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Article

Soil Erosion Dynamics and Driving Force Identification in the Yiluo River Basin Under Multiple Future Scenarios

by
Jun Hou
1,2,
Jianwei Wang
2,*,
Xiaofeng Chen
3,
Yong Hu
3 and
Guoqiang Dong
3
1
School of Resources and Environment, Anqing Normal University, Anqing 246133, China
2
Henan Key Laboratory of Ecological Environment Protection and Restoration of Yellow River Basin, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China
3
Anhui and Huaihe River Institute of Hydraulic Research, Hefei 230088, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(14), 2157; https://doi.org/10.3390/w17142157 (registering DOI)
Submission received: 25 June 2025 / Revised: 9 July 2025 / Accepted: 16 July 2025 / Published: 20 July 2025
(This article belongs to the Special Issue Yellow River Basin Management under Pressure: Present State, Restoration and Protection III)
Browse Figures
Versions Notes

Abstract

Our study focused on identifying the evolution of soil erosion and its key drivers under multiple future scenarios in the Yiluo River Basin. Integrating the Universal Soil Loss Equation (USLE), future land use and vegetation cover simulation methods, and the Geodetector model, we analyzed historical soil erosion trends (2000–2020), projected future soil erosion risks under multiple Shared Socioeconomic Pathways (SSPs), and quantified the interactive effects of key driving factors. The results showed that soil erosion within the basin exhibited moderate intensity. Over the past 20 years, soil erosion decreased by 28.78%, with 76.29% of the area experiencing reduced erosion intensity. Future projections indicated an overall declining trend in soil erosion, showing reductions of 4.93–35.95% compared to baseline levels. However, heterogeneous patterns emerged across various scenarios, with the highest risk observed under SSP585. Land use type was identified as the core driving factor behind soil erosion (explanatory capacity q-value > 5%). Under diverse future climate scenarios, interactions between land use type and precipitation and temperature exhibited high sensitivity, highlighting the critical regulatory role of climate change in regulating erosion processes. This research provides a scientific foundation for the precise prevention and adaptive management of soil erosion in the Loess Plateau region.

1. Introduction

The Loess Plateau ranks among the regions globally experiencing the most severe soil erosion [1,2]. The Yellow River discharges approximately 1.6 billion tons of sediment annually, shaping vast downstream alluvial plains while threatening the comprehensive high-standard growth of the basin [3,4]. As a primary right-bank tributary of the middle Yellow River, the Yiluo River Basin forms a crucial ecological corridor bridging the Loess Plateau’s hilly regions and downstream plains [5,6]. However, situated in a transitional zone from semi-humid to semi-arid climates characterized by active geological structures and loose surface cover, coupled with the prolonged high-intensity exploitation of water–land resources and intense demographic pressure, the basin has become a hotspot for severe soil erosion within the Yellow River Basin [7,8]. Against escalating universal climate variation, intensifying extreme weather occurrences, and rapid regional socioeconomic transformation, projecting future soil erosion risks under multiple scenarios and identifying their key driving factors are critically imperative. These endeavors hold irreplaceable strategic significance and represent an urgent practical necessity for promoting the high-standard advancement of the Yellow River Basin [9,10,11].
Empirical–physical models, epitomized by the USLE and its derivatives, have been extensively applied for watershed/regional-scale soil erosion quantification. Their widespread adoption stems from their clear structure, accessible input parameters, and effective multisource spatial data integration [12,13,14]. Studies across typical Loess Plateau watersheds have indicated significantly declining erosion intensities in recent decades, primarily driven by integrated ecological conservation strategies, such as the Grain-for-Green Program, terracing, and check-dam systems [15,16,17]. Soil erosion emerges from complex natural–socioeconomic interactions [18]. Natural drivers (rainfall, topography, soil erodibility, vegetation cover) establish soil erosion potential [19]. Human activities, such as land use changes, farming practices, conservation engineering, and urbanization processes, accelerate or mitigate erosion by altering surface conditions and hydrology [20,21]. Methodologies have evolved from qualitative descriptions to quantitative attribution using Geodetector, Structural Equation Modeling, and machine learning [22,23]. Such investigations have revealed regional divergent dominant drivers and complex nonlinear interactions exhibiting enhancement/suppression effects [24]. For instance, the improvement of vegetation coverage on the Loess Plateau is identified as the most crucial inhibitory factor for weakened soil erosion, while short-duration heavy rainfall events and the existence of sloping farmland constitute key risk factors [25,26].
Integrating future climate scenarios data with future land use/vegetation prediction methods constitutes a critical approach for exploring future soil erosion risks [27,28]. Such studies typically employ future climate models, land use/vegetation evolution simulations, and physically based erosion models to conduct multiscenario, multitemporal projections of soil erosion risks [29,30]. These projections are essential for anticipating priorities, challenges, and adaptive strategies under various development pathways [31,32]. However, fundamental questions persist regarding how the key drivers of soil erosion and their underlying mechanisms will evolve under future climate scenarios. These critical knowledge gaps remain underexplored in current research, representing scientific bottlenecks for achieving precise soil erosion prevention and adaptive watershed management.
Our study systematically integrates long-term remote sensing imagery, high-resolution geospatial data, and future climate scenarios datasets. Utilizing Geographic Information System (GIS) technology and numerical simulation methods, we establish a comprehensive research framework to analyze soil erosion dynamics and identify driving forces in the Yiluo River Basin under multiple future scenarios. The research comprises the following: (1) the spatiotemporal dynamics of soil erosion intensity; (2) future soil erosion projections; and (3) the identification of soil erosion driving factors. Our objectives are to reveal the soil erosion evolution patterns and future risks in the basin and quantitatively identify the key driving factors along with their interaction mechanisms.

