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Article

Multi-Scale Effect of Land Use Landscape on Basin Streamflow Impacts in Loess Hilly and Gully Region of Loess Plateau: Insights from the Sanchuan River Basin, China

by
Zexin Lei
1,2,*,
Shifang Zhang
3,
Wenzheng Zhang
4,
Xuqiang Zhao
2 and
Jing Gao
1
1
College of Architecture, Taiyuan University of Technology, No. 79 West Street Yingze, Taiyuan 030024, China
2
Shanxi Academy of Social Sciences, No. 14, South Road Dachang, Taiyuan 030032, China
3
College of Traffic Engineering, Shanxi Vocational University of Engineering Science and Technology, Taiyuan 030000, China
4
School of Architecture, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(23), 10781; https://doi.org/10.3390/su162310781
Submission received: 13 October 2024 / Revised: 30 November 2024 / Accepted: 5 December 2024 / Published: 9 December 2024
Browse Figures
Figure 1
<p>Location of the SRB. (<b>a</b>) The SRB in the Loess Plateau of northwest China, (<b>b</b>) the digital elevation model of the SRB, (<b>c</b>) rivers and major hydrologic stations of the SRB, and (<b>d</b>) soil classification of the SRB. Readers can refer to the official database (<a href="http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/" target="_blank">http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/</a>, accessed on 12 October 2024) for a complete list of soil classes and their definitions.</p> "> Figure 2
<p>Setting scenarios at different LUCCs in upper, middle, and lower basins. (<b>a</b>) Upstream, midstream, and downstream zoning of SRB, and (<b>b</b>) 27 scenarios set up for LUCC in the upstream, midstream, and downstream based on 1980 land use/cover.</p> "> Figure 3
<p>Land use/cover change from 1980 to 2020: (<b>a</b>) land use/cover of the SRB in 1980 and 2020, and (<b>b</b>) Sankey diagram of LUCC from 1980 to 2020.</p> "> Figure 4
<p>LUCC at different buffer scales along the stream. (<b>a</b>) Integrated LUCC dynamics at different buffer scales. (<b>b</b>) Spatial transformation of land use/cover at different buffer scales.</p> "> Figure 5
<p>The comparison data of monthly streamflow volume from 1975 to 1984, between the measured value of the Houdacheng Hydrological Station and the simulated value of the SWAT model.</p> "> Figure 6
<p>Model simulation results of multi-year average streamflow in the SRB under 27 scenarios of different LUCCs in the upper, middle, and lower basins.</p> "> Figure 7
<p>Model simulation results under scenarios of LUCC with different buffer widths. (<b>a</b>) Buffer zones of buffer widths from 150 to 1500 m. (<b>b</b>) Model simulation results of multi-year average streamflow in the SRB under 40 scenarios of LUCC with different buffer widths.</p> "> Figure A1
<p>Scenarios of different land uses in the buffer zone along the river based on 1980 land use.</p> ">
Versions Notes

Abstract

:
The gullies and valleys of the Loess Plateau, as key ecological zones for soil erosion control, play a critical role in the region’s sustainable development under increasing urbanization. This study employed the Soil and Water Assessment Tool (SWAT) to analyze the impacts of land use/cover changes (LUCC) on runoff at multiple spatial scales and locations within the Sanchuan River Basin (SRB) in the loess hilly and gully region. The methodology integrates SWAT modeling with LUCC scenario analysis, focusing on spatial and scale effects of land use changes on hydrological processes. The results revealed distinct spatial differences, with diminishing LUCC impacts on streamflow from the upper to lower reaches of the basin, regardless of land use type. Scale effects were also evident: grassland effectively controlled runoff within 300 m of riparian zones, while forest land was most effective beyond 750 m. A relatively insensitive range for runoff changes was observed between 300 and 750 m. These findings highlight the critical role of LUCC in influencing runoff patterns and underscore the importance of region-specific and scale-sensitive land use management strategies. This research provides valuable guidance for sustainable land planning, particularly in riparian zones, to enhance runoff control and optimize ecological benefits.

