Open AccessArticle

The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China

Yonggang Hao

^1,2

Peng Qi

^2,*

and

Chong Du

^1,*

Institute of Water Conservancy and Electric Power, Heilongjiang University, Harbin 150080, China

State Key Laboratory of Black Soils Conservation and Utilizations, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No.4888, Shengbei Street, Changchun 130102, China

Authors to whom correspondence should be addressed.

Water 2024, 16(23), 3456; https://doi.org/10.3390/w16233456

Submission received: 17 October 2024 / Revised: 14 November 2024 / Accepted: 27 November 2024 / Published: 1 December 2024

(This article belongs to the Section Soil and Water)

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Figure 1
Location and elevations of the River Basin in Northeast Black Soil Region of China, gauging stations, weather stations, rivers, and the study area. "> Figure 2
Land use in 1980, 1990, 2000, 2010, and 2020 in River Basin in Northeast Black Soil Region, China. "> Figure 3
Land use transfer matrix from 1980 to 2020. The lines illustrate the conversion between different land use types across time periods, highlighting the dynamics of land use change. "> Figure 4
Annual runoff at the downstream outlet of the Songhua River under different scenarios (S0, S1, S2, S3, and S4). "> Figure 5
Monthly distribution of multi-year average water yield in the River Basin of Northeast Black Soil Region, China, under different scenarios (S0, S1, S2, S3, and S4). "> Figure 6
Runoff coefficient distribution in River Basin in Northeast Black Soil Region, China. "> Figure 7
The six hydrological variables expressed as a proportion of the mean annual precipitation are: groundwater contribution to streamflow (GWQ), lateral flow contribution to streamflow (LATQ), percolation beyond the root zone (PERC), surface runoff generated within the watershed (SURQ), and soil water (SW). The six hydrological variables expressed as a proportion of the mean annual precipitation are: groundwater contribution to streamflow (GWQ), lateral flow contribution to streamflow (LATQ), percolation beyond the root zone (PERC), surface runoff generated within the watershed (SURQ), soil water content (SW), and water yield (WYLD). "> Figure 8
Spatial distribution of seasonal water yield under different scenarios (S0, S1, S2, S3, and S4). Each subfigure (a–d) represents the water yield for spring, summer, autumn, and winter. "> Figure 9
Spatial distribution of absolute changes in seasonal water yield under different scenarios (S0, S1, S2, S3, and S4). ">

Versions Notes

Abstract

With the intensification of climate change and human activities, the impacts of land use shifts on hydrological processes are becoming more pronounced, especially in regions with complex geographic, geological, and climatic conditions such as the Northeast Black Soil Region, China. This study quantitatively examines the variations in various land use types from 1980 to 2020 by means of a land use transfer matrix, and it incorporates the multi-year average runoff value to mitigate the interference of short-term climate fluctuations on the runoff trend, thereby enhancing the representativeness and stability of the simulation outcomes. The SWAT (Soil and Water Assessment Tool) model is employed to simulate land use alterations in different periods. The findings indicate that the area of farmland increased by 5.34% and the area of grassland decreased by 5.36% over 40 years. The areas of forest land and wetland have fluctuated significantly due to policy interventions and population growth. This study discovers that LUCC has resulted in a marginal increase in annual water yield. For instance, the water yield of paddy fields in 2020 amounts to 92.26 mm/year, which is 0.52–9.42% higher than the historical scenario and exhibits a notable upward trend in summer. Spatial analysis discloses regional disparities, with substantial changes in the hydrological behavior of northern watersheds (such as the Huma River) and southeastern regions (such as the Toudao River). The augmentation of wetland and forest coverage has effectively mitigated peak runoff, especially during extreme rainfall events. Wetlands have manifested strong water regulation capabilities and alleviated the impact of floods. This study quantitatively discloses the complex response pattern of LUCC to runoff by introducing a multi-scale analysis approach, which furnishes a scientific basis for flood risk assessment, land use optimization, and water resource management, and demonstrates the potential for extensive application in other countries and regions with similar climatic and topographic conditions.

Keywords:

land use change; runoff; Soil and Water Assessment Tool; human activities

1. Introduction

In the context of global environmental change, alterations in the hydrological cycle have emerged as a prominent subject of discussion [1]. Runoff, which serves as a crucial component of the hydrological cycle, not only acts as the primary conduit for water resources, but also plays a vital role in maintaining ecosystems and regulating floods [2]. Human activities and climate change significantly influence runoff intensity and impact [3,4,5]. On longer time scales, climate change may exert a more pronounced influence on runoff, whereas land use changes can directly manifest the impact of human activities on the environment and visually depict regional runoff trends on shorter time scales [6]. Specifically, land use change can directly affect key hydrological processes, such as surface runoff, groundwater recharge, and evapotranspiration, thus changing the runoff pattern of a watershed [7,8]. Studying the effects of various land use types on runoff at the watershed scale provides valuable insights into the relationship between water supply and demand. This understanding can serve as a scientific foundation for effective water resource management and land use planning. It also helps in developing sensible strategies to manage land use and water resources, addressing potential future challenges related to climate change and human activities.

Many studies have shown that land use affects runoff directly or indirectly. Munoth modeled that an increase in agricultural land area and a decrease in forests and pastureland in the Tapi River Basin, India, would result in increased surface runoff and sediment [9]. Liu demonstrated that changes in land use and cover change (LUCC) within China resulted in a reduction in evapotranspiration, while simultaneously increasing water quantity and runoff, agricultural land conversion, and management were identified as pivotal factors contributing to these changes [10]. The study conducted by Serp demonstrated that land use conversion in the Mediterranean Basin results in a significant augmentation of runoff [11]. Ochoa-Tocachi et al. revealed that LUCC led to reduced watershed regulation and water yield in the Andean Basin [12]. Hu et al. found that increased forest cover in the Gushanchuan watershed in the middle reaches of the Yellow River extended the catchment’s catchment duration and improved the watershed’s water storage capacity [13]; and Wan et al. demonstrated that urbanization in the Taihu Lake watershed will increase the risk of floods, and reforestation can help to mitigate the risk of floods [14]. This indicates significant advancements in current research on the impacts of land use and land cover change (LUCC) on runoff; however, most studies primarily focus on specific regions or watersheds, and there is still a need to adequately verify the generalizability of LUCC effects on runoff under diverse climatic, topographic, and vegetation conditions. In addition, the impacts of LUCC exhibit spatial scale-dependent variations, and current studies often face challenges in effectively extrapolating findings from small-scale investigations to larger scales or lack comprehensive exploration of multi-scale coupling effects.

