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Article

Simulation and Response of Runoff to Climate and Land-Use Changes in the Yanhe River Basin, Loess Plateau: A SWAT Model-Based Analysis

by
Kun Hou
*,
Jianhua Wang
and
Xiaoming Zhang
China Institute of Water Resources and Hydropower Research, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(7), 1042; https://doi.org/10.3390/w17071042
Submission received: 24 February 2025 / Revised: 29 March 2025 / Accepted: 30 March 2025 / Published: 2 April 2025
(This article belongs to the Section Water and Climate Change)
Browse Figures
Versions Notes

Abstract

:
Ecological restoration projects in the Loess Plateau have significantly altered the underlying surface, which has profoundly affected the regional water cycle. In the context of the ongoing climate change, quantitatively identifying the factors influencing runoff changes and simulating runoff responses to various land management policies are essential for achieving sustainable development in arid/semi-arid regions. Daily hydrological and meteorological data from 1981 to 2020 along with the SWAT model were employed to analyze the attribution of runoff changes in the Yanhe River basin and simulate runoff responses under different climate and land-use scenarios. The results show the following: (1) the improvement of the underlying surface conditions appeared to be the leading factor of runoff retention, with a contribution of 81.21%, while the influence of climate change on runoff was minimal; (2) woodland generally exhibited superior performance in retaining runoff compared to grassland under diverse climate conditions; (3) converting farmland on slopes between 15 and 25 degrees into woodland and farmland on slopes exceeding 25 degrees into grassland demonstrated to be a more effective approach to controlling soil erosion; (4) it is recommended that a balance between water resource utilization and the extent of afforestation should be considered concurrently in future ecological restoration.

1. Introduction

Changes in climate and land-use are decisive factors influencing hydrological processes [1,2]. Under the dual impact of global climate change and land-use alterations driven by human activities, the spatial distribution of global water resources and the water cycle have undergone significant transformations [3,4]. These changes profoundly influence the evolution of regional ecological patterns and social development [5]. Consequently, the response characteristics of runoff to climate and land-use changes have become a focal point in the study of water resources’ evolution [6,7].
Many scholars have conducted extensive research on the attribution of runoff changes in various large and medium-sized basins and consistently indicated that the combined effects of climate change and human activities were the primary drivers of these changes [8,9]. Additionally, researchers have quantified the influence of different factors on runoff changes using methods such as empirical statistical analysis [10], elasticity coefficient analysis [11,12], and hydrological modeling [13,14].
The empirical statistical method relies on extensive historical measured data, which makes it unsuitable for regions with limited hydrological data [15,16]. The elasticity coefficient method estimates the hydrological effects by altering individual factors in the model, which limits its effectiveness in explaining the mechanisms of the hydrological cycle. In contrast, the hydrological model method has become widely adopted due to its capability to account for spatiotemporal heterogeneity within watersheds and accurately describe the mechanisms of hydrological cycles [17,18].
With the rapid advancement of technology, hydrological models have increasingly been utilized to investigate the intricate relationships between the water cycle and the global climate change [19,20]. Arnell utilized the HadCM2 and HadCM3 climate models to develop a global-scale model, which simulated runoff generation, water consumption patterns, and other hydrological processes across various river basins [21]. Nosetto et al. utilized the SWAT model, informed by hydrological data from the Southern Hemisphere, to simulate the impacts of various vegetation types on the basin-scale water cycle [22].
The application of hydrological models in China has resulted in numerous successful cases. Huang et al. developed the HBV and SWAT models for the upper Yangtze River basin, simulated the quantitative relationship between precipitation and runoff, and projected future runoff changes under the RCP4.5 scenario [23]. Yang et al. utilized an integrated approach combining the CA–Markov model with the SWAT model to elucidate the regulatory role of land surface changes in the Luan River basin of North China in runoff and clarified how such changes alleviate the influence of climate change on runoff [24].
Among various hydrological modeling methods, the SWAT model is regarded as one of the most suitable models for assessing the response processes of runoff, sediment load, and nutrients in large and medium-sized basins. This is due to its capability to integrate multiple constraint conditions, including underlying surface conditions, soil types, climate information, and management practices [25].
The Yanhe River is a typical tributary of the middle reaches of the YR and represents an ecologically fragile area on the LP. Since the 21st century, numerous soil and water conservation measures have been implemented in the Yanhe River basin to control soil erosion and water loss, such as the GGP and silting dam construction, which have significantly altered the land-use patterns and profoundly impacted the runoff process. We conducted an attribution analysis of runoff changes in the Yanhe River basin and concluded that land surface variations caused by human activities were the primary drivers of the runoff changes over the past 50 years [26]. In the context of the ongoing climate change, formulating reasonable land-use policies is of great significance for the development of the Yanhe River basin. After reviewing publicly accessible research findings, we identified a lack of studies investigating the response of the runoff process to the dual scenario of climate conditions and land-use changes in this region. To fill this gap, we employed the SWAT model as a technical tool and set up different climate change and land-use scenarios, aiming to reveal the response mechanisms of runoff to complex environmental variations, thereby providing theoretical support and a scientific basis for ecological construction and high-quality development in the YR basin.