1. Introduction

The Loess Plateau, known for its severe soil erosion, plays a critical role in China’s ecological balance [1,2,3]. The effects of climate change and LUCC on the streamflow in this region have garnered considerable attention [4,5]. In particular, human-induced LUCC, including activities such as agriculture, industrialization, urbanization, ecosystem protection, and reservoir construction, substantially influences the regional hydrological system in the Loess Plateau region [6,7]. LUCC directly alters the surface’s physical characteristics, impacting water–surface interactions crucial for streamflow generation [8]. Measures such as afforestation, terracing, and silt dams aim to reduce soil erosion by decreasing surface streamflow [9]. At a landscape scale, these measures are understood to influence the processes of water yield and runoff absorption (sink) by altering land use and cover types [10,11,12]. Research has demonstrated their noteworthy impact on streamflow processes in the Loess Plateau [13,14]. Considering not only the individual characteristics but also the collective spatial distribution and connectivity of land use/cover patches is crucial. The arrangement of different patches affects water flow paths and connectivity [15,16]. For instance, the afforestation of the Loess Plateau has altered the original connectivity of the gullies, significantly reducing sediment transport [10,12,17]. Conversely, the expansion of water infrastructure patches exacerbates this process [18] to collect more water for use while settling sediment. There is still inconclusive evidence regarding whether LUCC on the Loess Plateau has increased or decreased streamflow [19,20]. However, numerous studies suggest that LUCC has a more significant effect on streamflow and hydrological processes than climate change [21,22]. Investigating how changes in the spatial distribution and scale of land use patterns affect streamflow provides a foundational framework for planning basin construction and development while ensuring ecological protection.
The loess hilly and gully region is recognized as the most ecologically fragile area on the Loess Plateau, particularly due to its severe soil erosion, highly dissected landscape, and vulnerable hydrological processes [1,23,24]. Characterized by deep gullies and river valleys, this region serves as a crucial link connecting upstream and downstream corridors throughout the basin [23,24]. In this context, a gully refers to a deep, narrow channel formed by water erosion, with typical depths ranging from 5 to 50 m and lengths extending from 500 m to several kilometers. Gullies in the upper reaches are often narrower (5–30 m wide), while those in the middle and lower reaches can broaden to over 100 m, transitioning into wider valleys that exceed 1–2 km in width. These gullies and river valleys together form a highly fragmented landscape, profoundly shaped by geomorphic processes and human activities. Effective management of streamflow, sediment transport, and overall basin hydrology is critical in this context, as this region serves as the epicenter of soil erosion on the Loess Plateau. For nearly half a century, the Chinese government, at both national and regional levels, has actively pursued soil and water conservation efforts in the loess hilly and gully region [1,25], spearheaded by initiatives such as the Grain for Green Project and the construction of water infrastructures in gullies or on gully slopes [26,27], including the well-known check dam system [28,29]. While efforts in soil and water conservation have been significant, urbanization has also predominantly concentrated in gullies and river valleys within the fragmented landscape [30,31,32,33], which aligns closely with the major distribution of ecological damages in this area [34,35]. LUCC induced by urbanization has significantly impacted biodiversity conservation, regional production trends, and patterns [36], while also exacerbating the already severe problem of soil erosion [37,38].
Rapid LUCC in gullies and valleys intensifies the competition between urban development and ecological protection [30,31,32,33]. This spatial conflict primarily arises along geomorphologically formed gullies or river valleys. Despite this, there is currently a lack of research on the hydrological impacts of LUCC occurring in these specific spaces. However, these areas, serving as corridors in the landscape pattern, are similar to riparian zones. Riparian zones are considered critical elements for influencing water processes [39]. Typically, a riparian zone is defined as a certain width of the bank area immediately adjacent to a stream or river [40]. Many studies have demonstrated the effect of the spatial scale between landscape patterns and streamflow [41,42]. Various riparian scales are employed to study the correlation between a specific buffer zone along a particular river and changes in water quantity [43,44] and quality [45,46]. However, due to the heterogeneity of landscape patterns, differences in the intensity of human disturbance, and disparities in the spatial resolution of datasets, it is difficult to develop a uniform scale across basins that best explains the relationship between these two factors. To understand the hydrologic response amid spatial conflicts along the gullies and river valleys of the Loess Plateau, it is essential to incorporate the concept of riparian zones from landscape patterns. This involves defining the river valleys and gullies formed under the scouring of streamflow. Applying the concept of a stream buffer, considering the typical dimensions of gullies and valleys, allows for a more accurate exploration of hydrological responses across various scales, including both the basin scale and the stream scale.
Quantitative analysis of hydrological processes often relies on modeling techniques to simulate the impacts of LUCC, climate variability, and human activities. Hydrological models can be broadly categorized into lumped models, semi-distributed models, and fully spatially explicit distributed models [47]. Lumped models, while computationally efficient, are limited in capturing spatial heterogeneity, making them unsuitable for complex terrains, such as the Loess Plateau [48]. Fully spatially explicit distributed models, such as WEPP, offer high-resolution simulations [49]. WEPP excels in modeling small-scale erosion and hydrological processes by capturing fine-scale features, such as gullies and slopes, but is computationally demanding and data-intensive, limiting its application in large-scale studies. Semi-distributed models, such as the Soil and Water Assessment Tool (SWAT) [50], strike a balance between computational efficiency and spatial detail, making them particularly suited for watershed-scale analyses. The SWAT divides watersheds into sub-basins and hydrological response units (HRUs), allowing for detailed simulation of LUCC impacts while maintaining computational efficiency. This study employs the SWAT to explore the relationship between LUCC and streamflow in the SRB, a typical hilly and gully region of the Loess Plateau. The SWAT was selected due to its extensive validation in similar contexts, its ability to model interactions between LUCC and streamflow at both basin and sub-basin scales, and its proven effectiveness in simulating large-scale hydrological processes in the Loess Plateau. While fully spatially explicit models, such as WEPP, can better capture fine-scale heterogeneity, this study focuses on broader-scale LUCC impacts, for which the SWAT provides robust simulations with fewer computational demands [47].
The SRB in the western part of Shanxi Province, recognized as one of China’s eight soil erosion pilot zones in the 1980s, serves as the study object. Over time, the region has transformed into a typical valley city within a loess hilly and gully region due to rapid urbanization. Research has demonstrated that changes in streamflow within this basin are more influenced by LUCC resulting from human activities than by the effects of climate change [22,51]. Building upon this understanding, the study seeks to address the following questions from the perspective of the land use landscape using the SWAT model: (1) How does the SWAT model perform in the unique geomorphological and climatic conditions of the Loess Plateau’s gully regions? (2) How does hydrology respond at different scales of LUCC, including the basin scale and stream buffer scale? (3) What adaptive landscape planning and management strategies are suitable at the landscape scale? This research aims to offer practical insights for mitigating the escalating impact of human activities on erosion in this ecologically critical area and for achieving a balance between urbanization and land resource management.

2. Materials and Methods

2.1. Study Area

The SRB is located in Lvliang of Shanxi Province, within the central Loess Plateau (Figure 1a), a region characterized by hills and gullies known as one of the most erosion-prone areas in China [2,23,24]. This watershed spans an area of 4134.56 km2, with Sanchuan River as its main stream (Figure 1b). The SRB belongs to the continental monsoon climate zone in the warm temperate zone, with an average annual temperature of 8.9 °C and an average annual precipitation of 463 mm. The SRB is characterized by its deeply incised gullies and narrow river valleys, which form a highly fragmented landscape due to the interplay between water erosion and human activities. In the SRB, gullies generally range from 5 to 50 m in depth and 5 to 100 m in width, with some larger gullies in the middle and lower reaches broadening to over 100 m and transitioning into valleys exceeding 1–2 km in width. These gullies are concentrated primarily in the upper reaches, where steep slopes and narrow channels dominate, while the middle and lower reaches are marked by wider and flatter river valleys more suitable for human settlements.
The SRB exemplifies the typical land use and landscape changes occurring in the hilly and gully areas of the Loess Plateau. This mountainous and hilly terrain faces challenges, such as severe soil erosion and ecological degradation. In 1980, the basin was designated as one of the first eight soil and water conservation control basins in China. Measures implemented for soil and water conservation include terracing, afforestation, and the construction of silt dams and reservoirs. These efforts primarily target slopes along rivers within the watershed and in the gullies and slopes of branch gullies. Currently, the ecological environment of the SRB has witnessed significant improvement. However, with rapid urbanization, concentrated urban areas have emerged in the more open gullies and riverbanks in the middle and lower reaches of the basin. Therefore, researching the SRB to explore changes in land use and the width of riparian buffers in the hilly areas of the Loess Plateau holds significant relevance. Understanding the hydrological implications of LUCC in this fragile landscape provides critical insights for local ecological planning and sustainable development.