2. Data and Methods

2.1. Study Area

The Yiluo River, a primary right-bank tributary of the middle Yellow River, originates from the confluence of the Yi River and Luo River near Yanshi City, Henan Province. It ultimately flows into the Yellow River at Gongyi City, draining a total basin area of approximately 18,800 km2 (Figure 1). The upper reaches are characterized by mid-low mountains with steep slopes, serving as the primary water conservation zone. The middle reaches transition into loess hills and intermountain basins with gradually gentler terrain. The lower reaches enter the flat and open Huang-Huai-Hai Plain. Situated in a climatic transition zone from semi-humid to semi-arid, the basin experiences a yearly mean temperature of 12.6 °C and a yearly mean precipitation of 680 mm. Rainfall is predominantly concentrated during the flood-prone season, making the area susceptible to flooding disasters. The watershed encompasses six prefecture-level cities, including Zhengzhou, Luoyang, Sanmenxia, Shangluo, Xi’an, and Weinan. Among these, Luoyang City occupies the largest area, accounting for approximately 59.45% of the total basin. Cultivated land is the predominant land use category, covering approximately 46% of the total basin area, making it an important area for grain and economic crop production. With its distinctive location, complex topography, significant water resource endowments, and profound cultural heritage, the Yiluo River Basin represents a strategically crucial area for implementing ecological conservation and high-quality development initiatives within the Yellow River Basin.

2.2. Data

The data utilized in this study comprise meteorological, topographic, land use, soil, and vegetation data, and a future climate model dataset, with detailed information presented in Table 1.