The impacts of LUCC on runoff can be studied using various methodologies, and the early scholars primarily employed experimental comparisons or the time series method to analyze hydrological characteristic parameters for small and medium-sized watersheds [15]. This method provides more precise hydrological data in localized areas and is well-suited for short-term runoff characterization. However, traditional experimental approaches have certain limitations: the findings are challenging to extrapolate to larger watersheds, and the experiments are costly, making it difficult to conduct long-term large-scale observations. Statistical analysis is another commonly used method, which mainly explores the interaction between land use and runoff by analyzing their statistical relationship [16]. This method is relatively simple and can reveal macroscopic correlations, but it is difficult to capture complex hydrological processes. Statistical analysis relies on historical data, which makes it difficult to reflect the cause-and-effect relationship of land use changes and predict future trends. The application of hydrological models enables the identification and quantification of the impact of LUCC on runoff, as distributed hydrological models continue to develop and mature [17]. Although distributed models have high spatial resolution and strong predictive capabilities, they also have certain limitations. For example, MIKE SHE and HSPF have high requirements for input data, especially accurate data; in addition, the operation of the model requires high computational power, which may result in high resource costs [18,19,20]. The SWAT model, recognized for its robust ability to comprehensively simulate hydrological processes within a watershed, has been widely adopted across various research fields, it is particularly effective at capturing long-term runoff trends in large watersheds. Additionally, the SWAT model can accurately simulate runoff responses under different land use scenarios, helping to elucidate the independent contributions of various land use types to runoff changes and enabling analysis of hydrological responses at multiple scales. Its relatively low input data requirements allow for modeling with readily accessible public datasets, significantly reducing data collection and preprocessing costs, thus enhancing the efficiency of model construction [21,22]. Memarian analyzed the effects of land use and cover changes on water flow and sediment load in the Hulu Langat watershed using the SWAT model [23]. The impacts of climate change and LUCC on runoff in the Upper Luan River Basin were assessed by Yang using SWAT modeling and CA-Markov prediction [24]. The impacts of LUCC on the hydrological elements in the Wei River Basin between 1980 and 2010 were simulated using SWAT modeling by Hu et al. [25].

Water resources and watershed environment in Northeast China play a pivotal role in economic development and ecological protection [26]. As an important grain production base in China, land use patterns in the Northeast have changed significantly over the past decade [27]. The 1980s witnessed significant agricultural expansion in the Northeast region, particularly in the Sanjiang Plain and other surrounding areas [28]. Since 2000, the government has implemented a series of ecological restoration policies aimed at mitigating soil erosion, enhancing forest coverage, and rehabilitating natural ecosystems [29,30]. Although some studies have been conducted to explore the impacts of land use change on hydrological processes in some watersheds in the Northeast Black Soil Region, China, e.g., Liu et al. investigated the spatial and temporal characteristics and environmental effects of LUCC in the Sanjiang Plain [31], there remains a gap in understanding the impacts of LUCC on runoff at the watershed scale across the broader region. Unlike earlier research focusing solely on current land use status, this study employs the SWAT model to invert and simulate land use data for five time periods: 1980, 1990, 2000, 2010, and 2020. By establishing this multi-period scenario, the long-term influence of land use alterations on runoff in diverse periods can be systematically analyzed. This study not only inspects the overall runoff response of large-scale watersheds. but also introduces a multi-scale analysis approach to capture the variances in the hydrological response of different land use types, such as farmland, forestland, and wetlands, at both the local and overall levels. The analysis of multi-scale coupling effects discloses the impact of local land use changes on regional hydrological processes, thereby compensating for the deficiencies in existing studies in extrapolating scales. Additionally, the designed controlled experiment quantitatively evaluated the independent contribution of land use type to runoff by adjusting land use types while keeping climatic conditions and geological background fixed, which not only enhanced the scientific nature and credibility of the simulation results, but also more precisely defined the mechanism of land use change in hydrological processes.

This study aims to establish historical inversion scenarios through the SWAT model and, based on the runoff simulation results under different scenarios, to explore the following issues: (1) analyze the characteristics of the dynamic changes in land use types in the Northeast Black Soil Region of China over a 40-year period; (2) assess the trends in annual-scale runoff changes and the intra-annual distribution characteristics of the representative watersheds in the Northeast Black Soil Region of China under the conditions of land use in different periods of time; and (3) identify the key factors affecting runoff changes and explore the impact of land use changes on runoff patterns in watersheds.

2. Materials and Methods

2.1. Study Area

The Northeast Black Soil Region of China (38°43′ N–58°30′ N, 115°30′ E–135°30′ E) administratively includes the provinces of Heilongjiang, Jilin, and Liaoning, and the four eastern leagues of the Inner Mongolia Autonomous Region, covering a total area of about 1,241,400 square kilometers (as shown in Figure 1). The region exhibits a temperate continental monsoon climate, characterized by a gradual decline in both temperature and precipitation from south to north, accompanied by pronounced seasonal variations. High temperatures and precipitation are predominantly concentrated between June and September, with climatic zones transitioning from humid to semi-humid and finally to semi-arid from east to west. The average annual temperature ranges from −4 °C to 12.5 °C, while the average annual precipitation varies between approximately 250 mm and 1100 mm [32]. Soil types are diverse and include mainly Luvic Chernozems, Calcaric Chernozem, and so on.

The Northeast Black Soil Region of China is characterized by numerous watersheds, with the Songhua River and the Liao River serving as the two principal water systems. The Songhua River (41°42′–51°38′ N, 119°52′–132°31′ E) encompasses a watershed area of 55.68 km² and has two primary sources located to the north and south. The southern source of the west-flowing Songhua River originates from Tianchi in Changbai Mountain, with a total length of 958 km; the northern source, originating from Yilehuli Mountain—a tributary of Daxing’an Mountain—has a total length of 1370 km and is recognized as the largest tributary of the Songhua River. These two rivers converge at Sanqiaohe Town in Jilin Province before flowing northeast to Tongjiang City in Jiamusi, where they discharge into the Heilongjiang River. The main stream of the Songhua River extends for a total length of 939 km [33]. The Liao River, encompassing an area of 21.9 km², originates from Guangtou Mountain in the Qilautu Range of Pingquan City, Hebei Province, and has a total length of 143 km [34].

The Northeast Black Soil Region is not only a significant agricultural zone renowned for its production of corn, soybeans, and rice, but it also boasts extensive forest resources, such as the pristine forests of the Changbai Mountain [35].