2.2. Data Collection

This study used various data to analyze streamflow responses to LUCC. Open-source meteorological data were collected from China Meteorological Data (http://www.escience.org.cn/), including daily precipitation, average temperature, maximum temperature, minimum temperature, relative humidity, pan evaporation, and sunshine hours. Precipitation data, spanning from 1970 to 2020, were obtained from 30 monitoring stations operated by the Lvliang Hydrological Survey Station (Figure 1c). Monthly discharge data were sourced from the Houdacheng Hydrological Station at the outlet of the SRB, recorded as discharge volumes. Both datasets cover the same time span. To address the temporal mismatch between daily precipitation data and monthly discharge data, the SWAT model’s daily streamflow simulations were aggregated into monthly values for calibration and validation. This approach aligns the temporal resolutions and is widely used in hydrological studies. However, using monthly discharge data may limit the ability to capture daily-scale dynamics and extreme flow events, which are particularly relevant in the Loess Plateau due to frequent intense rainfall. Despite this, the monthly-scale calibration ensures robust performance in capturing overall flow trends and provides reliable results for evaluating long-term hydrological impacts.
The land use types within the SRB were classified into five categories: agricultural land, grassland, forest land, wetland, and urban land. These classifications were based on visual interpretation of remote sensing data (1980–2020) provided by the Chinese Academy of Sciences (CAS), with a spatial resolution of 30 m. The classification was validated with ground truth data, achieving an overall accuracy of 85%. Agricultural land was parameterized in the SWAT model using wheat and corn as the dominant crops, while forest land was divided into deciduous and evergreen types. Urban land was categorized as medium-density areas, representing the small and gradually developing towns and cities on the Loess Plateau, effectively capturing the variability in impervious surfaces and their influence on runoff. Soil data supplemented by the Harmonized World Soil Database v1.2 (HWSD; http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1026141/, accessed on 12 October 2024) included soil type, soil texture, and hydraulic properties (e.g., bulk density, saturated hydraulic conductivity, and available water capacity). These data were essential for parameterizing the SWAT model and accurately simulating hydrological processes in the SRB. Readers are referred to the HWSD database for a detailed classification and characterization of the soil types visualized in Figure 1d.

2.3. Methodology

In this study, we first conducted an initial analysis of the spatial characteristics of LUCC in the study area, laying the necessary foundation for subsequent investigations. Following this, we constructed a SWAT model for the SRB using a basic database. Subsequently, we simulated the scale effect of streamflow change in response to LUCC by considering land use scenarios at various spatial scales. Finally, drawing upon the findings from our research, we discussed potential countermeasures aimed at optimizing the landscape pattern of the SRB.

2.3.1. LUCC Analysis

Using the Spatial Analyst tool of GIS 10.8 (Esri, Redlands, CA, USA), we analyzed land use types before and after change, to obtain the land use area transfer matrix. This matrix provides data on the mutual shift area between different land use types and their respective change transfer ratios in the SRB across various periods. The integrated dynamic attitude of LUCC reflects the rate of change of the overall land use type at a specific study time and spatial scale. It is calculated using the following formula:
R L U = ∑ i = 1 n Δ L U i − j L U × T × 100 %
where T is the timeframe of the study, L U is the area of the study area, and Δ L U i − j represents the absolute value of the area of land use i converted to a different type of land use j within the study period.

2.3.2. SWAT Model

I.
SWAT model setup
The SWAT model was constructed using aggregated data on elevation, soil type, and surface cover. To ensure the accuracy of the simulation results, the model outputs were validated against observed data from the watershed, using an iterative process to improve agreement between the simulated and observed values. The sub-basin catchment area threshold was set at 3000 hectares. Using the estuary of the Sanchuan River as the main watershed outlet, the entire watershed was divided into 73 sub-watersheds. Based on this division, HRUs were generated. HRUs are computational units within each sub-watershed that represent areas with homogeneous land use, soil, and slope characteristics, enabling detailed simulation of hydrological processes [47,50].
To simulate daily hydrological processes, the SWAT model operates on a daily time step, allowing it to capture variability and high-intensity rainfall events effectively. However, due to limitations in the temporal resolution of the available observed flow data, monthly averages were used for calibration and validation. This approach ensures consistency with the data while maintaining the model’s ability to simulate long-term trends. Despite this limitation, the use of daily simulations in the SWAT minimizes potential impacts, and the model remains capable of supporting future event-based analyses if higher-resolution data become available.
II.
Model calibration and validation
The SWAT model was calibrated using SWAT-CUP, specifically employing the Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm. This algorithm evaluates the goodness-of-fit of the model and quantifies the uncertainty in its predictions. The performance of the calibrated model was assessed using two widely recognized statistical metrics: the Nash–Sutcliffe model efficiency coefficient ( E N S ) and the coefficient of determination, R 2 . For detailed formulas and explanations of these metrics, readers can refer to relevant studies [20,52].
The warm-up period (1970–1974) was utilized to initialize the model and allow it to adjust to local hydrological conditions. Pre-calibration (1975–1979) refined the model parameters to enhance performance, while the actual calibration phase underwent 10 iterations with 500 parameter adjustments per iteration. Validation was conducted for 1980–1985 to assess model performance using an independent dataset. Both calibration and validation phases used monthly average flow data to simulate the observed trends effectively.
III.
Model parameter sensitivity calculation
The SUFI-2 algorithm in SWAT-CUP employs Latin Hypercube Sampling (LHS) to generate a wide range of parameter sets efficiently. LHS is a statistical method used to sample parameter space more effectively than simple random sampling. In the context of sensitivity analysis, SUFI-2 assesses the impact of parameter uncertainty on model outputs. To evaluate parameter sensitivity, SUFI-2 uses multiple regression models, where the sensitivity of each parameter is indicated by the t-statistic and the p-value. The t-statistic measures the relative influence of each parameter on the model output, with larger absolute values indicating greater sensitivity. The p-value assesses the significance of this sensitivity, with smaller values indicating higher statistical significance.