2.3. Methods

2.3.1. Soil Erosion Model

Soil erosion was calculated via the Universal Soil Loss Equation (USLE), expressed as
A = R × K × L S × C × P
where A denotes the soil loss amount ( t / ( hm 2 · a ) ); R refers to precipitation erosivity ( MJ · mm / ( hm 2 · h · a ) ); K indicates soil erodibility ( t · h / ( MJ · mm ) ); LS represents the topographic; and C and P are the dimensionless vegetation management and conservation practice, respectively, each ranging from 0 to 1. All factors were spatially resampled to a uniform resolution of 500 × 500 m. The computational methods for each factor are detailed below:
(1)
Rainfall Erosivity (R)
The R-factor was computed utilizing monthly and yearly precipitation data. It quantifies the erosive potential of rainfall, where higher R values indicate a greater capacity to induce soil erosion. The calculation formula is
R = ∑ i = 1 12 1.735 × 10 1.5 × l g P i 2 P − 0.8088
where i represents the month index; P i indicates monthly precipitation (mm); and P denotes yearly precipitation (mm).
(2)
Soil Erodibility (K)
The K-factor was calculated using the Erosion Productivity Impact Calculator (EPIC) model [33]. It quantifies a specific soil type’s susceptibility to rainfall erosion, where higher K values indicate greater erodibility. The calculation formula is
K = 0.2 + 0.3 e − 0.0256 S a 1 − S i 100 × S i C l + S i 0.3 × 1 − 0.25 C C + e 3.72 − 2.95 C × 1 − 0.7 S n S n + e − 5.51 + 22.9 S n
S n = 1 − S a 100
where S a denotes the sand content (0.1–2.0 mm); S i indicates the silt content (0.002–0.1 mm); C l represents the clay content (<0.002 mm); and C refers to the organic carbon content (%).
(3)
Slope Length and Steepness (LS)
The LS-factor quantifies the integrated effect of topography on soil erosion [34]. The calculation formulas are
S = 10.80 × s i n θ + 0.03 ,   θ < 5 ° 16.80 × s i n θ − 0.50 ,   5 ° ≤ θ < 10 ° 21.91 × s i n θ − 0.96 ,   θ ≥ 10 °
L = λ 22.13 m
λ = l × c o s θ
where θ indicates the slope angle (°); λ represents the horizontal slope length (m); l refers to the flow path length (m); and m is the variable slope exponent: If θ < 1 ° ,   m = 0.2 ; if 1 ° ≤ θ < 3 ° ,   m = 0.3 ; if 3 ° ≤ θ < 5 ° ,   m = 0.4 ; and if θ ≥ 5 ° ,   m = 0.5 .
(4)
Vegetation Management (C)
The C-factor reflects the protective effect of crops or vegetation on soil, where higher C values indicate greater effectiveness in preventing soil erosion [35]. The calculation formulas are
f c = E V I − E V I m i n E V I m a x − E V I m i n
C = 1 ,   f c = 0 0.6508 − 0.3436 l g c ,   0 < f c < 78.3 % 0 ,   f c ≥ 78.3 %
where f c is fractional vegetation cover (%); and EVI represents the Enhanced Vegetation Index.
(5)
Conservation Practice (P)
The P-factor represents the effectiveness of specific engineering measures for soil and water conservation. A lower p value signifies the better performance of conservation practices. Drawing on previous studies [36,37], the P-factor was assigned a value of 0.31 for farmland, 0.16 for grassland, and 0.05 for forestland. For water bodies, construction land, and bare land, where conservation measures are generally not implemented, the values are set at 1.
(6)
Soil Erosion Intensity Classification
The classification of soil erosion intensity followed China’s national standard, Standard for Classification and Grading of Soil Erosion (SL 190-2007), promulgated by the Ministry of Water Resources [38], with detailed information presented in Table 2.

2.3.2. Future Projection Methodology

Future projections took 2020 as the baseline year, with short-term planning to 2030 and the long-term outlook to 2050. In line with the future climate dataset, land use projections employed the Future Land Use Simulation (FLUS) model. The Enhanced Vegetation Index (EVI) was simulated via bivariate linear regression. All simulations results underwent rigorous calibration and validation, yielding satisfactory outcomes confirming their suitability for subsequent analysis. Future soil erosion was quantified by jointly inputting climate scenarios, projected land use, and vegetation cover data into the USLE model.
(1)
Future climate data
Future climate data incorporate four emission scenarios: SSP126 (Sustainable Development), SSP245 (Moderate Development), SSP370 (Regional Development), and SSP585 (Conventional Development). The future climate model data underwent downscaling using bilinear interpolation, with bias correction via the linear scaling method [39]. The calculation formulas are
T e m f = C M I P f + C M I P h − C M A h
P r e f = C M A h C M I P h × C M I P f
where T e m f and P r e f denote bias-corrected future temperature and precipitation data, respectively; C M I P f and C M I P h represent future and historical climate model data from CMIP6, respectively; and C M A h indicates historical observational climate data from the China Meteorological Administration Data Center.
(2)
Future Land Use Simulation
Future land use was projected by the FLUS model [40,41]. Accessible driving factors were selected, including topographic factors (DEM, slope, aspect), socioeconomic factors (population, GDP), and meteorological factors (precipitation, temperature). All data were unified to a spatial resolution of 500 × 500 m. The simulation workflow is illustrated in Figure 2.
(3)
Future EVI Simulation
The EVI was simulated using a bivariate linear regression model [42], with the EVI as the dependent variable and concurrent precipitation and temperature as independent variables. The calculation formula is
E V I f = a · P R E + b · T E M + c
where E V I f refers to the projected EVI value, P R E and T E M represent the annual mean precipitation and temperature, a and b are the regression coefficients, and c is a constant term.

