三江源地区2000-2015年土壤侵蚀变化及生态恢复潜在影响研究（英文）

Changes of Soil Erosion and Possible Impacts from Ecosystem Recovery in the Three-River Headwaters Region, Qinghai, China from 2000 to 2015

土壤侵蚀对于生态环境的保护恢复以及人与自然的和谐发展产生了巨大威胁。本文采用修正的通用土壤流失方程(Revised Universal Soil Loss Equation, RUSLE模型)对三江源区2000–2015年的土壤侵蚀进行定量模拟,通过对研究区土壤侵蚀强度的时空变化特征进行分析,比较了不同生态系统土壤侵蚀的差异,探讨了气候变化对研究区土壤侵蚀的影响作用,评价了该区域生态保护和恢复工程对土壤保持的作用效果。模型模拟结果可以通过地面观测站点验证,对模拟数据和验证数据的相关分析结果显示,模型的拟合效果良好,其复相关系数达到0.62。2010–2015年的土壤侵蚀模数比2000–2005年增加了6.2%,比2005–2010年下降了1.2%。基于叠加分析,影响土壤侵蚀的主要驱动因素是气候(约64%),特别是降水的作用明显,其次是植被覆盖度(约34%)。虽然模型和数据存在一些不确定性,但本研究量化了全球气候变化下生态恢复工程的相对贡献,同时也认识到了生态恢复工程是一项复杂、艰巨、长期的任务。在未来,持续的地面观测、进一步的参数化以及获取更高质量的输入数据是减少模拟结果不确定性的必要方式。

Soil erosion poses a great threat to the sustainability of the ecological environment and the harmonious development of human well-being.The revised universal soil loss equation(RUSLE)was used to quantify soil erosion in the Three-River Headwaters region(TRH),Qinghai,China from 2000 to 2015.The possible effects of an ecosystem restoration project on soil erosion were explored against the background of climatic changes in the study area.The model was validated with on-ground observations and showed a satisfactory performance,with a multiple correlation coefficient of 0.62 from the linear regression between the estimations and observations.The soil erosion modulus in 2010–2015 increased 6.2%,but decreased 1.2%compared with those in the periods of 2000–2005 and 2005–2010,respectively.Based on the method of overlay analysis,the interannual change of the estimated soil erosion was dominated by climate(about 64%),specifically by precipitation,rather than by vegetation coverage(about 34%).Despite some uncertainties in the model and data,this study quantified the relative contribution of ecological restoration under global climatic change;meanwhile the complexity,labor-intensiveness and long-range character of ecological restoration projects have to be recognized.On-ground observations over the long-term,further parameterization,and data inputs with higher quality are necessary and essential for decreasing the uncertainties in the estimations.