2.2. Methodology

2.2.1. Data Sources

The digital elevation data were derived from resampling the Shuttle Radar Topography Mission (SRTM) dataset, achieving a spatial resolution of 500 m. The soil data were sourced from the Harmonized World Soil Database (HWSD), collaboratively established by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA). The Chinese regional data were based on the “2nd National Soil Survey” dataset provided by the Institute of Soil Research of the Chinese Academy of Sciences (ISSCAS) at a scale of 1:1 million. The hydrological properties of the soil were calculated using Soil Analysis Software (SPAW6.02.75). The land use data were obtained from the Resources and Environmental Science Data Center with a resolution of 30 m [36]. The meteorological data were obtained from the daily values of 205 meteorological stations in Northeast Black Soil Region of China from 1961 to 2019 provided by the China Meteorological Data Network (http://data.cma.cn/) and the National Tibetan Plateau Data Center (http://bdc.casnw.net/) [37]. Runoff data from the Fulaerji, Dalai, Jiamusi, Gaolichengzi, Hanyangtun, and Linghai hydrological stations were sourced from the China Water Resources Bulletin and utilized for the calibration and validation of the hydrological model.

2.2.2. Land Use Transfer Matrix

The land use transfer matrix serves as a tool for describing and analyzing changes among different land use types, representing an application of the Markov model in land use change. It facilitates the representation of transitions between land use types across different periods and enables the analysis of trends in land use [38]. It takes the form of

M_{i j} = [\begin{matrix} M_{11} & M_{12} & \dots & M_{1 n} \\ M_{21} & M_{22} & \dots & M_{2 n} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ M_{n 1} & M_{n 2} & \dots & M_{n n} \end{matrix}]

(1)

where M_ij denotes the area at the beginning of the time period where the i land use type is transformed to the j land type at the end of the time period. Utilizing the transfer matrix, it is possible to calculate the proportion P_ij that represents the transformation from land use type i at the beginning of the time period to land use type j at its conclusion.

P_{i j} = 100 \times M_{i j} / \sum_{j = 1}^{n} M_{i j}

(2)

2.2.3. SWAT Model

SWAT (Soil and Water Assessment Tool) is a semi-distributed hydrological model developed by the Agricultural Research Service (ARS) of the U.S. Department of Agriculture (USDA). It has been extensively utilized in watershed hydrological simulations and various studies, allowing for the simulation of land use change effects on watershed hydrological processes through modifications to input land use data [39,40,41]. The fundamental equations governing its terrestrial water balance simulation are as follows:

S W_{t} = S W_{0} + \sum_{i = 1}^{t} (P_{d a y} - Q_{s u r f} - E_{a} - W_{s e e p} - Q_{g w})

(3)

S W_{t}

represents the final soil water content (mm);

S W_{0}

denotes the initial soil water content (mm); t indicates the time step (d);

P_{d a y}

refers to the precipitation amount on day

i

(mm);

Q_{s u r f}

signifies the surface runoff volume on day

i

(mm);

E_{a}

represents evapotranspiration for day

i

(mm);

W_{s e e p}

is the soil infiltration and lateral flow on day (mm); and

Q_{g w}

quantifies subsurface runoff on day

i

(mm).

In this paper, WYLD (total water entering the main channel from the sub-basin during the simulation time step) in the SWAT model output is selected to characterize the watershed runoff volume, and its fundamental principle is expressed by the following equation:

W Y L D = S U R Q + L A T Q + G W Q - T L O S S - P O N D I N T E R C E P T I O N

(4)

S U R Q

is the contribution of surface runoff to the total runoff from the main channel during the simulation time step (mm);

L A T Q

is the contribution (mm) of lateral flow to the main channel runoff within the simulation time step;

G W Q

is the contribution of subsurface runoff to the total runoff of the main channel within the simulation time step (mm); and

T L O S S

is the water loss (mm) transported by the tributaries through the streambed.

Model Construction

Initially, soil and meteorological data were pre-processed to create the necessary soil and meteorological databases for model operation. The land use data were reclassified into eight primary categories: paddy fields, dry fields, forest lands, pastureland, watersheds, construction lands, unutilized lands, and wetlands; the 2020 land use data were selected for model establishment. Furthermore, the DEM (Digital Elevation Model) data, land use data, and soil data were harmonized to ensure consistency in both projection coordinate systems and geographic coordinate systems.

In this study, the river network was extracted from the processed DEM data, with a catchment area threshold set at 600 km²; this process ultimately divided the Northeast Black Soil Region into 76 sub-basins while incorporating representative large reservoirs within these basins (e.g., Baishan Reservoir). Hydrological response units (HRUs) within each sub-basin were established based on land use data, soil characteristics, and slope parameters, with thresholds set at 10% for all three criteria. Meteorological input was configured with a warm-up period from 1980 to 1984 to facilitate initial model calibration.

Model Evaluation

The SWAT model encompasses a multitude of parameters related to soil, climate, and hydrological processes. In this study, sensitivity analysis, parameter calibration, and validation of the model simulation results were conducted using SWAT-CUP. Employing the global parameter sensitivity analysis method (LH-OAT), key factors influencing model outcomes were identified [42], leading to the selection of 21 highly sensitive parameters (Table 1) for calibrating hydrological stations within the basin. The principle underlying this calibration process involves sequentially adjusting upstream, midstream, and downstream hydrological stations.

In this study, the coefficient of determination (R²) and Nash–Sutcliffe Efficiency (NSE) were employed as evaluation criteria for the simulation results [43]. R² reflects the degree of correlation between simulated and observed runoff, while Nash–Sutcliffe Efficiency serves as a widely used statistical index in hydrological model performance assessment to quantify the fit between simulated and observed values. The closer these values approach 1, the more accurate the model simulation results are. The formulas for R² and NSE are as follows:

R^{2} = \frac{{[\sum_{i = 1}^{n} (Q_{i} - {\bar{Q}}_{i}) (Q_{i}^{'} - {\bar{Q}}_{i}^{'})]}^{2}}{\sum_{i = 1}^{n} {(Q_{i} - {\bar{Q}}_{i})}^{2} \sum_{i = 1}^{n} {(Q_{i}^{'} - {\bar{Q}}_{i}^{'})}^{2}}

(5)

N E S = 1 - \frac{\sum_{i = 1}^{n} {(Q_{i} - Q_{i}^{'})}^{2}}{\sum_{i = 1}^{n} {(Q_{i} - {\bar{Q}}_{i})}^{2}}

(6)

where

Q_{i}

represents the measured runoff volume, and

Q_{i}^{’}

represents the simulated runoff volume;

{\bar{Q}}_{i}

represents the average measured runoff volume, and

{\bar{Q}}_{i}^{’}

represents the average simulated runoff volume;

i

is the length of the simulated sequence, and n is the total number of observations.

When R² > 0.6 and NSE > 0.5, the model simulation results can be considered qualified [44,45]. The SWAT model result evaluation is provided in Supplementary Materials (Word File S1).

Scenario Setting

In this study, an historical inversion method was employed to analyze the impacts of various historical subsurface conditions on hydrological processes within the watershed by treating past subsurface conditions as current land use scenarios and integrating them with contemporary climatic conditions, soil properties, and human activities (the land use types for these scenarios are illustrated in Figure 2). This approach allows for a more comprehensive alignment with the actual circumstances of the watershed, accurately reflecting hydrological responses triggered by changes in subsurface conditions and elucidating the mechanisms through which Land Use/Cover Change (LUCC) influences runoff [46]. The scenario settings are detailed in Table 2.