2.3.3. Geodetector Model

The Geodetector method was applied to identify the key drivers of soil erosion, utilizing its single-factor and interaction detection modules [43,44]. The single-factor detection module quantified the explanatory capacity of a given driver X on the spatial heterogeneity of variable Y. The interaction detector evaluated how paired driving factors jointly influence variable Y, specifically evaluating whether the combined effect of driver 1 and driver 2 will enhance or diminish their explanatory power for Y, or whether they operate independently. A larger q-statistic signifies the greater explanatory capacity of variable X in relation to Y, while a smaller value suggests a weaker influence.
The following driving factors were chosen to analyze their impact on soil erosion: land use type (X1), vegetation coverage (X2), temperature (X3), precipitation concentration (X4), annual precipitation (X5), heavy rainfall days (X6), elevation (X7), slope (X8), and soil depth (X9). The data processing of driving factors is detailed in Table 3.

3. Results

3.1. Spatiotemporal Dynamics of Soil Erosion Intensity

Based on the USLE, soil erosion in the Yiluo River Basin was modeled from 2000 to 2020. The simulation outcomes were consistent with the soil erosion intensity reported in the Yellow River Sediment Bulletin, indicating that the model performed well in simulating soil erosion and can be used for subsequent calculations. The multiyear average erosion modulus was 37.49 t / ( hm 2 · a ) , classified as moderate erosion intensity. Areas of relatively strong erosion intensity were largely concentrated in the middle and upper reaches, while the lower reaches experienced slight- and light-level erosion (Figure 3a–c). Regarding the various land use categories, cultivated land exhibited the highest soil erosion modulus, reaching 64.77 t / ( hm 2 · a ) , followed by grassland (42.38 t / ( hm 2 · a ) ) and forestland (27.57 t / ( hm 2 · a ) ) (Figure 3d). Water bodies and construction land showed negligible erosion. Over the past two decades, the soil erosion modulus showed a fluctuating decreasing trend, with a reduction of 28.78%, of which forestland and grassland experienced reductions of 43.8% and 18.8%, respectively (Figure 4). Specifically, the areas with slight- and light-level erosion continuously increased by 3.75% and 13.14%, respectively, while moderate, - strong-, and severe-level erosion zones continuously decreased by 5.75%, 6.20%, and 3.03%, respectively, with violent-level soil erosion showing a fluctuating decrease of 1.91%.
Spatially, changes in soil erosion in the Yiluo River Basin exhibited pronounced heterogeneity (Figure 5a–c). From 2000 to 2020, 76.29% of the area showed declining trends, while 23.71% exhibited increasing trends. The regions with increasing soil erosion were largely concentrated in the lower reaches of the basin. Specifically, 51.82% of the area experienced a decreasing trend, and 48.18% showed an increasing trend in the first decade (2000–2010), while the proportion with a decreasing trend accounted for 76.52%, and the area exhibited an upward trend comprising 23.44% in the latter decade (2010–2020). We further quantified the land-use-specific spatial distributions of soil erosion trends (Figure 5d). From 2000 to 2020, declining erosion occurred across 65.74% of cultivated land, 94.66% of forestland, and 68.92% of grassland. Specifically, decreasing trends covered 37.11% of cultivated land, 72.41% of forestland, and 31.39% of grassland in the first decade, while these proportions rose to 73.01% (cultivated land), 79.47% (forestland), and 63.31% (grassland) in the latter decade.

3.2. Future Projections of Soil Erosion

Based on the bias correction future climate model data and the simulated future land use and vegetation cover data, the USLE model was applied to simulate the spatial patterns of future soil erosion in the Yiluo River Basin for the years 2030 (Figure 6a–d) and 2050 (Figure 7a–d) under the SSP126, SSP245, SSP370, and SSP585 scenarios. The results indicated that in the short term (2030), the soil erosion modulus was the highest under the SSP370 scenario and the lowest under the SSP126 scenario, ranging from 18.8 to 26.44 t/(hm2·a) across different SSPs (Figure 8). Compared to the baseline year, the soil erosion modulus decreased by 9.94% to 35.95%. In the long term (2050), the soil erosion modulus was the highest under the SSP585 scenario and the lowest under the SSP370 scenario, ranging from 21.85 to 27.91 t/(hm2·a) across different SSPs (Figure 8). Compared to the baseline year, the soil erosion modulus decreased by 4.93% to 25.58%. In terms of different SSP scenarios, the soil erosion modulus showed an increasing trend under the SSP126 and SSP585 scenarios, with increases of 20.97% and 21.28% in the long term versus the short term, respectively, while the soil erosion modulus exhibited a decreasing trend under the SSP245 and SSP370 scenarios, with decreases of 1.27% and 17.36% in the long term versus the short term, respectively.

