conclusions

Globally, understanding the spatiotemporal pattern of rainfall erosivity and its environmental impact is important in many ecologically fragile areas, as well as in the Qinling Mountains in China. Spatially, annual rainfall erosivity generally displayed a decreasing trend from southeast to northwest. The annual R exhibited a slight increasing trend over the past 62 years (p > 0.05) with a periodic downward trend before 1999 and an upward trend after 1999. RX1d and RX5d accounted for 20–40% and 60–80% of the total annual R value, respectively. We concluded that the increasing rates of rainfall erosivity for RX1d and RX5d were greater than the average level of annual rainfall erosivity, which indicated the corresponding threat of soil erosion during the wet season. However, compared with those from 1957 to 1989, the contributions of rainfall erosivity from extreme precipitation events to the reduction in sediment load in the study area decreased from 1990 to 2018. This indicates that the National Natural Forest Protection Project and the ‘Grain for Green’ Project have significantly reduced soil erosion in the Qinling Mountains. However, the risk of soil erosion is still very high in areas with significant effects from human activities, such as sloping farmlands, gully terraces, and channel platforms in shallow mountain areas. An increase in sediment transport from 2010 to 2018 confirmed this problem, which should receive increased attention from water conservancy departments.

This study helps clarify the effects of rainfall erosion and human activities on soil erosion in the Qinling Mountains at different stages in the context of a changing environment, improves the understanding of specific ecological and hydrological processes in the Qinling Mountains, and provides results to support water source planning, water resource protection and management, soil erosion control, and high-quality development of the environment in this region.