3. Results

3.1. Land Use Change Characteristics

3.1.1. Characterization of Changes in Land Use Dynamics

As shown in Table 3, the land use types of major watersheds in the Northeast Black Soil Region underwent significant changes between 1980 and 2020. The areas designated for cultivated and construction land increased annually, with paddy fields and drylands expanding by 21,459 km² and 43,794 km², respectively, while construction land expanded by an additional 10,714 km². In contrast, pastureland witnessed a significant decline of 65,594 km² over this four-decade span. Forest land exhibited notable fluctuations; it decreased by 3333 km² by 2020, a trend closely linked to population growth and escalating food demand. Nevertheless, policies enacted post-2000 aimed at converting farmland back to forest have contributed positively to the recovery of forest resources. Water bodies and unused land remained relatively stable; however, there was a marked reduction in water area between 2010 and 2020. Furthermore, wetland area diminished by 18,556 km² from 1980 to 2010, but subsequently rebounded with an increase of 11,088 km² due to restoration initiatives.

3.1.2. Characteristics of Land Use Type Shifts

By utilizing the ArcGIS tool to compute the four-phase land use transfer matrix for the period spanning from 1980 to 2020, a dynamic change map can be generated (Figure 3). The findings reveal that between 1980 and 1990, approximately 14.7% of paddy fields underwent conversion into dry fields (5212 km²), with dry fields predominantly transitioning into pastureland (8676 km²) and paddy fields (7031 km²). Moreover, forests, pastureland, and wetlands were primarily transformed into dry fields (28,571 km²). During the period from 1990 to 2000, around 15.87% of paddy fields experienced conversion into dry fields (6417 km²), while forests and pastureland were mainly converted into dry fields (31,910 km²). From 2000 to 2010, approximately 26.87% of paddy fields (6417 km²) underwent conversion into dry fields, while forests and pastureland were predominantly transformed into dry fields (31,910 km²). Additionally, pastureland experienced significant conversion into dry fields (31,910 km²). During the same period, around 26.29% of paddy fields (12,314 km²) were converted into dry fields, with approximately 19% of the latter being further converted into other types of land (62,626 km²). Furthermore, about 36% of water bodies underwent conversion into different land types, particularly wetlands, which were mainly transformed into dry fields, forests, and pastureland (16,031 km²). Between 2010 and 2020, roughly 25.43% of water bodies transitioned to become dry fields (12,737 km²), while about 29% of existing dry field areas expanded to include paddy fields and forests, among others (97,582 km²). Moreover, a notable proportion, equaling to approximately 8.31% of forested regions, was converted into dry field areas as well (40,702 km²), whereas changes in both pastureland and wetlands exhibited similar significance.

3.2. Impacts on Runoff Under Land Use Change Scenarios

3.2.1. Characteristics of Inter-Annual Variability of Hydrological Elements Under Different Land Use Scenarios in the Northeast Black Soil Region

The impact of LUCC on hydrological processes in a watershed is typically observed through changes in various hydrological elements. This study compares the simulation results of four key hydrological components, namely annual mean evapotranspiration, surface runoff, subsurface runoff, and water yield within the watershed (as shown in Table 4). These comparisons are based on a calibrated model under different historical land use scenarios. Additionally, the influence of different land use types on hydrological processes within each scenario is analyzed.

According to the simulation results of evapotranspiration (ET), the changes in ET within the northeast basin under different scenarios are relatively negligible, except for scenario S2, which exhibits a clear and gradual decrease trend. Amongst all scenarios, the baseline scenario S0 demonstrates the lowest annual average ET value (430.5 mm), while the remaining four scenarios show annual average ET values that range from 0.16% to 2.48% higher than that of S0, with scenario S1 having the highest annual average ET value (441.17 mm). In general, it can be inferred that land use changes, such as cropland expansion and forest and pasture degradation, may contribute to a reduction in annual ET within this watershed.

In contrast, the average annual water yield exhibited a gradual upward trend. The baseline scenario S0 simulated the lowest average annual water yield (96.26 mm), while the remaining four scenarios showed average annual water yields 0.52–9.42% lower than that of S0. This could be attributed to reduced evapotranspiration resulting from the decrease in forested pastureland, leading to increased retention of water in the soil and subsequent conversion into runoff as water yield. Additionally, the expansion of cropland resulted in greater surface runoff and consequently contributed to an increase in overall water yield [47].

The main components of water yield are surface runoff and subsurface runoff. The simulation results show that the S0 scenario has the highest surface runoff (39.81 mm), while the S2 scenario has the highest subsurface runoff (47.84 mm). It is possible that under the S3 scenario, soil types in some areas are already highly permeable, and due to agricultural activities such as tilling, the top layer of the soil may be loosened, which reduces soil compactness, which makes it easier for precipitation to infiltrate into the subsurface and replenish subsurface runoff [48,49]; simultaneously, the reduction in forest pasture area leads to a decrease in evapotranspiration due to the substantial water absorption and consumption by trees and grasses. Consequently, this results in enhanced precipitation infiltration into the soil, thereby augmenting groundwater recharge and subsurface runoff, which aligns with the findings of Owuor’s study [50].

3.2.2. Characteristics of Inter-Annual Changes in Runoff in the Songhua River Basin Under Different Land Use Scenarios

The Songhua River is the largest river in Northeast China. Table 5 shows the changes in land use types at different times. These changes in land use type have greatly affected the runoff patterns in the watershed. The Jiamusi station, located near the outlet of the Songhua River watershed, is located in the sub-watershed shown in Figure 4. Under scenario S1, the annual runoff simulation values are generally lower than those of the other scenarios, with scenario S0 always having higher annual runoff levels; this is particularly evident in 1981, when the annual runoff value of scenario S0 is about 21% higher than that of scenario S1. Except for some slight changes in some years in scenario S2, the annual runoff in the other scenarios is from 1% to 21% higher than that in scenario S1. In addition, although the overall runoff in scenario S1 is lower, it is more stable, with minimal inter-annual fluctuations, while the runoff in scenario S0 is always higher, especially in years with concentrated precipitation; this trend persists throughout the entire 40-year period.