2.3.3. Scenario Definitions

Since the 1980s, human activities, such as soil conservation and urban construction, in the SRB have significantly influenced the land use landscape, leading to notable changes in the basin’s yield, catchment, and sediment load. Consequently, the land use in 1980 can be considered closest to the natural state of the basin’s subsurface condition. Based on the 1980 land use type, various LUCC test scenarios were established by modifying the watershed scale, sub-watershed scale, and the width of the buffer zone along the river channel. The calibrated SWAT model was then used to simulate streamflow under each scenario, allowing for a comparative analysis of how different land use changes influence hydrological processes.
  • Scenario I. Watershed scale: setting scenarios at different LUCCs in upper, middle, and lower basins
According to the Strahler stream order [53], the rivers in the SRB are categorized into four orders: first-order tributaries represent the upper reaches, second- and third-order tributaries indicate the middle reaches, and the combined fourth-order tributaries represent the lower reaches. The SRB can be divided into sub-basin types based on this classification (Figure 2a). Retaining the urban land and wetland unchanged from 1980, the remaining land use types were varied. Given that both forest land and shrubland are similarly effective in acting as sinks for water and sediment, the actual land use depends largely on the topographic slope direction and climatic conditions. Therefore, different land use types (agricultural land, forest land, and grassland) were assigned to the upstream, midstream, and downstream areas of the study region, respectively. These assignments were rotated in various combinations to create 27 different land use scenarios (Figure 2b). The distribution of land use in each simulation scenario is detailed in Table A1.
  • Scenario II. River scale: scenario setting for LUCC with different buffer widths
To investigate the influence of the buffer width on hydrological responses, we tested a range of buffer widths. The maximum buffer width was determined based on the analysis of characteristics of LUCC in the SRB, particularly the discussion on LUCC at different buffer scales along rivers (see Section 3.1.2). This range was divided into 10 equal intervals, resulting in 10 buffer groups with incrementally varying widths. Agricultural land, forest land, grassland, and urban land were then paired with these 10 groups of buffer widths to create 40 experimental scenarios (Figure A1). The area shares and spatial distributions of land use types within these 40 scenarios are detailed in Table A2.

3. Results and Discussion

3.1. Characteristics of LUCC in the SRB

3.1.1. Land Use/Cover Conversion in SRB

This study analyzed LUCC in the basin from 1980 to 2020, providing insights into the spatial distribution and dynamics of land use changes (Figure 3a). Over the past 40 years, noticeable changes in land use have occurred, particularly in the gullies and river valleys, driven by urban expansion and agricultural intensification. Combined with the LUCC transfer matrix (Table A3), it is evident from Figure 3b that the area of urban land expanded significantly along the river valley, increasing from 15.5 km2 in 1980 to 145.77 km2 in 2020. Approximately 72% of this increase in the urban land area originated from agricultural land, with the remaining 22% from grassland. Regarding the decrease in agricultural land area by 191.15 km2, half of it was converted into urban land, while 24% and 20% were converted into grassland and shrubland, respectively. The portion of agricultural land area that increased primarily saw conversions to grassland, accounting for nearly 75%. Moreover, the grassland area across the entire basin decreased by 42.08 km2, indicating frequent landscape changes between agricultural land and grassland in the river valley and hilly and gully areas of the basin. Additionally, a notable interconversion relationship exists between forest land and shrubland, with nearly 60% of the forest land area converted to shrubland over 40 years. Conversely, almost half of the increase in forest land area originated from shrubland.

3.1.2. LUCC at Different Buffer Scales Along Rivers

To comprehensively examine the spatial and temporal variations in LUCC, the GIS buffer tool was utilized to establish buffer ranges at 500 m intervals from 0 to 3000 m along the stream within the SRB. By calculating the integrated LUCC dynamics of different buffer zones over the 40 years from 1980 to 2020, it became apparent that the highest integrated LUCC dynamics occurred within the 1500 m buffer zone. As the distance from the river increased, the integrated dynamics of LUCC decreased, indicating that the river valley and gully areas in the SRB experienced the most frequent human activities and underwent the most significant land use changes. To delve deeper into LUCC within stream buffers, we established buffer ranges from 0 to 3000 m at 500 m intervals along streams within the SRB using the GIS buffer tool (Figure 4b). This approach enabled us to examine how LUCC varied across different buffer zones over the 40 years from 1980 to 2020 (Figure 4a). Our analysis revealed that the integrated dynamics of LUCC were most pronounced within the 1500 m buffer zone. This suggests that the river valleys and gullies within this 1500 m range of the SRB watershed experienced the most frequent human activities, leading to notable changes in land use and cover. Within the 0–1500 m buffer zone, we observed that the integrated dynamic (calculated using Equation (1)) change in LUCC increased with distance from the river, and this trend intensified with the expansion of the buffer area. This phenomenon indicates that within the 1500 m river buffer range, land change became more pronounced, and human activities intensified as we moved farther away from the river. These findings underscore the significance of river valleys and adjacent areas in driving land use dynamics and human-induced changes.

3.2. SWAT Model Calibration and Parameters’ Sensitivity

Through the SWAT-CUP calibration process, the model achieved an E N S of 0.73 and an R 2 value of 0.83 for monthly streamflow during the calibration period. For the validation period, the E N S was 0.70, and the R 2 value was 0.78. These results demonstrate that the SWAT model performed reasonably well in simulating monthly streamflow, aligning with recommended statistical benchmarks for hydrological models operating on monthly time steps [54]. Similar studies using the SWAT model in regions with comparable hydrological and land use characteristics, such as the loess hill and gully region, reported similar values of E N S and R 2 , indicating that our model’s performance was consistent with or slightly above the norms for this type of study [52,53]. Figure 5 illustrates the consistency between the observed and simulated streamflow data, reaffirming the model’s reliability in reproducing streamflow dynamics.
To calibrate the model, 17 parameters were selected based on previous studies [52,55] and their relevance to the hydrological processes in the SRB [56]. Using measured monthly streamflow data from the Houdacheng Hydrological Station, we determined the final parameter ranges and optimal values for runoff simulation through 5000 iterations of the calibration process, as summarized in Table 1. Parameter sensitivity was evaluated using the t-stat and p-value metrics. The p-value indicates the statistical significance of a parameter’s influence on the simulation, with values ≤ 0.05 signifying high sensitivity. The t-stat, on the other hand, quantifies the magnitude of sensitivity, with larger absolute values denoting greater influence on simulation outcomes.
While the SWAT primarily operates as a process-based model, it integrates physical and empirical components to address challenges inherent to fully distributed physically based models [57]. The sensitivity of the SWAT parameters is closely tied to the basin’s land use, topographic, and soil characteristics, all of which significantly impact streamflow. In this study, 10 parameters exhibited significant sensitivity, consistent with findings from other basins within the loess hilly and gully region [58,59].
One such parameter, CN2, is an empirically established dimensionless parameter, with values ranging from 1 (indicating fully permeable soils) to 100 (indicating fully impermeable soils) [60]. The sensitivity of CN2 underscores the considerable influence of land use, soil type, and antecedent moisture conditions on surface runoff and streamflow dynamics [52]. Additionally, the higher bulk density of soil (SOL_BD) results in a smaller saturated hydraulic conductivity (SOL_K) and an increased surface runoff volume [61]. This relationship highlights the impact of Loess Plateau soil characteristics on streamflow, particularly reflecting the limited water content in shallow soil layers due to the deep soil cover. Moreover, parameters such as GW_REVAP, GW_DELAY, and GWQMN reflect the deep groundwater table and the challenge of recharging shallow aquifers in the SRB. The sensitivity to CH_N2, representing the Manning coefficient of the main channel, underscores the significant influence of channel roughness on streamflow within the basin [62]. Another parameter, ALPHA_BN, representing the base flow factor for riparian storage, exhibited an optimal value of 0.17 in our model. This value reflects the rapid changes in riverbank water levels due to substantial elevation differences in the SRB [55,59]. Additionally, the frequent appearance of the parameters HRU_SLP or SLSUBBSN in modeling hilly gully basins indicates the influence of natural geographic features, such as slope and slope length, on the streamflow process [58,59].