2. Materials and Methods

2.1. Study Area: An Overview of the Research Locale

This study primarily focused on the area above the control section of the Ganguyi hydrological station in the Yanhe River basin, encompassing an area of 5891.64 km2 (Figure 1), with a relative altitude variation of 972 m.
From 1969 to 2019, the annual average precipitation was 489.79 mm, the maximum precipitation was 844.60 mm, and the minimum was 296.46 mm. The annual precipitation distribution was mostly concentrated in the flood season (from June to September), accounting for more than 70% of the total annual precipitation. The annual average temperature was 9.4 °C, the annual average wind speed was 1.3–3.3 m s−1, the annual average sunshine was 2418 h, the annual average frost-free period was 172 days, and the annual average evaporation was about 1000 mm.

2.2. Data Preprocessing: A Critical Step in Data Analysis

The Digital Elevation Model (DEM) with a resolution of 25 m × 25 m was obtained from the Research Center for Eco-Environment, Chinese Academy of Sciences. This DEM was utilized for river network extraction, basin boundary delineation, sub-basin division, and Hydrologic Response Unit (HRU) identification.
The land-use data were derived from remote sensing images acquired in 1985, 1995, 2008, and 2015 (Figure A1). The primary land-use categories within the study area were reclassified into new attribute types recognizable by the SWAT model: farmland as AGRL, woodland as FRST, grassland as PAST, construction land as URLD, water bodies as WATR, and other land as SWRN. The soil data, with a resolution of 1 km × 1 km, were sourced from the Harmonized World Soil Database (HWSD).
The meteorological records, including precipitation, temperature, etc., were obtained from five stations: Ansai, Jingbian, Yan’an, Yanchang, and Zhidan. The long-term (1981–2020) average monthly precipitation distribution is illustrated in Figure 2. The daily measured runoff data utilized in this study were recorded by the Yan’an hydrological station and Ganguyi hydrological station, covering the period from 1981 to 2020.

2.3. Scenario Configuration

2.3.1. Response of Runoff to Climate and Land-Use Changes

According to previous research, we identified an abrupt change in the temporal sequence of runoff in 2000 during the study period. Consequently, the climatic conditions were categorized into a reference period (1981–1999) and a change period (2000–2020), and the land-use data of 1995 were utilized, while other datasets remained unchanged. This approach allowed us to simulate the response of runoff to varying climatic conditions. Under the climatic conditions of the reference period (1981–1999), three land-use scenarios—1995, 2008, and 2015—were designed to simulate the response of runoff to varying land-use patterns (Table 1).

2.3.2. Runoff Simulation Under Scenarios of Climate and Land-Use Change

As a primary tool for basin management and ecological restoration, it is crucial to accurately assess the future development strategies of the GGP within the context of ecological protection and high-quality development in the YR basin. In this study, we designed simulations to evaluate the runoff effects under different climate conditions and varying implementations of the GGP policy (Table 2).
Given the warming–wetting trend observed in arid and semi-arid regions of Northwest China due to global warming, we established climate change scenarios with differing degrees of warming–wetting trends (RC1~RC3). Based on the specific conditions of the Yanhe River basin, where farmland areas with slopes exceeding 25 degrees cover 34.38 km2 and those exceeding 15 degrees cover 140.91 km2, we formulated six distinct GGP policy scenarios (SG1~SG6).

2.4. Technical Approach

The technical approach employed in this study is presented in Figure 3.