The water yield in the study area exhibited distinct seasonal variations throughout the year (Figure 5). The simulation results of the baseline scenario (S0) revealed that it was lowest during winter, gradually increased in spring, reached its peak in summer, and then gradually declined in fall. Notably, the water yield was significantly higher in August (23.38 mm) compared to other months, while February had the lowest water yield of the year. Analysis of the simulation results for scenarios S1–S4 indicated considerable variability in multi-year average water yields during spring. In March, compared to baseline scenario S0, scenarios S1 and S3 showed an increase of 55.21% and 65.98%, respectively; however, scenario S2 experienced a decrease of 36.12%. In April, both scenarios S1 and S3 witnessed a decrease of 43.41% and 42.08%, respectively, compared to S0; by May, these decreases were recorded as 41.34% and 37.45%, again for scenarios S1 and S3, respectively, when compared to baseline conditions.

During the summer months—particularly July and August—a progressive increase in water yield was observed on a monthly basis with respect to both scenarios considered; specifically, comparing monthly multi-year average water yields between scenario S4 and scenario S3 resulted in increases of approximately 7% and 4%, respectively.

3.3. Characteristics of Spatial Distribution of Runoff Under Different Land Use Scenarios in the Northeast Black Soil Region

3.3.1. Current Status of Runoff Distribution Characteristics in the Northeast Black Soil Region

The spatial distribution of runoff in the Northeast Black Soil Region is characterized by a clear pattern; in general, the flow production in the southeast is higher, and the main land use types are paddy fields, dry fields, and forests, while the runoff in the west is relatively low, and the main land use types are dry fields and pasture land. The average annual runoff coefficients of each sub-basin are shown in Figure 6. Areas with higher runoff are generally in the humid and semi-humid zones, with more abundant precipitation and more mountainous and hilly terrain in the southeast, with runoff coefficients ranging from 0.3 to 0.5; areas with lower runoff tend to belong to semi-arid zones or higher latitude zones, with lower precipitation and flatter terrain, with runoff coefficients ranging from 0 to 0.1 only.

Precipitation serves as the primary source of runoff. This study examines the proportion of precipitation attributed to six hydrological variables (WYLD, SW, PERC, SURQ, GWQ, and LATQ) in each sub-basin based on the known spatial distribution characteristics of runoff in the Northeast Black Soil Region (Figure 7). For instance, in the Toudao and Erdao watersheds, located within the Changbai Mountain region, more lateral runoff occurs on the slopes of the mountains, and LATQ dominates precipitation. Conversely, in low-precipitation and high-evaporation plains within the study area’s watersheds, precipitation is predominantly retained in soil, with a larger contribution from SW. Furthermore, PERC holds a significant proportion in watersheds where forestland constitutes the primary land use type.

3.3.2. Characteristics of Spatial Distribution of Runoff Under Different Land Use Scenarios in the Northeast Black Soil Region

The results presented in Figure 8 demonstrate that the water yield of the sub-basins exhibit a comparable pattern across the five scenarios during each respective season; however, certain sub-basins exhibit more pronounced disparities, particularly during spring and summer.

The water yield of the Huma River and Emuer River basins in the north, as well as the Songhua River tributaries and Tumen River tributaries in the southeast, followed a similar trend in spring and summer. In scenario S2, compared to scenario S1, there was a decrease of 33 km² and 9 km² in wetlands and pasture, respectively, in the Huma River and Emuer River basins in the north, while forested land increased by 42 km². In scenario S3, compared to scenario S2, there was a decrease of 402 km² in pasture, while forested land and wetlands increased by 299 km² and 103 km², respectively, resulting in a decrease in spring and summer water yield by 6–15 mm. Finally, comparing scenario S4 to S3 showed a decrease in forest land by 1946 km² but an increase in pasture by 1878 km² and wetland by 68 km², leading to an increase in water yield by 6–15 mm.

In comparison to scenario S1, scenario S2 exhibited an increase in the area of paddy fields by 1986 km² and dry fields by 4408 km² within the tributary basins of the Songhua, Tumen, and Yalu rivers. Consequently, this led to a corresponding rise in water yield ranging from 6 to 18 mm. The water yield in the Tumen River (sub54, sub55, and sub57) and Pushi River (sub72) basins increased by 15–18 mm. The area of dry land in the Toudao River Basin increased by 301 km², while decreasing by 37 km² in the Pushi River Basin with a corresponding increase in forest land by 37 km². In comparison to S2, scenario S3 shows an increase of 208 km² and 101 km² in the area of paddy fields and dry fields, respectively, within the southeastern watershed, along with a decrease in forest land by 309 km², resulting in a decrease in water yield by 6–18 mm. Compared to the S3 scenario, the S4 scenario shows a decrease of 319 km² and 157 km² in the area of dry fields, respectively, while the forest area increased by 476 km². The water yield in the basins of the upper reaches of the Toudao River (sub55), Hun River (sub63), and Pushi River (sub72) increased most significantly.

In summer, compared to scenario S1, the water yield of sub-basins such as the Xun River, Muling River, central Liao River, and main stream of Songhua River increased by 6–15 mm in scenario S2. Changes in land use within these areas include an increase in dry fields or paddy fields, while forest land, pastureland, and wetlands decreased. Some basins experienced an increase in unused land. The water yield of tributaries within the Nen River and Erdao River basins decreased by 6 mm and 8 mm, respectively. In the former scenario, there was an increase in forest land and wetlands, while pasture degraded. In the latter scenario, land use changes were minimal and were predominantly characterized by an expansion of forest land. Compared to the S2 scenario, the S3 scenario shows an increase in water yield ranging from 6 to 15 mm in the middle and upper reaches of the Nen River Basin (except sub9), with a notable rise of 22 mm observed in sub11. Land use changes are marked by significant growth in dry fields and a decline in forest land, pasture, and wetlands. Specifically, for sub11, there was an expansion in fields by 1034 km² and pasture by 734 km², respectively, accompanied by a reduction in forest land by 535 km² and wetlands by 233 km², respectively. However, within sub11 itself, there was an increase in forest land by 50 km² and wetlands by 44 km², while pasture decreased by 94 km². Additionally, some basins along the Songhua River experience a decrease in water yield such as sub-basin 40 which sees a decline of 7 mm due to reduced unused land area coupled with increased pasture and dry fields. The water yield of some basins along the Suifen River and Mudan River in the east decreased by 7 mm despite an increase in forest land and a decrease in cultivated land. The Huolin River Basin saw a significant increase in forest land, with 2156 km² of unused land being converted into forests while pasture decreased. In contrast, both agricultural and forest lands decreased, while construction land increased by 2976 km² in the Hun River basin. In scenario S0, the conversion of forest land and wetlands in the upper reaches of the Nen River into dry fields resulted in a 10 mm increase in water yield compared to scenario S4. In the Nemoer River basin, there was a decrease of 596 km² in forest land, while dry fields and wetlands increased by 155 km² and 441 km², respectively, leading to a 7 mm increase in water yield. All unused land in the Huolin River Basin was reclaimed as dry land and pasture, causing a decrease of 7 mm in water yield. Lastly, an increase of 66 km² in pasture occurred in the Daling River Basin, which resulted in an 11 mm increase in water yield.