3.3. Streamflow Response to Multiple Scenarios

3.3.1. Under Scenarios of Different LUCCs in Upper, Middle, and Lower Basins

From 1976 through 1985, characterized by moderate rainfall, measured meteorological data were selected. Subsequently, 27 different land use types in sub-basins were inputted into the SWAT model to generate simulation results depicting multi-year average streamflow changes in the basin (Figure 6). Initially, in the extreme land use scenarios encompassing the entire basin (S1, S11, and S21), runoff from forest land exhibited the smallest magnitude, followed by runoff from grassland, while agricultural land displayed the largest change in streamflow. This indicates notable disparities in the ability of various land uses to regulate streamflow in the SRB. Based on the multi-year average change in streamflow, it was observed that extreme agricultural land cover generated over twice the amount of runoff compared to extreme forest land cover. In the comparative analysis conducted under different land use changes and combinations in the upper and middle reaches, it was found that when the upper area was forest land, streamflow from agricultural land in the middle reaches significantly exceeded that from forest land or grassland. Specifically, the former surpassed the latter by more than double. Conversely, with grassland upstream, midstream streamflow from agricultural land was approximately half that of forest land or grassland. Similarly, midstream streamflow produced by agricultural land was roughly one-quarter higher than that produced by forest land or grassland upstream of agricultural land. These findings indicate that streamflow production was higher when agricultural land occupies the upstream region compared to forest land or grassland, as reflected in the results. The modeling results for the two scenarios were comparable when the land use of the upstream and downstream sub-basins was determined, and when the midstream sub-basin was forest land or grassland. However, if the midstream consisted of agricultural land, the average level of streamflow production was 15.8% and 20.9% higher than the average of the first two scenarios, respectively.

3.3.2. Under Scenarios of LUCC with Different Buffer Widths

Based on the analysis of the integrated dynamics of LUCC, a buffer range of 1500 m was selected to construct scenarios of land use change. Buffer zones were established along both sides of the river at 150 m intervals, resulting in a total of 10 buffer zones (Figure 7a). Using the land use/cover in 1980 as the base, different buffer widths were matched with forest land, agricultural land, grassland, and urban land, resulting in a total of 40 land use scenarios.
The SWAT simulation continued under climatic conditions from 1980 to 1989. The outcomes (Figure 7b) indicate that basin streamflow tended to decrease with the widening of forest or grassland buffer zones along the river, while it increased with the narrowing of agricultural land or urban land buffer zones. When analyzing the trend of annual streamflow changes, it was observed that widening forest and grass buffer zones by every 100 m effectively reduced streamflow by 0.088 m3/s and 0.026 m3/s per year, respectively. Conversely, expanding agricultural land buffers by every 100 m increased streamflow by 0.052 m3/s per year. Similarly, the increased width of construction land along the stream channel resulted in greater annual streamflow, with every 100 m of widening resulting in an increase of 0.12 m3/s in annual streamflow.

3.3.3. Scaling Effects of LUCC on Streamflow

LUCC within the SRB, such as the conversion between agricultural land and grassland, the expansion of urban areas, and the increase in water body coverage, primarily occurred within the gully or river valley regions of the basin. These land use alterations exhibited a clear spatial correlation with the unique characteristics of the Loess Plateau, which is distinguished by numerous hills and valleys [63,64]. This pattern of land transformation is also reminiscent of the urbanization trends observed in other mountainous regions across China [65,66]. Whether within cities or towns, the natural geographic features exert a more pronounced influence on the size, distribution, and structure of settlements in mountainous areas compared to those in plains [67,68].
Therefore, this study utilized the SWAT model to simulate and analyze various spatial scales of land use change scenarios in the SRB. The findings revealed significant scale effects of land use change on streamflow. On one hand, the impact of land use change on streamflow varied notably across different areas within the basin. Distinct land use types in the upper, middle, and lower reaches of the basin exhibited distinct impacts on streamflow dynamics, which are closely associated with the potential for soil erosion, as highlighted in previous studies [69,70,71]. Additionally, the findings underscore the importance of considering diverse hydrological processes that can emerge from varying spatial configurations of land use patterns within the basin (see Figure 6). Through scenario modeling, it was observed that forested land and shrubland in the upper region of the basin played a crucial role in stormwater regulation. In contrast, land use changes in the middle and lower reaches of the basin exhibited minimal impact on annual streamflow. The Loess Plateau is characterized by distinct altitude variations, particularly evident in the SRB, where the elevation range exceeds 4000 m. This significant elevation drop across the basin facilitates the rapid discharge of precipitation-generated runoff from the upstream sub-catchment area. Increasing the structural diversity (e.g., above-ground vegetation) and components (e.g., deadfall cover and organic matter) of ecological surface formations in upstream regions enhances surface roughness, boosts infiltration rates, and lowers runoff generation thresholds [2,10,11,12]. Consequently, these changes significantly enhance the soil’s resistance to water erosion. In contrast, the downstream area, characterized by relatively flat terrain, experiences minimal effects from land use changes on streamflow processes.
On the other hand, the width of buffer zones played a critical role in the runoff control effects across different land use types, with distinct scaling effects observed within varying width ranges. These patterns highlight how the buffer width influenced the hydrological responses of each land use type. Forest land buffers showed a progressive improvement in runoff control capacity as the width increased, making them the most effective land type for reducing runoff. Grassland buffers, while less effective overall, exhibited a non-linear pattern: their runoff control was slightly better within the 0–300 m range, diminished between 300 and 750 m, and improved again at widths exceeding 750 m. Interestingly, despite the differing runoff control capacities of forest, grassland, agricultural, and urban land buffers, a common turning point emerged at a width of approximately 750 m for all land use types. Beyond this width, the impact of buffer zones on runoff became notably different: forest and grassland buffers regained or strengthened their runoff control abilities, while urban and agricultural buffers amplified streamflow more significantly. For agricultural land, streamflow increased linearly with the buffer width, while urban land buffers exhibited the strongest influence on runoff when buffer widths exceeded 750 m. These findings underscore that the buffer zone width is a critical factor in runoff management, and the shared turning point at 750 m suggests a pivotal threshold for designing buffer zones across different land use types. The emergence of this threshold highlights the importance of tailoring land management practices to optimize hydrological outcomes, ensuring effective runoff control while minimizing adverse impacts on streamflow dynamics.
The changes in streamflow or water quality due to similar spatial scale changes have been well studied [72,73,74]. However, the study revealed two critical values of 300 m and 750 m for the river buffer range, which stand out as unique findings in the context of the streamflow response to land use change within the SRB. These critical values are closely linked to the width and slope variations characteristic of the loess hill topography. Examination of DEM data indicated that these critical values align with inflection points of a slope change, reflecting the distinctive terrain features of the basin. Similar to other basins in the loess hilly and gully region, the SRB exhibited a terrace-shaped bed [23]. Moreover, discussions on land use planning and vegetation planting on the Loess Plateau have underscored the significance of slope change [1,75]. This further validates the heightened sensitivity of the SWAT model constructed in this study to specific parameters of natural geographic features, such as topographic slope and slope length, distinguishing it from modeling parameters in other regions.