3.3. Driving Factor Identification of Soil Erosion

Utilizing the Geodetector model, we analyzed the influences of various factors on soil erosion in the Yiluo River Basin. The single-factor detection findings (Figure 9) revealed that the q-values of various driving factors on soil erosion ranged from 0.45 to 5.31% (2020), 0.19 to 5.57% (2030), and 0.15 to 6.13% (2050). Among these factors, the land use type dominated with q-values exceeding 5%, followed by vegetation coverage, soil depth, elevation, temperature, and PCI, with q-values ranging from 1 to 4%, while other factors contributed minimally, with q-values below 1%. Notably, PCI and temperature, as major driving factors, exhibited significant fluctuations across different periods. Specifically, in the short term (2030), the q-values for PCI were minimized under SSP126 and maximized under SSP370, while the q-values of temperature peaked under SSP126 and troughed under SSP370. Compared to the baseline, the q-values for PCI ranged from −59.86% to 15.92%, and for temperature, from 74.76% to 115.67%. In the long term (2050), the q-values for both PCI and temperature were the lowest under the SSP585 scenario and highest under the SSP126 scenario, PCI change relative to the baseline ranged from −61.77% to −1.79%, while temperature changes ranged from −0.05% to 44.2%.
The interaction detection results (Figure 10) indicated that pairwise interactions among the driving factors significantly enhanced their effects on soil erosion, far exceeding the effects of individual factors. Specifically, the interaction between land use type and elevation exerted the strongest effect, followed by the interactions of land use type and PCI, soil depth, and temperature, all presenting a nonlinear enhancement. The q-values for these interactions in the baseline year were quantified at 26.81%, 24.29%, 23.62%, and 18.06%, respectively (Figure 10a). Compared to the baseline year, the interactions involving land use type with elevation and soil depth showed relatively minor changes in both the short term (2030) and long term (2050). Conversely, the interactions of land use type and PCI and temperature exhibited more noticeable fluctuations. In the short term (2030), the land use type and PCI interaction was smallest under the SSP126 scenario and largest under the SSP245 scenario. The land use type and temperature interaction was largest under the SSP126 scenario and SSP370 scenario (Figure 10b–e). Compared to the baseline year, these interactions changed by −31.43% to 0.72% and 44.32% to 51.07%, respectively. In the long term (2050), the land use type and PCI interaction was smallest under the SSP370 scenario and largest under the SSP245 scenario. The land use type and temperature interaction was largest under the SSP245 scenario and smallest under the SSP585 scenario (Figure 10f–i). Compared to the baseline year, these interactions changed by −23.91% to 9.18% and 46.01% to 59.15%, respectively.

4. Discussion

4.1. Influence of Climatic Factors on Soil Erosion

Studies have shown that precipitation and temperature were the primary driving forces behind the dynamic changes in soil erosion in the Yiluo River Basin, consistent with existing studies [45,46,47]. Climate factors profoundly govern erosion dynamics through their direct provision of erosive energy (precipitation) and indirect modification of the erosion environment (temperature). Statistical analyses have revealed that the multiyear average precipitation was 680.88 mm (Figure 11a). The precipitation within the basin exhibited a trend of “slight decrease in total amount but increased extremity”, with an increasing frequency of short-duration, high-intensity rainstorm events. Such rainfall events possess a far greater capacity to detach and transport surface soil compared to general precipitation, easily triggering severe gully and slope erosion [48,49]. Compared to the baseline period, the annual average precipitation increased by 9.11% to 11.62%. Altered rainfall patterns under climate change may further amplify soil erosion risks. Additionally, the annual average temperature under different SSP scenarios increased by 4.36% to 7.74% compared to the baseline (Figure 11b). Although temperature is not directly incorporated in USLE calculations, it indirectly modulates erosion by influencing vegetation phenology and growth (affecting the C-factor). The impact of temperature presents dual effects: extended growing seasons and enhanced vegetation NPP may mitigate rainfall erosivity through improved ground cover, partially offsetting the negative impacts of extreme rainfall. Conversely, in semi-arid regions, the warming–drying trend exacerbates soil moisture deficits, suppresses vegetation recovery, and may even cause degradation, ultimately weakening surface erosion resistance [50,51]. Notably, climate factors exhibited strong spatial heterogeneity in erosion impacts, primarily resulting from the synergistic amplification between inherent climatic spatial variability and complex interactions with topographic features and land use/cover patterns.