3. Results

3.1. Calibration and Evaluation of the SWAT Model Applicability

The ArcSWAT platform was utilized to conduct the HRU analysis. Two monitoring points, Yan’an station and Ganguyi station, were added, with Ganguyi station also serving as the outlet of the Yanhe River basin. The catchment area threshold was determined through repeated trial calculations and set to 8000 hm2. Consequently, the study area was divided into 38 sub-basins comprising a total of 656 HRUs.
Based on relevant research on the SWAT model in the LP, 20 parameters pertinent to runoff simulation were selected. The SUFI-2 algorithm was employed to evaluate these parameters using the sensitivity index (t-stat) and the significance level (p-value). The preheating period of the model was set from 1981 to 1983, the calibration period from 1984 to 1989, and the verification period from 1990 to 1995. The processed database, incorporating land-use data from 1985, was imported into the model. The parameters were calibrated using the measured monthly runoff data from Ganguyi station to evaluate the simulation results. The coefficient of determination (R2), the Nash–Sutcliffe efficiency coefficient (Ens), and percent bias (PBIAS) were selected as evaluation indicators. A simulation result was deemed satisfactory if R2 > 0.75, Ens > 0.65, and |PBIAS| ≤ 15%.
The simulated runoff values during the calibration and validation periods were compared with the observed runoff values to evaluate the model’s applicability in the Yanhe River basin. The evaluation results showed that the R2 values for the calibration and verification period were 0.79 and 0.77, respectively, while the Ens values were 0.70 and 0.67, and the |PBIAS| values were 10.38% and 11.83%, respectively. These results indicated a high degree of consistency between the simulated and the observed monthly runoff trends in the study area (Figure 4), confirming the model’s suitability for the Yanhe River basin.

3.2. Runoff Response to Varied Climate Conditions

According to the simulation results obtained using the SWAT model, runoff increased by 3.87% and 4.09% annually and during the flood season, respectively (Table 3), under the land-use of 1995 and the climatic conditions during the change period (2000–2020). These findings indicated that the changes in the climatic conditions had a modest positive impact on the runoff process in the Yanhe River basin.
Compared with the BC-C scenario, the mean and median in the box plot for the scenario C1 were significantly improved, the interquartile range became narrower, and there were outliers that deviated substantially from the box limits (Figure 5). These findings indicated that the annual nonuniformity of the simulated runoff in the study area increased after 2000, leading to a higher frequency of flood disasters caused by extreme climate events.

3.3. Runoff Response to Varied Land-Use Patterns

Under the climate conditions of the reference period (1981–1999), the runoff responses to land-use in 1995, 2008, and 2015 were simulated and analyzed (Table 4). Compared with 1995, runoff decreased by 39.09% in 2008 and by 81.21% in 2015, with a more pronounced decline during the flood season than the annual average. These findings are consistent with our previous research, which concluded that the changes in land-use (vegetation) during the study period were the primary drivers of the significant reduction in runoff in the Yanhe River basin.
In addition, with the moderate increase in woodland and grassland in the study area, the annual non-uniformity of runoff significantly improved, as illustrated by the box plot in Figure 6.
This indicates that an enhanced vegetation coverage on the underlying surface has a notable effect on regulating the annual runoff. Specifically, this effect manifests in two ways: firstly, an increased surface roughness due to the vegetation coverage reduces the collection speed of runoff; and secondly, the abundance of plant roots enhances the soil water retention capacity, thereby decreasing runoff generation. The combined impact of these two factors ultimately results in a reduced peak runoff and a more uniform distribution of the annual runoff.

3.4. Simulation of the Runoff Effects Under Diverse Climatic Conditions and the GGP Policy

To control for other variables, the six scenarios were divided into three groups for analysis: Group 1 (SG1 and SG2), Group 2 (SG3 and SG4), and Group 3 (SG5 and SG6) (Table 5 and Figure 7). Under different climate change conditions, the scenarios focusing on woodland as the primary direction for ecological restoration performed better than those emphasizing grassland in terms of runoff reduction. Overall, when the goal of watershed management is to consolidate soil, conserve water, and reduce soil and water loss, woodland is a more effective choice for ecological restoration compared to grassland. For the two scenarios (SG3 and SG4) where ecological restoration was conducted only in areas with slopes exceeding 25 degrees, the effect on runoff reduction was minimal. When the climatic conditions shifted towards a warming–wetting trend, the runoff volume in these scenarios tended to increase, making it difficult to achieve the objective of controlling soil and water loss.
In different climatic scenarios, the scenario SG6 (farmland with slopes between 15 and 25 degrees, as well as slopes exceeding 25 degrees, converted into woodland and grassland, respectively) was a relatively better choice compared to all the others. This approach can lead to a more reasonable landscape layout and vegetation ecosystem while taking into account economic and social benefits.
Among the six different scenarios of the GGP policy, the effectiveness of runoff reduction followed the order SG1 > SG6 > SG5 > SG2 > SG3 > SG4. In comparison, under the three climatic conditions (RC1, RC2, and RC3), the differences in the annual runoff reduction rates between the SG1 and the SG6 scenarios were 0.25%, 0.14%, and 0.34%, respectively. During the flood season, these differences were 0.31%, 0.03%, and 0.34%, respectively. The above two scenarios showed similar contributions to runoff reduction, but the implementation of the SG1 scenario (all farmland with a slope exceeding 15 degrees converted into woodland) would clearly require more time and economic costs. Additionally, a simple and uniform ecosystem structure may reduce the resilience to external environmental changes. Therefore, woodland is more suitable for slopes between 15 and 25 degrees within the basin.
Under the same GGP policy, the examined climate condition scenarios exerted a certain influence on the runoff process. Specifically, runoff increased with rising temperature and precipitation, while the storage capacity of the underlying surface for runoff relatively diminished; however, this effect remained extremely limited.
As shown in Figure 7, the mean lines of the boxplot were consistently higher than the median lines. This discrepancy was attributed to the significantly higher measured runoff in 2013, which was driven by frequent extreme weather events in the study area, identified by the outlier values in the statistical analysis. Since this phenomenon was consistently observed across all simulation results, these outlier values had minimal impact on the overall mean-level analysis.