3.4. A Proposal for Optimizing Landscape Patterns in the SRB for Flow Control

The theory of landscape ecology provides a comprehensive framework for the scaling effects of LUCC on streamflow alterations. Within this framework, landscapes are comprised of patches, corridors, and matrix mosaics, each contributing to the spatial structure of the landscape pattern. This pattern is shaped by both human activities and natural evolution, and its distribution across the basin directly influences ecohydrological processes, such as streamflow and sedimentation [15,76]. LUCC impacts water flow pathways by influencing several factors, including rainfall infiltration, surface streamflow interception, and groundwater recharge. Despite experiencing similar precipitation, different land use types exhibit varying hydrologic functions, resulting in distinct streamflow and sedimentation outcomes [77,78].
Based on the findings of this study, recommendations for ecological planning in the SRB are proposed, emphasizing the importance of scaling effects and spatial optimization of land use types to regulate streamflow and enhance water conservation. At the basin scale, it is critical to ensure that forest land, grassland, and water bodies are strategically distributed to maximize their runoff control capacity. For instance, this study highlighted that the effectiveness of forest and grassland buffers in reducing streamflow varied significantly with buffer widths, suggesting that sufficient buffer zones are necessary to optimize their hydrological function. Agricultural and urban land use should be carefully regulated, particularly in upstream areas, to mitigate their disproportionate impact on streamflow and soil erosion. In the upstream region, conservation measures, such as soil and water conservation practices, afforestation, and vegetation restoration, should be prioritized to enhance the ecological environment and stabilize runoff dynamics. In the middle reaches, agricultural and urban land expansion should be restricted to prevent exacerbating streamflow fluctuations and ecological degradation. Conversely, downstream areas, which are more hydrologically stable, may be more appropriate for agricultural and urban development, provided they do not compromise the basin’s overall ecological integrity. These recommendations underscore the necessity of integrating spatial and scaling considerations into land use planning, ensuring that the landscape configuration effectively balances ecological and developmental needs.
Furthermore, in terms of landscape structure optimization, fostering positive relationships between urbanization and urban greening development is imperative, requiring innovative approaches [79]. The ribbon space created by the river and the adjoining land should prioritize natural features, such as river lakes, mudflats, depressions, and grassy slopes. This approach aims to restore the ecological attributes of wetlands and floodplain areas, enhancing their environmental significance.
In the middle and lower reaches of the river, urban areas should adopt advanced stormwater management concepts, such as sponge cities and water-sensitive cities, to establish a blue–green infrastructure system suitable for the Loess Plateau region. Blue–green infrastructure leverages the symbiotic relationship between vegetation and the water cycle to enhance urban living conditions [80], promoting sustainable development and ecosystem services related to water and greening. This approach is crucial for sustainable construction in the loess hilly and gully region [81]. Particularly within 300 m along the river channel, efforts to replace parkland grassland with areas of meadow vegetation could mitigate some of these impacts. Diversifying urban greening through the introduction of urban grassland instead of lawns may offer significant biodiversity benefits, with various grassland types likely to enhance these advantages [82].