4.2. Impacts of Land Use Factors on Soil Erosion

Land use and vegetation cover changes profoundly reshape surface coverage (affecting the C-factor) and conservation practices (influencing the P-factor), constituting the primary anthropogenic pathway for erosion regulation [51]. Our research identified that land use type exhibited the strongest explanatory power for soil erosion in the Yiluo River Basin, with q-values greater than 5% across the baseline year, the short term (2030), and the long term (2050). Furthermore, the interaction between land use and other factors enhanced its influence on soil erosion [52]. Land use change analyses indicated a 1.28% expansion in forested areas over the past two decades (Figure 12). This increase was attributable to post-2000 ecological restoration initiatives, particularly the Grain-for-Green Program and protected area conservation measures. Coupled with the heightened public awareness of forest protection and reduced deforestation [53,54], forested areas have increased by 1.28%. Increased forest and grassland coverage has reduced soil erosion by 43.8% and 18.8%, respectively. Future multiscenario simulations further confirmed the consistent patterns. Cultivated land area is projected to decrease by 3.39% to 8.92% versus the baseline, while forestland is expected to increase by 0.69% to 1.48%. Consequently, soil erosion is projected to decrease by 9.94% to 35.95% (2030) and by 4.93% to 25.58% (2050) versus the current year. Notably, the spatial heterogeneity in vegetation coverage (C-factor) directly governs erosion risk patterns [55,56]. Upper basin regions exhibited favorable natural conditions with stable forest cover (low C-values) and minimal erosion. Reforested areas predominantly cluster in the central-northern loess hilly region, spatially coinciding with significant erosion reduction zones. Critically, built-up land has expanded by 85.53% over 20 years and is projected to increase by 16.7–43.42% in future scenarios. Urbanization encroaches on prime cropland and forests, while surface impermeabilization concentrates runoff, potentially exacerbating downstream erosion risks [57,58].

4.3. Impacts of Topography and Soil Depth Factors on Soil Erosion

Topography (LS-factor) and soil properties (K-factor) constitute the fundamental environmental background for erosion dynamics. Topography fundamentally controls the spatial pattern of erosion dynamics and the intensity of erosion processes by influencing precipitation redistribution, runoff convergence pathways, flow scouring capacity, and gravitational potential energy [52,59]. Simultaneously, topography exerts a magnifying effect on precipitation erosion [9,22]. In the upper reaches of the watershed, steep terrain significantly accelerated surface runoff convergence, enhanced its scouring capacity, and increased erosion risk. In the downstream plains, despite minimal changes in precipitation, flat topography (extremely low LS-factor values) and intensive agricultural management substantially reduced the actual erosive power of precipitation. The widespread loess-derived cinnamon soils in the watershed, characterized by loose structure, low clay content, and high silt–sand ratio, are highly susceptible to dispersion and transport by runoff [26,60]. Particularly in the Loess Plateau regions of the middle Luo River, the deep loess layers (reaching tens or even hundreds of meters) provide an exceptionally abundant material source for erosion. Moreover, land use in this area is predominantly cropland, where tillage loosens the soil, further exacerbating topsoil loss, especially on sloping farmland.

4.4. Limitations and Uncertainties

Our study implemented the USLE model to calculate soil erosion. Daily precipitation data were used to determine the rainfall erosivity factor (R), the EPIC method to compute the soil erodibility factor (K), the EVI to estimate the cover management factor (C), high-resolution DEM to generate the slope length and steepness factor (LS), and high-precision land use data to indirectly estimate the support practice factor (P). Nevertheless, non-negligible uncertainties persist, primarily stemming from model structure, parameter acquisition, and data limitations. A key limitation regarding model validation, the lack of monitoring data generally necessitates comparisons with regional empirical data or known studies and cross-validation between different model simulations. For projecting future erosion responses under climate scenarios, we used the FLUS model to predict future land use based on future climate model data and a multiple linear regression model to simulate future EVIs. These projections are highly dependent on the reliability of the selected climate models and the accuracy of downscaling methods, resulting in considerable uncertainty. In future research, field observation experiments should be integrated to strengthen in situ measurements of key parameters (K, C, P) and improve model parameterization and simulation validation.