4. Discussion

4.1. Attribution Analysis of Runoff Changes in the Yanhe River Basin

The changes in landscape patterns caused by adjustments in the land-use structure are among the key factors influencing the runoff process, directly reflecting the intensity of human activities [1]. As a typical basin in the middle reaches of the YR, the Yanhe River basin is highly sensitive to the impacts of climate change and human activities. Since the large-scale implementation of the GGP in 1999, the area of woodland and grassland in the Yanhe River basin has steadily increased, while the runoff volume has significantly decreased. Some scholars attribute this phenomenon primarily to climate change and human activities; however, the assessment methods used, and the resulting contribution values vary considerably.
According to Ning et al. [27], who employed the double cumulative curve method and water balance model, human activities, particularly the GGP, were the primary drivers of the significant reduction in runoff in the Yanhe River basin between 1960 and 2015, contributing by approximately 89.09% to this decrease. Liu et al. [28] also used the double cumulative curve method and found that the land-use changes driven by human activities have been the main cause of the significant reduction in both runoff and sediment load in the Yanhe River basin since 1987, contributing by approximately 80.61% to the observed decrease in runoff. The results from the SWAT model analysis in this study indicated that the changes in underlying surface conditions, driven by land-use alterations, contributed by 81.21% to the reduction in runoff in the Yanhe River basin during the study period. This finding is largely consistent with existing research.

4.2. Future Land Management Policies in the Yanhe River Basin

With the intensification of the global climate change, over the past 10 to 15 years, Northwest China has witnessed a significant increase in both temperature and precipitation, indicative of a warming–wetting trend [29,30]. Notably, the rate of temperature increase in the YR source area [31] and certain regions of the LP has surpassed the national and global averages [32,33]. A warming–wetting climate will inevitably alter the survival conditions and water requirements of the terrestrial vegetation. Given the severe scarcity of water resources in arid and semi-arid regions, this issue must be of primary consideration in rationally determining the scale of future ecological restoration efforts. If the water resource conditions are inadequate to support a large-scale ecological restoration, this may result in suboptimal outcomes that fall short of expectations [34,35].
A review of the existing literature revealed that no studies have used the SWAT model to couple climate conditions with land-use change scenarios to predict runoff trends in the Yanhe River basin. Our research indicated that, despite the ongoing efforts to control soil erosion, the Yanhe River basin still holds potential for further implementation of the GGP. However, the response of runoff to vegetation restoration measures exhibited a threshold effect, meaning that the runoff reduction effect will not persist indefinitely. Therefore, it is crucial to assess the current effectiveness of ecological restoration efforts through reliable methods and to determine the carrying capacity threshold for regional ecological restoration based on these assessments.