3.5. Limitations

There are several limitations worth considering in this study. Firstly, while this paper utilized SWAT modeling to offer insights into the impacts of LUCC on streamflow in the SRB, it also investigated the effects of geomorphic conditions on parameter sensitivity in the study area. However, the limited duration of operation of other hydrologic stations in the basin means they provide insufficient data to support this study. Consequently, this paper relied solely on the data from the Houdacheng Hydrological Station at the basin outlet for SWAT correction. The reliability and robustness of the modeling results may be compromised by the constraints of the input data. The precipitation data were collected at a daily resolution, while the observed streamflow data used for calibration and validation were available only as monthly averages. This temporal mismatch might limit the ability to validate daily-scale hydrological dynamics and extreme flow events explicitly. However, the SWAT model inherently operates on a daily time step, ensuring the simulation of rainfall–runoff processes at fine temporal resolutions. This framework minimizes the potential impact of this limitation and allows for future analyses focusing on daily or event-based hydrological processes if high-resolution streamflow data become available.
Additionally, the sediment modeling faces limitations due to significant yearly fluctuations in sediment transport, further diminishing accuracy owing to reliance on data from a single hydrological station. Therefore, this study concentrated on the effects of LUCC on runoff changes and did not delve into the relationship between the land use landscape and soil erosion, encompassing streamflow and sediment production. This limitation underscores the necessity for broader and longer-term datasets to enhance the accuracy and reliability of modeling results. Future research could focus on expanding the hydrologic station network, increasing data collection efforts, and employing advanced data interpolation and validation techniques to overcome this limitation.
Secondly, while many studies have explored urban expansion patterns through simulation prediction [83,84], which is effective in examining the impact of LUCC on the hydrological cycle [30], the dynamics of urbanization may not solely be driven by natural or socioeconomic factors but also heavily influenced by external policies [85]. The interaction between policy interventions and urban development patterns is particularly pronounced in hilly and gully areas, where economic development lags. Hence, this study simplified the land use settings by utilizing buffer zone construction in conjunction with changing urbanization patterns in hilly and gully areas, focusing mainly on the spatial and scale impacts of land use changes on streams. Nevertheless, future research could explore how policy changes affect land use decisions, consequently influencing stream dynamics. Additionally, integrating policy scenarios into the modeling framework could provide insight into potential future trajectories of urban expansion and their hydrologic process impacts.
Furthermore, this study solely focused on streamflow as the indicator and did not address the concept of runoff in the analysis. In the loess hilly and gully regions, surface streamflow may be the main mechanism of runoff rather than groundwater flow [12,86]. Streamflow may, therefore, more accurately portray the basin’s hydrologic characteristics, particularly in terms of water yield and discharge into the larger Yellow River system. Concentrating solely on streamflow enabled a focused study of these aspects without the complexity of incorporating runoff data. Moreover, river flow measurements are more readily available than streamflow data, especially in areas with established hydrologic monitoring networks. In many instances, government agencies or research institutions routinely collect streamflow data, rendering it a convenient and accessible metric for hydrologic studies.

4. Conclusions

With the implementation of soil and water conservation projects and urbanization, LUCC in the SRB has primarily transformed gullies and valleys from 1980 to the present. Using the 1980 land use status as a baseline, the SWAT model was employed to accurately simulate both runoff generation and discharge dynamics. By constructing and analyzing land use scenarios at varying spatial scales, the results revealed that streamflow exhibited distinct scale-dependent patterns across different landscape configurations. Grassland and forest land in riparian zones demonstrated specific thresholds for runoff control effectiveness, emphasizing the critical role of scale in land use management. Furthermore, the study highlighted that LUCC impacts differed spatially, with diminishing effects on streamflow observed from the upper to the lower reaches of the basin, irrespective of land use type.
These findings provide valuable insights for decision-makers to formulate more comprehensive and region-specific land use policies and basin management strategies. Specifically, the study emphasized the importance of implementing scale-sensitive land use management practices, particularly in riparian zones, to optimize hydrological benefits.

Author Contributions

Conceptualization, Z.L. and S.Z.; methodology, Z.L.; software, Z.L.; validation, Z.L. and W.Z.; formal analysis, Z.L.; investigation, W.Z.; resources, X.Z.; data curation, Z.L.; writing—original draft preparation, Z.L., W.Z. and X.Z.; writing—review and editing, S.Z. and J.G.; visualization, S.Z.; supervision, S.Z.; project administration, Z.L.; funding acquisition, Z.L. and J.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Shanxi Province Science Foundation for Youths, grant number 202203021222125.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Area statistics of land types under simulation scenarios in the upper, middle, and lower reaches of the basin.
Table A1. Area statistics of land types under simulation scenarios in the upper, middle, and lower reaches of the basin.
Scenario NumberDistribution of Land Use TypesPercentage of Land Use Types (%)
Upper Sub-BasinMiddle Sub-BasinLower Sub-BasinForest LandGrasslandAgricultural Land
SI1Forest landForest landForest land99.400.000.00
SI2Grassland91.807.600.00
SI3Agricultural land91.800.007.60
SI4GrasslandForest land66.6132.790.00
SI5Grassland59.0140.390.00
SI6Agricultural land59.0132.797.60
SI7Agricultural landForest land66.610.0032.79
SI8Grassland59.017.6032.79
SI9Agricultural land59.010.0040.39
SI10GrasslandGrasslandForest land7.6091.800.00
SI11Grassland0.0099.400.00
SI12Agricultural land0.0091.807.60
SI13Forest landForest land40.3959.010.00
SI14Grassland32.7966.610.00
SI15Agricultural land32.7959.017.60
SI16Agricultural landForest land7.6059.0132.79
SI17Grassland0.0066.6132.79
SI18Agricultural land0.0059.0140.39
SI19Agricultural landAgricultural landForest land7.600.0091.80
SI20Grassland0.007.6091.80
SI21Agricultural land0.000.0099.40
SI22Forest landForest land40.390.0059.01
SI23Grassland32.797.6059.01
SI24Agricultural land32.790.0066.61
SI25GrasslandForest land7.6032.7959.01
SI26Grassland0.0040.3959.01
SI27Agricultural land0.0032.7966.61
Sustainability 16 10781 g0a1
Figure A1. Scenarios of different land uses in the buffer zone along the river based on 1980 land use.
Figure A1. Scenarios of different land uses in the buffer zone along the river based on 1980 land use.
Sustainability 16 10781 g0a1
Table A2. Area statistics of land use types under simulation scenarios with different buffer widths of the basin stream.
Table A2. Area statistics of land use types under simulation scenarios with different buffer widths of the basin stream.
Scenario NumberLand Use Types Along the RiverBuffer Width (m)Scenario NumberLand Use Types Along the RiverBuffer Width (m)
SII1Forest land150SII21Agricultural land150
SII2Forest land300SII22Agricultural land300
SII3Forest land450SII23Agricultural land450
SII4Forest land600SII24Agricultural land600
SII5Forest land750SII25Agricultural land750
SII6Forest land900SII26Agricultural land900
SII7Forest land1050SII27Agricultural land1050
SII8Forest land1200SII28Agricultural land1200
SII9Forest land1350SII29Agricultural land1350
SII10Forest land1500SII30Agricultural land1500
SII11Grassland150SII31Urban land150
SII12Grassland300SII32Urban land300
SII13Grassland450SII33Urban land450
SII14Grassland600SII34Urban land600
SII15Grassland750SII35Urban land750
SII16Grassland900SII36Urban land900
SII17Grassland1050SII37Urban land1050
SII18Grassland1200SII38Urban land1200
SII 19Grassland1350SII39Urban land1350
SII 20Grassland1500SII40Urban land1500
Table A3. Land use/cover conversion matrix for the period 1980–2020 (in km2).
Table A3. Land use/cover conversion matrix for the period 1980–2020 (in km2).
Land Use/Cover Types2020
Agricultural LandUrban LandForest LandScrublandGrasslandWetlandTotal
1980Agricultural land947.9994.297.9638.4745.345.091139.14
Urban land0.5814.7000.010.180.0315.50
Forest land5.411.50760.3712.562.290.27782.40
Scrubland11.174.569.981092.763.530.421122.42
Grassland53.8628.673.076.17971.782.141065.69
Wetland1.402.051.040.060.494.379.41
Total1020.41145.77782.421150.031023.6112.324134.56