In autumn, precipitation decreases, and at the same time, many plants enter a dormant period; transpiration by plants decreases and crops in farmlands are harvested, so the evaporation, interception, and utilization of water decreases. This results in a reduction in runoff differences between the different land use types in the study area. However, the Hun River Basin has a large variation in water yield, and its main land use type is forest land. Compared with scenario S1, the water yield in the Hun River Basin decreases by 9–19 mm. The dry land in this basin decreased by 83 km² and the forest land increased by 83 km². Compared with scenario S3, the water yield in scenario S4 increased by 9–18 mm, arable land increased by 22 km², and forest land decreased by 22 km². Due to the low winter temperatures in the Northeast Black Soil Region, much of the surface water is covered in ice, surface runoff is significantly reduced, and precipitation mainly occurs in the form of snow. Snow water almost does not form runoff before melting in the spring, so there is no significant change in the multi-year average winter runoff volume under the five scenarios (Figure 9).

4. Discussion

4.1. Impact of Land Use Type on Runoff

Wetlands have the ability to weaken the hydrological regulation of flood peaks [51]. A reduction in wetland area, especially during periods of concentrated snowmelt and precipitation, may result in higher peak flows of surface runoff [52]. This means that more water is accumulated in the river within a short period of time, potentially leading to an increased risk of flooding [53]. Furthermore, wetlands restored through artificial restoration after 2010 (e.g., the Nemur River Basin) are not as effective in hydrological regulation as natural wetlands. The increase in runoff volume due to the reduction in forested land and the increase in dry fields outweighs the regulatory effect provided by these restored wetlands [54].

Based on the simulation of inter-annual runoff variability in the Songhua River Basin and the spatial characterization of summer in each sub-basin, this study demonstrates a significant disparity in how dryland and paddy fields influence agricultural land runoff mechanisms. The augmentation of runoff yield resulting from an expansion of dry fields is particularly prominent during summer months. Increased dry field areas lead to intensified tillage activities, inducing soil compaction, elevated soil bulk weight and strength, reduced soil porosity, as well as modified soil hydraulic properties [55]. Consequently, a greater proportion of precipitation rapidly transforms into surface runoff. Furthermore, unlike woodlands and pastureland, which possess deeper root systems and a greater capacity to draw water [56,57], the shallow root systems of dryland crops are not effective at facilitating precipitation infiltration, potentially resulting in relatively higher runoff in dryland areas. Rice exhibits high sensitivity to water stress and unsaturated soil conditions [58]. To ensure normal growth and development, paddy fields demonstrate a strong ability to retain water. Even if rainfall or snowmelt occurs within the paddy field area during spring, most of the water is retained, thereby reducing the amount of surface runoff directly flowing into the stream channel. Paddy fields often require irrigation from the stream channel during spring [59], which may lead to lower spring yield flows in the watershed. Additionally, both paddy field crops and surfaces contribute significantly to evapotranspiration [60]. An increase in paddy field area results in a substantial rise, but also diminishes the volume of water flowing into the stream channel.

The impact of forests on runoff is often intricate. Generally, precipitation is intercepted by woodlands through the canopy, foliage, and vegetative cover, which reduces direct contact between rainfall and the surface. Woodlands typically have good soil structure that enables effective infiltration of precipitation. This, combined with high evapotranspiration rates, may result in a reduction in direct surface runoff [61,62]. By comparing the annual mean values of hydrological elements between scenarios S1 and S0, it can be observed that scenario S1—which has the largest proportion of original woodland—exhibits higher evapotranspiration but lower water yield. This suggests that a decrease in woodland area leads to a decrease in intercepted water amount and an increase in surface runoff [63]. The water yield of the S4 scenario exceeds that of the S3 scenario, despite a substantial increase in woodland area. This could be attributed to the conversion of predominantly dry fields and pastureland into forested areas between 2000 and 2010. Moreover, certain portions of forest land were also converted to other land use types during this period. The post-2000 policy project aimed at returning farmland to forests primarily focused on afforestation efforts in hilly regions with barren mountains and wastelands [64]. During the initial stages of afforestation, soil compaction resulting from deforestation activities may lead to reduced soil infiltration capacity. Additionally, surface runoff is more likely to occur on steeper slopes [65,66,67], thereby contributing to an increase in surface runoff. Conversely, the deeper root system associated with forested land facilitates greater precipitation recharge into groundwater through roots [68], potentially leading to an increase in groundwater quantity (GWQ). Over time, increased woodland cover may gradually enhance baseflow within stream channels during later stages [69], particularly when soils are drying out in late spring. However, in basins such as the Hun River Basin, where upstream and midstream areas exhibit higher slopes, a minor reduction in woodland can still result in a significant increase in runoff. This finding aligns with Cheng’s research conducted on Qu River Basin [70]. The Pushi River Basin, on the other hand, is in close proximity to the ocean and experiences ample precipitation. Additionally, it is situated at a relatively lower latitude within the study area, resulting in higher temperatures. These conditions may contribute to an increase in surface runoff even when there is an expansion in forested land during spring thaw or concentrated rainfall events [71].

The retention capacity of pastureland was poor compared to that of woodland in our study, but changes in unutilized land (e.g., the Hollin River Basin) and built-up land (e.g., the Hun River Basin) could also significantly affect water yield, especially during summer. This may be due to the fact that unutilized land is mostly bare, wasteland, or abandoned without sufficient vegetation to absorb and retain precipitation, resulting in weak infiltration capacity and evapotranspiration [72,73]. Additionally, the impermeable surface of built-up land almost completely prevents water from infiltrating into the subsurface. As a result, precipitation forms surface runoff directly into rivers, particularly during heavy and frequent rainfall in summer months [74].

Additionally, although land use types, such as those in sub15 and 56, remain largely unchanged, they may still be influenced by hydrologic connectivity. Changes in the land use and land cover (LUCC) of adjacent watersheds can impact the watershed through the river system, leading to a decrease in their water yield [75,76].

