| 刘雯丹,吴一平,KIVALOV Sergey,ALEXANDROV Georgii.汉江流域安康段径流和泥沙对土地利用变化的响应[J].农业环境科学学报,2025,44(9):2347-2361. |
| 汉江流域安康段径流和泥沙对土地利用变化的响应 |
| Responses of runoff and sediment to land use change in the Ankang section of the Han River basin |
| 投稿时间：2024-10-16 |
| DOI：10.11654/jaes.2024-0888 |
| 中文关键词: 土地利用 SWAT模型 GeoSOS-FLUS模型 径流 泥沙 |
| 英文关键词: land use SWAT GeoSOS FLUS runoff volume sediment load |
| 基金项目:中国气象局秦岭和黄土高原生态环境气象重点开放实验室基金项目（2021K-6）；陕西省重点研发产业链项目（2022ZDLSF06-04）；陕西省科技创新团队项目（2021TD-52） |
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| 中文摘要: |
| 以汉江流域安康段为研究区，为揭示流域径流量和泥沙量对土地利用变化的响应，本研究收集了研究区2001、2010年和2022年3期土地利用数据，并利用GeoSOS-FLUS模型预测了2034年3种情景（惯性发展、优先经济、生态保护）的土地利用情况，基于SWAT模型，结合6期土地利用数据和2001—2022年气象数据，定量分析了径流量和泥沙量在不同土地利用情景下的变化。结果表明：流域3个水文站（武侯镇、石泉、安康）的径流校准结果较好（r2≥0.88，NSE≥0.85，|PBIAS|≤20%），泥沙的校准结果合格（r2≥0.60，NSE≥0.50，|PBIAS|≤25%）；2001—2022 年间林地占优势，土地类型变更较为平缓，2001—2010 年的变化速率略高于 2010—2022年，建设用地面积变化速率较快，以每年5.62%的速率增加，研究时段内土地利用变化主要表现在耕地和林地间相互转换、草地转化为林地及耕地转向建设用地；GeoSOS-FLUS模型精度验证的Kappa系数为79.22%，模拟总精度为91.31%，最终利用模型预测出2034年3种情景（惯性发展、优先经济、生态保护）下的土地利用情况。情景分析结果表明，2001—2022年的土地利用变化导致流域年均径流量和泥沙量呈减少趋势，未来情景中 2034年生态保护土地利用情景下产流、产沙量最少。流域 6—10月的径流量和泥沙量约占全年的 80%和 90%。流域单位面积产水量呈现上、下游高，中游低的格局，单位面积产沙量则在中、下游高。产流、产沙变化大的位置均位于流域中部，2034年优先经济土地利用情景下多数子流域产流、产沙量相较于惯性发展情景有所增加，生态保护情景与之相反，且产沙量变化率的增减幅度大于产流量变化率。 |
| 英文摘要: |
| This study focuses on the Ankang section of the Han River basin to investigate the response of basin runoff and sediment volume to changes in land use. Data on land use for three periods（2001, 2010, and 2022）were collected, and three scenarios（inertial development, economic prioritization, and ecological preservation）were projected for 2034 using the SWAT in conjunction with the GeoSOS-FLUS. By integrating six phases of land use data and meteorological data from 2001 to 2022, we quantitatively analyzed the changes in runoff and sediment volume under different land use scenarios. The results showed that：the runoff calibration results from the three hydrological stations in the basin（Wuhou Town, Shiquan, and Ankang）were satisfactory, with r2 greater than or equal to 0.88, NSE values greater than or equal to 0.85, and |PBIAS| less than or equal to 20%. The calibration results for sediment were also acceptable, with r2 values greater than or equal to 0.60, NSE values greater than or equal to 0.50, and |PBIAS| less than or equal to 25%. Forest land predominated from 2001 to 2022, with relatively minor changes in land type. The rate of change from 2001 to 2010 was slightly higher than that from 2010 to 2022. The area of construction land increased at a rate of 5.62% per year. The primary land use changes during the study period involved the conversion of cropland and forest land to one another, the transformation of grassland into forest land, and the transition of farmland to construction land. The Kappa coefficient for the accuracy validation of the GeoSOS-FLUS model was 79.22%, and the overall simulation accuracy was 91.31%. This model was ultimately employed to predict land use in 2034 under three scenarios：inertial development, priority economy, and ecological protection. The results of the scenario analysis indicated that land use changes from 2001 to 2022 led to a decreasing trend in average annual runoff and sediment yield in the watershed. Among the future scenarios, the ecological protection land use scenario in 2034 produced the least runoff and sediment. The runoff and sediment yield from June to October accounted for approximately 80% and 90% of the annual totals, respectively. The water production per unit area of the basin exhibited a pattern of being high in the upper and lower reaches and low in the middle reaches, while sediment production per unit area was high in the middle and lower reaches. Significant changes in streamflow and sediment production were concentrated in the middle of the basin. Most sub-basins under the 2034 priority economic land use scenario showed an increase in streamflow and sediment production compared to the inertial development scenario, whereas the ecological protection scenario exhibited the opposite trend. Notably, the rate of change in sediment production was more pronounced than that in streamflow production. |
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