5. Conclusions

Based on the USLE model, our study systematically revealed the spatiotemporal evolution of soil erosion in the Yiluo River Basin from 2000 to 2020. By coupling the USLE model with future land use and vegetation cover simulation models, we projected future erosion risks under multiple scenarios from 2030 to 2050. Additionally, the Geodetector model was employed to quantitatively identify the key driving factors of soil erosion under different future scenarios. Our work addressed the research gaps regarding how the key driving factors and mechanisms of soil erosion in the Loess Plateau region would evolve under future climate scenarios. The key conclusions are as follows:
(1)
Soil erosion showed a decreasing trend from 2000 to 2020 in the study area. The multiyear average erosion modulus was 37.49 t / ( hm 2 · a ) (moderate intensity). Total erosion decreased by 28.78% over 20 years, with 76.29% of the area showing reduced intensity, primarily attributed to the Grain-for-Green Program and protected area conservation.
(2)
Future soil erosion showed a decreasing trend but scenario-dependent heterogeneity. It will decrease by 9.94–35.95% (2030) and 4.93–25.58% (2050) versus the baseline. Erosion will increase under SSP126 and SSP585 but decrease under SSP245 and SSP370, highlighting the heterogeneity of soil erosion under different development pathways.
(3)
Factor interactions amplify their impacts on soil erosion. Land use type dominated as the core driver (q > 5%). Under different future climate scenarios, interactions between land use and PCI/temperature exhibited heightened sensitivity and significant fluctuations, emphasizing the critical regulatory role of future climate change on soil erosion.
(4)
Limitations and prospects. While this study coupled the USLE model with future land use and vegetation cover simulation models to effectively reveal the evolution characteristics and key driving factors of soil erosion under different scenarios, uncertainties in parameters and future data remain. Future research will integrate field observations to strengthen the in situ measurements of key parameters (K, C, P), optimize parameterization, and enhance the reliability of model simulation validation.

Author Contributions

Conceptualization, J.H. and J.W.; writing—original draft preparation, J.H.; writing—review and editing, J.H. and X.C.; supervision, Y.H. and G.D.; funding acquisition, J.H. and X.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Open Research Fund of Henan Key Laboratory of Ecological Environment Protection and Restoration of Yellow River Basin (LYBEPR202401), the National Science Fund for Young Scholars (52409002), the Anhui Provincial Natural Science Foundation (2208085US16), and the Key Project of Natural Science Research of Anhui Provincial Education Department (2024AH051101).

Data Availability Statement

Any additional information required to reanalyze the data reported in this paper is available from J.H.

Conflicts of Interest

The authors declare no competing interests.

Abbreviations

The following abbreviations are used in this manuscript:
SSPsShared Socioeconomic Pathways
USLEUniversal Soil Loss Equation
EVIEnhanced Vegetation Index
PCIPrecipitation Concentration index
FLUSFuture Land Use Simulation