4.3. Assessment of the Application Performance of the SWAT Model

Several studies indicated that the resolution of the DEM is strongly correlated with the simulation performance of the SWAT model [36]. As the resolution decreases, the simulation results exhibit irregular variations, and the optimal simulation performance does not necessarily align with the highest DEM resolution. Meanwhile, the resolution of the land-use data also affects the performance of the SWAT model, but to a much lesser extent compared to the DEM resolution. The sensitivity of the calibrated model parameters differs significantly across various combinations of DEM and land-use.
In this study, the monthly runoff simulation results obtained using the SWAT model were generally satisfactory. However, discrepancies between the measured and the simulated values existed in certain periods. During the flood season, for months where the measured runoff was significantly higher than the average, the simulated values tended to exhibit overestimation. In contrast, during non-flood seasons, the deviation between measured and simulated values was relatively smaller.
The quality of the basic data and the selection of the model parameters are essential prerequisites for the successful application of hydrological models. Calibrated parameters represent the optimal compromise under specific combinations of underlying surface conditions and climate scenarios. However, these parameters may still exhibit limitations when addressing anomalies such as those encountered during flood seasons or with “sudden factors”. The LP region is characterized by a complex runoff transport process. Consequently, the model parameters may fail to fully and accurately capture the actual runoff dynamics, leading to inevitable overestimation or underestimation in the simulation results.
From a seasonal perspective, the simulated values in spring (February to April) were consistently lower than the measured values. This suggests that the model may underestimate the contribution of snowmelt to runoff during this period. Therefore, further refinement of the snowmelt runoff simulation module is necessary.

5. Conclusions

We selected the Yanhe River basin, located in the arid and semi-arid regions of the Loess Plateau, as our study area. Through parameter calibration and validation, we developed a SWAT model tailored to this region. We established various climate and land-use scenarios to identify the key factors influencing runoff changes utilizing this model. Through scenario simulations, we assessed the soil and water conservation effects of different land management strategies.
The research results indicated the following: (1) Under the 1995 land-use conditions, the climate conditions from 2000 to 2020 would lead to increases in annual runoff and flood season runoff by 3.87% and 4.09%, respectively. The impact of climate change on the runoff process in the study area was minimal. (2) Under the land-use scenarios of 2008 and 2015, the simulated runoff decreased by 39.09% and 81.21%, respectively, compared to the that in the 1995 scenario. The reduction during the flood season was more pronounced than the annual average. Vegetation changes during the study period were the primary factor contributing to the significant decrease in runoff in the Yanhe River basin. (3) Under changing climate conditions, the woodland scenarios performed better in reducing runoff compared to the grassland scenarios.
Among different scenarios of the GGP, the decision to convert farmland on slopes of 15–25 degrees to woodland and farmland on slopes over 25 degrees to grassland is a relatively optimal choice. This plan will facilitate the formation of a well-structured landscape layout and vegetation ecosystem, while balancing the economic and social benefits.

Author Contributions

Methodology, K.H.; project administration, J.W.; writing—original draft, K.H.; writing—review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Natural Science Foundation of China] grant number [41871195] and [Natural Science Foundation of China] grant number [52379082]. The APC was also funded by the foundation above.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We thank the two anonymous reviewers for their valuable comments and constructive suggestions on the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
LPLoess Plateau
YRYellow River
GGPGrain for Green Project

Appendix A

Water 17 01042 g0a1
Figure A1. Land-use from 1985 to 2015: (a) 1985, (b) 1995, (c) 2008, (d) 2015.
Figure A1. Land-use from 1985 to 2015: (a) 1985, (b) 1995, (c) 2008, (d) 2015.
Water 17 01042 g0a1