4.2. The Impact of Land Use Change on Runoff and Flood Risk and a Discussion of Model Applications

This study quantifies the changes in various land use types at different times through a land use transfer matrix, and uses multi-year average runoff values to smooth out the impact of short-term climate fluctuations on runoff changes so as to make the trend of runoff changes more stable and representative [77]. This method not only excels in data management, but also improves the credibility of the model simulation results. Compared with the traditional year-by-year analysis method, the combination of multi-year average values and transfer matrices can better capture long-term trends and provide a more solid basis for in-depth analysis of hydrological processes. In addition, the SWAT model was used to simulate changes in runoff under different land use scenarios, providing a scientific basis for flood risk assessment. The results show that increasing wetland and forest cover can significantly reduce peak runoff, especially during extreme rainfall events. Wetlands play a strong water regulating role, effectively mitigating the impact of floods. In areas with higher wetland and forest cover, the frequency and intensity of floods are significantly reduced. This shows that optimizing the land use structure can not only effectively reduce flood risk, but also provide valuable decision-making support for water resource management [50,78]. The application of this model has broad potential, especially in other countries and regions with similar climatic and topographic conditions and land use patterns, to help water resource management departments identify flood risk areas and optimize land use management strategies. However, when the SWAT model is applied in new countries and regions, basic geographic data, climatic data, soil types, and land use data for the target area need to be collected. In addition, in order to ensure that the model accurately reflects the hydrological processes in the new area, sufficient calibration and verification must be carried out to improve the accuracy of the simulation. By calibrating key parameters (such as soil permeability and vegetation coefficients), the model will be able to more accurately predict flood risk and enhance its adaptability to different land use scenarios [79].

In order to achieve rational land resource utilization in the Northeast Black Soil Region, it is crucial to strike a balance between dry fields and paddy fields [80]. To accomplish this, modern water-saving irrigation techniques should be applied to enhance water resource utilization efficiency while avoiding excessive expansion of paddy fields and minimizing negative impacts on water resources [81]. Furthermore, optimizing land use structure based on regional hydrological characteristics, topography, and climatic conditions is indispensable for mitigating the impact of irrational land use while maintaining equilibrium with paddy fields [82]. Ultimately, integrated watershed management should foster coordination and harmonization between upstream and downstream regions regarding land use and water resource allocation [83]. The overarching goal is to promote the rational utilization of land and water resources in the Northeast Black Soil Region while fostering the sound development of the regional ecological environment.

4.3. Inadequate Research

There are also some limitations to this study. Due to the large size of the study area, the threshold level of land use was set at 10 percent when dividing the HRUs. Only land use types that occupy more than 10 percent of the area were identified and included in the analysis within the HRUs. Land use types with less than 10 percent of the area were ignored or merged into other major land use types, which may result in overlooking smaller but potentially important land use types (e.g., wetlands, built-up land, or unused land). Although these small land use types do not account for a large percentage of the total area, they can have a significant impact on local hydrological processes. Therefore, ignoring these details may decrease model accuracy. In future studies, it is recommended to lower the threshold level or adopt finer delineation criteria in key areas to enhance the model’s accuracy and the credibility of the results.

In addition, although representative hydrologic stations in the study area were used for rate setting and calibration in this study, it is possible that the model’s simulation results may deviate from the actual situation in some unmonitored watershed domains, leading to potential inaccuracies in runoff simulation.

5. Conclusions

In this study, historical inversion scenarios were established using the SWAT model to simulate the impacts of land use changes on watershed runoff in the Northeast Black Soil Region of China during different periods. By comparing the temporal and spatial characteristics of the multi-year average runoff under each scenario, the following conclusions were summarized:

(1) Between 1980 and 2020, there were significant changes in land use within the watersheds of the Northeast Black Soil region. The area of agricultural land increased by 65,253 km², while both pastureland and forest land decreased by 65,554 km² and 3333 km², respectively. Additionally, the area of wetland exhibited a decreasing trend followed by an increasing trend.

(2) Changes in hydrological elements within the watershed were analyzed under various land use scenarios using the model comparison. The findings indicate a distinct trend in declining evapotranspiration and increasing water yield in the watershed associated with cropland expansion and the degradation of forest and pasturelands.

(3) The simulation of the Songhua River Basin indicated that the lowest runoff occurred in 1980, while the maximum was observed in 2020, with a significant increase in water output during July and August. Land use changes had a notable impact on runoff patterns in both northern and southeastern regions of the study area. Specifically, dryland expansion led to increased summer runoff, paddy field conversion reduced spring runoff, forest growth enhanced precipitation interception capacity, but new forest areas may have insufficient permeability, resulting in increased runoff. The decrease in wetlands heightened flood risk, while unused land and construction sites significantly amplified summer runoff due to poor permeability.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16233456/s1, File S1: SWAT Model result evaluation. This file contains detailed calibration and validation results for the SWAT-CUP model applied to six hydrological stations from 1980 to 2019, including statistical performance metrics (R² and NSE) for each station.

Author Contributions

Y.H. and P.Q. conceived the idea of the study and wrote the manuscript; Y.H. and C.D. carried out data collection and analysis; P.Q. and C.D. contributed valuable analysis and manuscript review. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Outstanding Young Scientist Project in Jilin Province (20230508099RC), Major Science and Technology Projects in Jilin Province (20230303007SF), National Natural Science Foundation of China (42371037).

Data Availability Statement

Some or all of the data that support the findings of this study are available from the corresponding authors upon reasonable request.

Conflicts of Interest

The authors have no competing interests to declare that are relevant to the content of this article.

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Figure 1. Location and elevations of the River Basin in Northeast Black Soil Region of China, gauging stations, weather stations, rivers, and the study area.

Figure 2. Land use in 1980, 1990, 2000, 2010, and 2020 in River Basin in Northeast Black Soil Region, China.

Figure 3. Land use transfer matrix from 1980 to 2020. The lines illustrate the conversion between different land use types across time periods, highlighting the dynamics of land use change.

Figure 4. Annual runoff at the downstream outlet of the Songhua River under different scenarios (S0, S1, S2, S3, and S4).

Figure 5. Monthly distribution of multi-year average water yield in the River Basin of Northeast Black Soil Region, China, under different scenarios (S0, S1, S2, S3, and S4).

Figure 6. Runoff coefficient distribution in River Basin in Northeast Black Soil Region, China.

Figure 7. The six hydrological variables expressed as a proportion of the mean annual precipitation are: groundwater contribution to streamflow (GWQ), lateral flow contribution to streamflow (LATQ), percolation beyond the root zone (PERC), surface runoff generated within the watershed (SURQ), and soil water (SW). The six hydrological variables expressed as a proportion of the mean annual precipitation are: groundwater contribution to streamflow (GWQ), lateral flow contribution to streamflow (LATQ), percolation beyond the root zone (PERC), surface runoff generated within the watershed (SURQ), soil water content (SW), and water yield (WYLD).

Figure 8. Spatial distribution of seasonal water yield under different scenarios (S0, S1, S2, S3, and S4). Each subfigure (a–d) represents the water yield for spring, summer, autumn, and winter.

Figure 9. Spatial distribution of absolute changes in seasonal water yield under different scenarios (S0, S1, S2, S3, and S4).

Table 1. Description of parameters in SWAT-CUP for calibration.