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Figure 1. The geographical location, topography, and major cities of the Yiluo River Basin.
Figure 1. The geographical location, topography, and major cities of the Yiluo River Basin.
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Figure 2. Workflow of the FLUS model.
Figure 2. Workflow of the FLUS model.
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Figure 3. Spatiotemporal Pattern of Soil Erosion Intensity in the Yiluo River Basin from 2000 to 2020. (d) Soil Erosion in different land use types from 2000-2020. The terms “slight, light, moderate, strong, severe, violent” in the legend represent soil erosion intensities.
Figure 3. Spatiotemporal Pattern of Soil Erosion Intensity in the Yiluo River Basin from 2000 to 2020. (d) Soil Erosion in different land use types from 2000-2020. The terms “slight, light, moderate, strong, severe, violent” in the legend represent soil erosion intensities.
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Figure 4. Percentage Composition of Soil Erosion Intensity in the Yiluo River Basin from 2000 to 2020.
Figure 4. Percentage Composition of Soil Erosion Intensity in the Yiluo River Basin from 2000 to 2020.
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Figure 5. Spatiotemporal Changes in the Soil Erosion Modulus of the Yiluo River Basin from 2000 to 2020. (d) Changes of the Soil Erosion for different land use types from 2000 to 2010. The terms “decrease, stable, increase” in the legend represent the change trend of soil erosion intensity.
Figure 5. Spatiotemporal Changes in the Soil Erosion Modulus of the Yiluo River Basin from 2000 to 2020. (d) Changes of the Soil Erosion for different land use types from 2000 to 2010. The terms “decrease, stable, increase” in the legend represent the change trend of soil erosion intensity.
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Figure 6. Soil Erosion Intensity Under Different SSP Scenarios in 2030. The terms “slight, light, moderate, strong, severe, violent” in the legend represent soil erosion intensity.
Figure 6. Soil Erosion Intensity Under Different SSP Scenarios in 2030. The terms “slight, light, moderate, strong, severe, violent” in the legend represent soil erosion intensity.
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Figure 7. Soil Erosion Intensity Under Different SSP Scenarios in 2050. The terms “slight, light, moderate, strong, severe, violent” in the legend represent soil erosion intensity.
Figure 7. Soil Erosion Intensity Under Different SSP Scenarios in 2050. The terms “slight, light, moderate, strong, severe, violent” in the legend represent soil erosion intensity.
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Figure 8. Soil erosion modulus in the Yiluo River Basin from 2020 to 2050.
Figure 8. Soil erosion modulus in the Yiluo River Basin from 2020 to 2050.
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Figure 9. The q-values of each driving factor.
Figure 9. The q-values of each driving factor.
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Figure 10. Interactive detection values (%) among different driving factors.
Figure 10. Interactive detection values (%) among different driving factors.
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Figure 11. Interannual variations in precipitation (a) and temperature (b) of the Yiluo River Basin from 1991 to 2050. The shaded area is the 95% confidence interval.
Figure 11. Interannual variations in precipitation (a) and temperature (b) of the Yiluo River Basin from 1991 to 2050. The shaded area is the 95% confidence interval.
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Figure 12. Land use changes in the Yiluo River Basin from 2020 to 2050.
Figure 12. Land use changes in the Yiluo River Basin from 2020 to 2050.
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Table 1. The data types, years, sources, and descriptions used in the study.
Table 1. The data types, years, sources, and descriptions used in the study.
TypesYearsSourcesDescriptions
Precipitation1961–2020http://data.cma.cn/ (accessed on 1 June 2025)Daily data
Temperature
DEM2000http://www.gscloud.cn/ (accessed on 1 June 2025)90 m resolution
Land use2000, 2010, 2020http://www.ncdc.ac.cn (accessed on 1 1 June 2025)30 m resolution
EVI2001–2020https://lpdaac.usgs.gov/ (accessed on 1 June 2025)250 m resolution
Soil depth2018http://globalchange.bnu.edu.cn/research/cdtb.jsp (accessed on 1 June 2025)100 m resolution
Soil type2009http://www.geodata.cn/ (accessed on 1 June 2025)1000 m resolution
Soil erosion2001–2020Sediment Bulletin of the Yellow River-
Future precipitation2021–2050https://aims2.llnl.gov/search/cmip6/ (accessed on 1 June 2025)1000 m resolution
Future temperature
Table 2. Classification of Soil Erosion Intensity.
Table 2. Classification of Soil Erosion Intensity.
LevelSlightLightModerateStrongSevereViolent
Soil erosion modulus
t/(hm2·a)		<55~2525~5050~8080~150>150
Table 3. Data Processing of Driving Factors.
Table 3. Data Processing of Driving Factors.
Driving FactorsCode NameMethod/Data SourceDescriptions
Land useX1See Table 1Land use categories
Vegetation coverageX2Mean value methodAnnual mean vegetation coverage
TemperatureX3Mean value methodAnnual mean temperature
Precipitation concentrationX4Precipitation concentration index (PCI)Intra-annual precipitation distribution
Annual precipitationX5Mean value methodAnnual precipitation
Heavy rainfall daysX6Daily precipitation > 25 mm Annual heavy rainfall days
ElevationX7See Table 1Topographic elevation
SlopeX8Derived from DEMTerrain steepness
Soil depthX9See Table 1Soil layer thickness
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Hou, J.; Wang, J.; Chen, X.; Hu, Y.; Dong, G. Soil Erosion Dynamics and Driving Force Identification in the Yiluo River Basin Under Multiple Future Scenarios. Water 2025, 17, 2157. https://doi.org/10.3390/w17142157

AMA Style

Hou J, Wang J, Chen X, Hu Y, Dong G. Soil Erosion Dynamics and Driving Force Identification in the Yiluo River Basin Under Multiple Future Scenarios. Water. 2025; 17(14):2157. https://doi.org/10.3390/w17142157

Chicago/Turabian Style

Hou, Jun, Jianwei Wang, Xiaofeng Chen, Yong Hu, and Guoqiang Dong. 2025. "Soil Erosion Dynamics and Driving Force Identification in the Yiluo River Basin Under Multiple Future Scenarios" Water 17, no. 14: 2157. https://doi.org/10.3390/w17142157

APA Style

Hou, J., Wang, J., Chen, X., Hu, Y., & Dong, G. (2025). Soil Erosion Dynamics and Driving Force Identification in the Yiluo River Basin Under Multiple Future Scenarios. Water, 17(14), 2157. https://doi.org/10.3390/w17142157

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