References

  1. Ureta, J.C.; Clay, L.; Motallebi, M.; Ureta, J. Quantifying the Landscape’s Ecological Benefits—An Analysis of the Effect of Land Cover Change on Ecosystem Services. Land 2021, 10, 21. [Google Scholar] [CrossRef]
  2. Elfert, S.; Bormann, H. Simulated impact of past and possible future land use changes on the hydrological response of the Northern German lowland ’Hunt’ catchment. J. Hydrol. 2010, 383, 245–255. [Google Scholar] [CrossRef]
  3. Kundu, S.; Khare, D.; Mondal, A. Past, present and future land use changes and their impact on water balance. J. Environ. Manag. 2017, 197, 582–596. [Google Scholar] [CrossRef]
  4. Xue, D.; Zhou, J.; Zhao, X.; Liu, C.; Wei, W.; Yang, X.; Li, Q.; Zhao, Y. Impacts of climate change and human activities on runoff change in a typical arid watershed, NW China. Ecol. Indic. 2021, 121, 107013. [Google Scholar] [CrossRef]
  5. Tirupathi, C.; Shashidhar, T. Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios. Sci. Total Environ. 2020, 721, 137736. [Google Scholar] [CrossRef]
  6. Zhang, L.; Karthikeyan, R.; Bai, Z.; Srinivasan, R. Analysis of streamflow responses to climate variability and land use change in the Loess Plateau region of China. Catena 2017, 154, 1–11. [Google Scholar]
  7. Schilling, K.; Chan, K.; Liu, H.; Zhang, Y. Quantifying the effect of land use land cover change on increasing discharge in the Upper Mississippi River. J. Hydrol. 2015, 387, 343–345. [Google Scholar] [CrossRef]
  8. Miao, C.; Ni, J.; Borthwick, A.; Lin, Y. A preliminary estimate of human and natural contributions to the changes in water discharge and sediment load in the Yellow River. Glob. Planet. Change 2011, 76, 196–205. [Google Scholar] [CrossRef]
  9. Liu, J.; Zhang, Q.; Singh, V.; Shi, P. Contribution of multiple climatic variables and human activities to streamflow changes across China. J. Hydrol. 2016, 545, 145–162. [Google Scholar] [CrossRef]
  10. Wang, S.; Yan, M.; Yan, Y.; Shi, C.; He, L. Contributions of climate change and human activities to the changes in runoff increment in different sections of the Yellow River. Quatern. Int. 2012, 282, 66–77. [Google Scholar] [CrossRef]
  11. Xu, X.; Yang, D.; Yang, H.; Lei, H. Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin. J. Hydrol. 2014, 510, 530–540. [Google Scholar] [CrossRef]
  12. Gao, G.; Fu, B.; Wang, S.; Liang, W.; Jiang, X. Determining the hydrological responses to climate variability and land use/cover change in the Loess Plateau with the Budyko framework. Sci. Total Environ. 2016, 557–558, 331–342. [Google Scholar] [CrossRef]
  13. Baker, T.; Miller, S. Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed. J. Hydrol. 2013, 486, 100–111. [Google Scholar] [CrossRef]
  14. Amiri, S.; Khoshravesh, M.; Valashedi, R. Assessing the effect of climate and land use changes on the hydrologic regimes in the upstream of Tajan river basin using SWAT model. Appl. Water Sci. 2023, 13, 130. [Google Scholar] [CrossRef]
  15. Wu, J.; Miao, C.; Zhang, X.; Yang, T.; Duan, Q. Detecting the quantitative hydrological response to changes in climate and human activities. Sci. Total Environ. 2017, 586, 328–337. [Google Scholar] [CrossRef]
  16. Ma, Z.; Kang, S.; Zhang, L.; Tong, L.; Su, X. Analysis of impacts of climate variability and human activity on streamflow for a river basin in arid region of northwest China. J. Hydrol. 2008, 352, 239–249. [Google Scholar] [CrossRef]
  17. Ahn, K.; Merwade, V. Quantifying the Relative Impact of Climate and Human Activities on Streamflow. J. Hydrol. 2014, 515, 257–266. [Google Scholar] [CrossRef]
  18. Mo, C.; Huang, K.; Ruan, Y.; Lai, S.; Lei, X. Quantifying uncertainty sources in runoff change attribution based on the Budyko framework. J. Hydrol. 2024, 630, 130790. [Google Scholar] [CrossRef]
  19. Fujihara, Y.; Tanaka, K.; Watanabe, T.; Nagano, T.; Kojiri, T. Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: Use of dynamically downscaled data for hydrologic simulations. J. Hydrol. 2008, 353, 33–48. [Google Scholar] [CrossRef]
  20. Murray, S.; Foster, P.; Prentice, I. Future global water resources with respect to climate change and water withdrawals as estimated by a dynamic global vegetation model. J. Hydrol. 2012, 448–449, 14–29. [Google Scholar] [CrossRef]
  21. Arnell, N. Climate change and global water resources: SRES emissions and socio-economic scenarios. Glob. Environ. Change 2004, 14, 31–52. [Google Scholar] [CrossRef]
