Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Framework for Runoff Response to Land Use Change
2.3. Data Source
2.4. SWAT+ Model
2.5. CLUE-S Model
2.6. Land Use Development Scenarios Establishment
3. Results
3.1. Model Calibration and Validation
3.2. Historical Land Use Changes and Land Use Development Scenarios
3.3. The Impact of Historical Land Use on Runoff
3.4. Runoff Response to Different Land Use Change Scenarios
4. Discussion
4.1. Impacts of Different Land Use Scenarios on Runoff
4.2. Implications and Policies for Different Sub-Basins
4.3. Limitations and Uncertainties
5. Conclusions
- (1)
- In the upstream mountainous basin of Baiyangdian, SWAT+ was well applicable, with NSE values exceeding 0.7 for all hydrological stations. The simulated land use data generated by the CLUE-S model demonstrated high spatial distribution consistency with real data, achieving a Kappa coefficient of 0.83.
- (2)
- Urban land expansion drove changes in land use in ND, FP, and EP, while the FP and EP decelerated this expansion. Additionally, the scenario EP effectively controlled the reduction of forest area in the basin.
- (3)
- The annual runoff in the upstream basin of Baiyangdian showed an upward trend under the influence of historical urbanization trends in land use. The simulated annual runoff changes under different land use scenarios also exhibited an upward trend, with no significant differences observed among the three scenarios. However, significant seasonal disparities in runoff changes were observed under different scenarios. Generally, the ND and FP scenarios promoted runoff production from May to October, while the EP scenario had a significant reduction effect on the monthly runoff peak from July to August, particularly in the DRS. These differences were mainly attributed to the different contributions of various land use types to water yield.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Types of Change 1 | Calibrated Value | |||||
---|---|---|---|---|---|---|---|
Zijingguan | Fuping | Manshuihe | Zhongtangmei | Zhangfang | Luobaotan | ||
cn2 | pctchg | 5.47 | −7.26 | −6.12 | 13.60 | −0.63 | 4.80 |
lat_len | abschg | −4.54 | −27.79 | −23.29 | 4.70 | 14.46 | −8.19 |
lat_ttime | abschg | −0.21 | 5.61 | 25.07 | −25.73 | 2.87 | −16.56 |
esco | absval | 0.25 | 0.02 | 0.85 | 0.07 | 0.89 | 0.18 |
epco | absval | 0.34 | 0.35 | 0.69 | 0.57 | 0.06 | 0.75 |
slope | abschg | 5.83 | 2.40 | 5.79 | 1.98 | −24.37 | −20.04 |
canmx | absval | 1.94 | 6.36 | 23.10 | 11.68 | 87.06 | 84.20 |
ovn | abschg | 4.87 | −16.12 | −24.95 | −16.39 | −10.26 | −4.63 |
snofall_tmp | abschg | 18.13 | −8.10 | 17.57 | 26.11 | −8.72 | −3.42 |
snomelt_tmp | abschg | −3.38 | 3.04 | 7.56 | −35.16 | −3.50 | −16.00 |
snomelt_max | absval | 1.72 | 3.14 | 8.49 | 2.22 | 2.42 | 4.56 |
snomelt_min | absval | 1.02 | 2.94 | 1.58 | 5.36 | 5.77 | 9.59 |
snomelt_lag | absval | 0.13 | 0.17 | 0.97 | 0.64 | 0.34 | 0.43 |
alpha | absval | 0.01 | 0.85 | 0.37 | 0.49 | 0.93 | 0.01 |
flo_min | abschg | 13.37 | 22.53 | −32.55 | 16.58 | −21.90 | −2.17 |
revap_co | abschg | 14.26 | −0.65 | −31.23 | 25.86 | 25.76 | 8.80 |
revap_min | abschg | −11.60 | −2.01 | 36.27 | 10.02 | −11.88 | 19.92 |
k | abschg | 8.19 | 34.85 | −3.38 | 0.34 | 34.26 | 15.63 |
awc | abschg | −17.54 | 27.37 | −7.39 | −35.41 | 21.92 | −16.74 |
Kappa = 0.83 | Number of Simulated Grids | |||||||
---|---|---|---|---|---|---|---|---|
Farmland | Forest | Grassland | Water | Urban | Bare Land | Producer Accuracy% | ||
Number of actual grids | Farmland | 136,140 | 5170 | 14,068 | 2514 | 4739 | 16 | 83.70 |
Forest | 5428 | 635,004 | 33,398 | 1030 | 2216 | 13 | 93.78 | |
Grassland | 14,276 | 32,878 | 442,689 | 1556 | 7335 | 32 | 88.76 | |
Water | 1904 | 1236 | 1819 | 11,523 | 558 | 1 | 67.62 | |
Urban | 4880 | 2787 | 6755 | 373 | 14,849 | 55 | 50.00 | |
Bare land | 0 | 14 | 36 | 66 | 0 | 0 | 0.00 | |
User accuracy% | 83.71 | 93.78 | 88.76 | 67.54 | 50.00 | 0.00 | - |
1980 | 2015 | ||||||
---|---|---|---|---|---|---|---|
Farmland | Forest | Grassland | Water | Urban | Bare Land | Transfer-Out Area | |
Farmland | 1138.21 | 52.21 | 124.89 | 16.94 | 66.52 | 0.00 | 260.55 |
Forest | 42.62 | 5175.68 | 271.85 | 10.59 | 27.97 | 0.11 | 353.14 |
Grassland | 111.29 | 245.61 | 3628.92 | 14.45 | 59.69 | 0.23 | 431.28 |
Water | 15.23 | 5.04 | 10.61 | 95.09 | 3.22 | 0.00 | 34.10 |
Urban | 10.07 | 4.96 | 3.62 | 0.97 | 83.16 | 0.00 | 19.62 |
Bare land | 0.02 | 0.92 | 0.11 | 0.00 | 0.00 | 0.60 | 1.05 |
Transfer-in area | 179.23 | 308.74 | 411.08 | 42.95 | 157.40 | 0.34 | 1099.74 |
Net increase | −81.32 | −44.40 | −20.19 | 8.85 | 137.78 | −0.71 | - |
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Gong, Y.; Geng, X.; Wang, P.; Hu, S.; Wang, X. Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study. Land 2024, 13, 1374. https://doi.org/10.3390/land13091374
Gong Y, Geng X, Wang P, Hu S, Wang X. Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study. Land. 2024; 13(9):1374. https://doi.org/10.3390/land13091374
Chicago/Turabian StyleGong, Yuan, Xin Geng, Ping Wang, Shi Hu, and Xunming Wang. 2024. "Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study" Land 13, no. 9: 1374. https://doi.org/10.3390/land13091374