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Figure 1. Location of the SRB. (a) The SRB in the Loess Plateau of northwest China, (b) the digital elevation model of the SRB, (c) rivers and major hydrologic stations of the SRB, and (d) soil classification of the SRB. Readers can refer to the official database (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/, accessed on 12 October 2024) for a complete list of soil classes and their definitions.
Figure 1. Location of the SRB. (a) The SRB in the Loess Plateau of northwest China, (b) the digital elevation model of the SRB, (c) rivers and major hydrologic stations of the SRB, and (d) soil classification of the SRB. Readers can refer to the official database (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/, accessed on 12 October 2024) for a complete list of soil classes and their definitions.
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Figure 2. Setting scenarios at different LUCCs in upper, middle, and lower basins. (a) Upstream, midstream, and downstream zoning of SRB, and (b) 27 scenarios set up for LUCC in the upstream, midstream, and downstream based on 1980 land use/cover.
Figure 2. Setting scenarios at different LUCCs in upper, middle, and lower basins. (a) Upstream, midstream, and downstream zoning of SRB, and (b) 27 scenarios set up for LUCC in the upstream, midstream, and downstream based on 1980 land use/cover.
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Figure 3. Land use/cover change from 1980 to 2020: (a) land use/cover of the SRB in 1980 and 2020, and (b) Sankey diagram of LUCC from 1980 to 2020.
Figure 3. Land use/cover change from 1980 to 2020: (a) land use/cover of the SRB in 1980 and 2020, and (b) Sankey diagram of LUCC from 1980 to 2020.
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Figure 4. LUCC at different buffer scales along the stream. (a) Integrated LUCC dynamics at different buffer scales. (b) Spatial transformation of land use/cover at different buffer scales.
Figure 4. LUCC at different buffer scales along the stream. (a) Integrated LUCC dynamics at different buffer scales. (b) Spatial transformation of land use/cover at different buffer scales.
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Figure 5. The comparison data of monthly streamflow volume from 1975 to 1984, between the measured value of the Houdacheng Hydrological Station and the simulated value of the SWAT model.
Figure 5. The comparison data of monthly streamflow volume from 1975 to 1984, between the measured value of the Houdacheng Hydrological Station and the simulated value of the SWAT model.
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Figure 6. Model simulation results of multi-year average streamflow in the SRB under 27 scenarios of different LUCCs in the upper, middle, and lower basins.
Figure 6. Model simulation results of multi-year average streamflow in the SRB under 27 scenarios of different LUCCs in the upper, middle, and lower basins.
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Figure 7. Model simulation results under scenarios of LUCC with different buffer widths. (a) Buffer zones of buffer widths from 150 to 1500 m. (b) Model simulation results of multi-year average streamflow in the SRB under 40 scenarios of LUCC with different buffer widths.
Figure 7. Model simulation results under scenarios of LUCC with different buffer widths. (a) Buffer zones of buffer widths from 150 to 1500 m. (b) Model simulation results of multi-year average streamflow in the SRB under 40 scenarios of LUCC with different buffer widths.
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Table 1. Results of parameter calibration and sensitivity ranking of the SWAT model for the SRB.
Table 1. Results of parameter calibration and sensitivity ranking of the SWAT model for the SRB.
Sensitivity RankingParameterst-Statp-ValueReasonable Range of ValuesOptimal Value
Min.Max.
1R__CN2.mgt−57.720−0.50.5−0.431
2V__GW_DELAY.gw18030450374.82
3R__SOL_BD.sol−10.80−0.50.5−0.059
4R__HRU_SLP.hru−8.90−0.50.5−0.401
5R__SOL_K.sol−8.30−0.50.5−0.319
6V__ALPHA_BNK.rte−7.80010.166667
7R__SLSUBBSN.hru5.30−0.50.50.001
8V__CH_N2.rte3.10.0100.30.0861
9V__GWQMN.gw2.80.0201000721
10V__GW_REVAP.gw2.50.05010.553
11V__CH_K2.rte2.30.070508.650001
12V__ESCO.hru−2.10.090.110.2917
13R__SOL_AWC.sol−1.80.11−0.50.5−0.215
14V__ALPHA_BF.gw−1.50.19010.003
15R__OV_N.hru0.90.53−0.50.5−0.061
16V__EPCO.hru0.50.72010.053
17V__REVAPMN.gw0.10.97050033.5
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Lei, Z.; Zhang, S.; Zhang, W.; Zhao, X.; Gao, J. Multi-Scale Effect of Land Use Landscape on Basin Streamflow Impacts in Loess Hilly and Gully Region of Loess Plateau: Insights from the Sanchuan River Basin, China. Sustainability 2024, 16, 10781. https://doi.org/10.3390/su162310781

AMA Style

Lei Z, Zhang S, Zhang W, Zhao X, Gao J. Multi-Scale Effect of Land Use Landscape on Basin Streamflow Impacts in Loess Hilly and Gully Region of Loess Plateau: Insights from the Sanchuan River Basin, China. Sustainability. 2024; 16(23):10781. https://doi.org/10.3390/su162310781

Chicago/Turabian Style

Lei, Zexin, Shifang Zhang, Wenzheng Zhang, Xuqiang Zhao, and Jing Gao. 2024. "Multi-Scale Effect of Land Use Landscape on Basin Streamflow Impacts in Loess Hilly and Gully Region of Loess Plateau: Insights from the Sanchuan River Basin, China" Sustainability 16, no. 23: 10781. https://doi.org/10.3390/su162310781

APA Style

Lei, Z., Zhang, S., Zhang, W., Zhao, X., & Gao, J. (2024). Multi-Scale Effect of Land Use Landscape on Basin Streamflow Impacts in Loess Hilly and Gully Region of Loess Plateau: Insights from the Sanchuan River Basin, China. Sustainability, 16(23), 10781. https://doi.org/10.3390/su162310781

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