  22. Nosetto, M.; Jobbágy, E.; Paruelo, J. Land-use change and water losses: The case of grassland afforestation across a soil textural gradient in central Argentina. Glob. Change Biol. 2010, 11, 1101–1117. [Google Scholar] [CrossRef]
  23. Huang, J.; Wang, Y.; Su, B.; Zhai, J. Future Climate Change and Its Impact on Runoff in the Upper Reaches of the Yangtze River Under RCP4.5 Scenario. Meteorol. Mon. 2016, 42, 614–620. (In Chinese) [Google Scholar] [CrossRef]
  24. Yang, W.; Long, D.; Bai, P. Impacts of future land cover and climate changes on runoff in the mostly afforested river basin in North China. J. Hydrol. 2019, 570, 201–219. [Google Scholar] [CrossRef]
  25. Meng, X.; Wang, H.; Chen, J. Profound Impacts of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). Water 2019, 11, 832. [Google Scholar] [CrossRef]
  26. Hou, K.; Wang, J.; Wang, X. Characteristic and Attribution of Runoff Variation in the Yanhe River Basin, Loess Plateau, Based on the Budyko Hypothesis. Water 2022, 14, 495. [Google Scholar] [CrossRef]
  27. Ning, Y.; Yang, X.; Sun, W.; Mu, X.; Gao, P.; Zhao, G.; Song, X. The trend of runoff change and its attribution in the middle reaches of the Yellow River. J. Nat. Resour. 2021, 36, 256–269. (In Chinese) [Google Scholar] [CrossRef]
  28. Liu, Q.; Mu, X.; Zhao, G.; Gao, P.; Sun, W.; Nan, L. Runoff and sediment changes and their responses to precipitation and land use change in the Yanhe river basin. J. Arid Land Resour. Environ. 2021, 35, 129–135. (In Chinese) [Google Scholar] [CrossRef]
  29. Li, M.; Sun, H.; Su, Z. Research progress in dry/wet climate variation in Northwest China. Geogr. Res. 2021, 40, 1180–1194. (In Chinese) [Google Scholar] [CrossRef]
  30. Zhang, X.; Zheng, J.; Hao, Z. Climate change assessments for the main economic zones of China during recent decades. Prog. Geogr. 2020, 39, 1609–1618. (In Chinese) [Google Scholar] [CrossRef]
  31. Liu, C.; Wang, P.; Wen, T.; Yu, D.; Bai, W. Spatio-temporal characteristics of climate change in the Yellow River source area from 1960 to 2019. Arid Zone Res. 2021, 38, 293–302. (In Chinese) [Google Scholar] [CrossRef]
  32. Zhao, J.; Xia, H.; Yue, Q.; Wang, Z. Spatiotemporal variation in reference evapotranspiration and its contributing climatic factors in China under future scenarios. Int. J. Climatol. 2020, 40, 3813–3831. [Google Scholar] [CrossRef]
  33. Ji, G.; Lai, Z.; Xia, H.; Liu, H.; Wang, Z. Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT. Land 2021, 10, 421. [Google Scholar] [CrossRef]
  34. Piao, S.; Yin, G.; Tan, J.; Cheng, L.; Huang, M.; Li, Y.; Liu, R.; Mao, J.; Myneni, R.; Peng, S.; et al. Detection and attribution of vegetation greening trend in China over the last 30 years. Glob. Change Biol. 2015, 21, 1601–1609. [Google Scholar] [CrossRef]
  35. Li, Z.; Wang, Y.; Zhang, J. Runoff response to changing environment in Loess Plateau, China: Implications of the influence of climate, land use/land cover, and water withdrawal changes. J. Hydrol. 2022, 613, 128458. [Google Scholar] [CrossRef]
  36. Li, R.; Wu, L.; Du, B.; Guo, Z.; Wang, Y.; Xu, L. Impact of DEM and land cover resolution on the simulation of runoff andsediment in SWAT Model: A case study of Jinghe River Basin. J. Water Resour. Water Eng. 2023, 34, 70–79. (In Chinese) [Google Scholar]
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Figure 1. Location of the study area: (a) location of the Yanhe River basin on the LP, (b) distribution of the hydrological and weather stations.
Figure 1. Location of the study area: (a) location of the Yanhe River basin on the LP, (b) distribution of the hydrological and weather stations.
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Figure 2. The long-term distribution of monthly precipitation in the study area.
Figure 2. The long-term distribution of monthly precipitation in the study area.
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Figure 3. Technical approach of the study.
Figure 3. Technical approach of the study.
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Figure 4. Comparison of the simulated runoff results at the Ganguyi hydrological station: (a) the calibration period, (b) the verification period.
Figure 4. Comparison of the simulated runoff results at the Ganguyi hydrological station: (a) the calibration period, (b) the verification period.
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Figure 5. Statistical analysis of runoff response under diverse climatic scenarios.
Figure 5. Statistical analysis of runoff response under diverse climatic scenarios.
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Figure 6. Statistical analysis of runoff response under diverse land-use scenarios.