Parameter	Physical Significance	Range	Hydrologic Station
Parameter	Physical Significance	Range	Fulaerji	Dalai	Gaolichengzi	Hanyangtun	Jiamusi	Linghai
R_CN2	SCS runoff curve number f	(−0.5, 0.5)	0.26	0	−0.27	0.42		0.05
V_GW_REVAP	Groundwater revap coefficient	(0.02, 0.2)	0.15	0.02	0.02	0.09	0.02	0.02
V_GW_DELAY	Groundwater delay (days)	(0, 500)	31	31	31	239	31	31
V_RCHRG_DP	Deep aquifer percolation fraction	(0, 1)	0.91	0.05	0.88	0.24	0.05	0.62
V_ALPHA_BF	Baseflow alpha factor (days)	(0, 1)	0.54	0.048	0.048	0.3	0.048	0.048
V_REVAPMN	Threshold depth of water in the shallow aquifer for revap to occur (mm)	(0, 500)	360	750	63.09	750	750	15.5
V_GWQMN	Threshold depth of water in the shallow aquifer required for return flow to occur (mm)	(0, 5000)	3923	1000	1899	1418	1000	1000
V_SLSUBBSN	Average slope length	(10, 150)	117.55	121.95	91.46	123.43	121.95	121.95
V_ESCO	Soil evaporation compensation factor	(0, 1)	0.69	0.95	0.98	0.95	0.95	0.07
V_CANMX	Maximum canopy storage	(0, 100)	3.78	0	3.63	0	0	7.3
R_HRU_SLP	Average slope steepness	(0, 1)	0	0	0	0.11	0	0
V_SLSOIL	Slope length for lateral subsurface flow	(0, 150)	75.66	0	2.97	58.62	0	95.55
V_EPCO	Plant uptake compensation factor	(0, 1)	0.89	1	1	0.5	1	0.82
V_OV_N	Manning’s n value for overland flow	(0.01, 30)	0.1	0.1	1.62	24.36	0.1	28.42
V_CH_N2	Manning’s ‘n’ value for the main channel	(0, 0.5)	0.25	0.014	0.014	0.014	0.014	0.014
V_CH_K2	Effective hydraulic conductivity in main channel alluvium	(0, 500)	51.37	0	0	274.97	0	0
V_ALPHA_BNK	Baseflow alpha factor for bank storage	(0, 1)	0.19	0	0	0	0	0
V_TIMP	Snowpack temperature lag factor	(0, 1)	0.32	0.32	0.32	0.32	0.32	0.32
V_SFTMP	Snowfall temperature	(−20, 20)	4.89	4.89	4.89	4.89	4.89	4.89
R_SOL_AWC	Available water capacity of the soil layer	(−1, 1)	0.7	0	0.75	0.93	0	0.73
R_SOL_K	Saturated hydraulic conductivity	(−0.5, 1)	0.56	0	0.29	0.98	0	0.78

Table 2. Scenario framework for assessing land use changes in runoff.

Scenario	Description
S0	Baseline scenario using 2020 land use data
S1	Land use restored to the 1980 status
S2	Land use restored to the 1990 status
S3	Land use restored to the 2000 status
S4	Land use restored to the 2010 status

Table 3. Percentage changes in various land use types from in the River Basin in Northeast Black Soil Region, China.

Type\Year	1980	1990	2000	2010	2020
Paddy field	3.00%	3.31%	3.83%	4.10%	4.76%
Dry farmland	24.43%	25.30%	27.39%	27.67%	28.01%
Forest land	39.85%	39.29%	38.17%	40.06%	39.59%
Pasture	18.50%	17.63%	16.56%	14.53%	13.14%
Water area	2.22%	2.37%	2.24%	2.22%	1.97%
Construction land	2.18%	2.37%	2.42%	2.74%	3.06%
Unused land	2.37%	2.53%	2.50%	2.76%	2.65%
Wetland	7.44%	7.20%	6.90%	5.92%	6.83%

Table 4. Changes in hydrologic variables under different land use scenarios in the River Basin in the Northeast Black Soil Region, China.

Scenario	Water Yield	Surface Runoff	Groundwater Runoff	Evapotranspiration
Scenario	(mm)	(mm)	(mm)	(mm)
S1	87.19	34.58	41.78	441.17
S2	93.60	36.85	44.65	435.11
S3	89.75	37.90	41.05	438.86
S4	95.76	39.16	45.00	431.19
S0	96.26	39.81	44.78	430.5

Table 5. Changes in the area of various land use types in the Songhua River Basin.

LUCC (km²)	S1	S2	S3	S4	S0
Paddy field	224,229	231,409	255,131	258,887	257,533
Dry farmland	3923	10,718	17,136	17,346	25,091
Forestland	301,937	286,632	270,932	287,024	282,690
Pasture	82,159	76,426	67,829	53,603	48,649
Water area	2366	2674	2503	2346	2123
Construction land	1256	1348	1467	1616	1997
Unused land	10,040	15,761	14,518	13,668	12,902
Wetland	37,051	37,995	33,446	28,471	31,981

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Hao, Y.; Qi, P.; Du, C. The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China. Water 2024, 16, 3456. https://doi.org/10.3390/w16233456

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Hao Y, Qi P, Du C. The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China. Water. 2024; 16(23):3456. https://doi.org/10.3390/w16233456

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Hao, Yonggang, Peng Qi, and Chong Du. 2024. "The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China" Water 16, no. 23: 3456. https://doi.org/10.3390/w16233456

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Hao, Y., Qi, P., & Du, C. (2024). The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China. Water, 16(23), 3456. https://doi.org/10.3390/w16233456

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The Response of Runoff to Land Use Change in the Northeastern Black Soil Region, China

Abstract

1. Introduction

2. Materials and Methods

2.1. Study Area

2.2. Methodology

2.2.1. Data Sources

2.2.2. Land Use Transfer Matrix

2.2.3. SWAT Model

Model Construction

Model Evaluation

Scenario Setting

3. Results

3.1. Land Use Change Characteristics

3.1.1. Characterization of Changes in Land Use Dynamics

3.1.2. Characteristics of Land Use Type Shifts

3.2. Impacts on Runoff Under Land Use Change Scenarios

3.2.1. Characteristics of Inter-Annual Variability of Hydrological Elements Under Different Land Use Scenarios in the Northeast Black Soil Region

3.2.2. Characteristics of Inter-Annual Changes in Runoff in the Songhua River Basin Under Different Land Use Scenarios

3.3. Characteristics of Spatial Distribution of Runoff Under Different Land Use Scenarios in the Northeast Black Soil Region

3.3.1. Current Status of Runoff Distribution Characteristics in the Northeast Black Soil Region

3.3.2. Characteristics of Spatial Distribution of Runoff Under Different Land Use Scenarios in the Northeast Black Soil Region

4. Discussion

4.1. Impact of Land Use Type on Runoff

4.2. The Impact of Land Use Change on Runoff and Flood Risk and a Discussion of Model Applications

4.3. Inadequate Research

5. Conclusions

Supplementary Materials

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

References

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