Figure 6. Statistical analysis of runoff response under diverse land-use scenarios.
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Figure 7. Statistical analysis of the runoff simulation results under different climate conditions and the GGP policy.
Figure 7. Statistical analysis of the runoff simulation results under different climate conditions and the GGP policy.
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Table 1. Scenario design for runoff response to climate and land-use conditions.
Table 1. Scenario design for runoff response to climate and land-use conditions.
GroupCodeScenario Design
Climatic conditionsBC-CClimatic conditions of the reference period (1981–1999) with land-use from 1995
C1Climatic conditions of the change period (2000–2020) with land-use from 1995
Land-use conditionsBC-LClimatic conditions of the reference period (1981–1999) with land-use from 1995
L1Climatic conditions of the reference period (1981–1999) with land-use from 2008
L2Climatic conditions of the reference period (1981–1999) with land-use from 2015
Table 2. Scenario design for runoff simulation under varying climate and land-use conditions.
Table 2. Scenario design for runoff simulation under varying climate and land-use conditions.
GroupCodeScenario Design
Climatic conditionsRC1Climatic conditions of the change period (2000–2020)
RC2Precipitation and temperature increased by 2%, respectively, under the RC1 scenario
RC3Precipitation and temperature increased by 5%, respectively, under the RC1 scenario
GGP policiesSG1Converting all farmland with a slope exceeding 15 degrees into woodland
SG2Converting all farmland with a slope exceeding 15 degrees into grassland
SG3Converting all farmland with a slope exceeding 25 degrees into woodland
SG4Converting all farmland with a slope exceeding 25 degrees into grassland
SG5Converting farmland on slopes between 15 and 25 degrees into grassland, and farmland on slopes exceeding 25 degrees into woodland
SG6Converting farmland on slopes between 15 and 25 degrees into woodland, and farmland on slopes exceeding 25 degrees into grassland
Table 3. Runoff response to varied climatic conditions.
Table 3. Runoff response to varied climatic conditions.
ScenariosAnnual RunoffChange RateRunoff in Flood SeasonChange Rate
BC-C7.24 m3·s−13.87%13.68 m3·s−14.09%
C17.52 m3·s−114.24 m3·s−1
Table 4. Runoff response to varied land-use.
Table 4. Runoff response to varied land-use.
ScenariosAnnual RunoffChange RateRunoff in Flood SeasonChange Rate
BC-L7.24 m3·s−1/13.68 m3·s−1/
L14.41 m3·s−1−39.09%7.87 m3·s−1−42.47%
L21.36 m3·s−1−81.21%2.24 m3·s−1−83.63%
Table 5. Simulation results of runoff under different climate conditions and the GGP policy.
Table 5. Simulation results of runoff under different climate conditions and the GGP policy.
ScenariosSimulated Runoff of the Change Period (m3·s−1)
Annual
Average		Change from the Measured ValueFlood Season
Average		Change from the Measured Value
RC1SG14.381−7.54%7.154−9.67%
SG24.427−6.57%7.249−8.47%
SG34.717−0.45%7.898−0.27%
SG44.729−0.19%7.913−0.08%
SG54.407−6.98%7.214−8.92%
SG64.392−7.29%7.178−9.36%
RC2SG14.422−6.66%7.235−8.65%
SG24.469−5.68%7.341−7.32%
SG34.7630.54%7.9780.73%
SG44.7820.93%8.0261.34%
SG54.456−5.95%7.301−7.82%
SG64.429−6.52%7.237−8.62%
RC3SG14.514−4.72%7.378−6.84%
SG24.556−3.83%7.507−5.21%
SG34.7961.23%8.0601.77%
SG44.8392.14%8.1522.93%
SG54.566−3.63%7.484−5.50%
SG64.530−4.38%7.405−6.50%
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Hou, K.; Wang, J.; Zhang, X. Simulation and Response of Runoff to Climate and Land-Use Changes in the Yanhe River Basin, Loess Plateau: A SWAT Model-Based Analysis. Water 2025, 17, 1042. https://doi.org/10.3390/w17071042

AMA Style

Hou K, Wang J, Zhang X. Simulation and Response of Runoff to Climate and Land-Use Changes in the Yanhe River Basin, Loess Plateau: A SWAT Model-Based Analysis. Water. 2025; 17(7):1042. https://doi.org/10.3390/w17071042

Chicago/Turabian Style

Hou, Kun, Jianhua Wang, and Xiaoming Zhang. 2025. "Simulation and Response of Runoff to Climate and Land-Use Changes in the Yanhe River Basin, Loess Plateau: A SWAT Model-Based Analysis" Water 17, no. 7: 1042. https://doi.org/10.3390/w17071042

APA Style

Hou, K., Wang, J., & Zhang, X. (2025). Simulation and Response of Runoff to Climate and Land-Use Changes in the Yanhe River Basin, Loess Plateau: A SWAT Model-Based Analysis. Water, 17(7), 1042. https://doi.org/10.3390/w17071042

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