| 沙质草地植被防风抗蚀生态效应的野外观测研究 |
| 摘要点击 1226 全文点击 1292 投稿时间：2003-06-07 修订日期：2003-09-15 |
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| 中文关键词 沙质草地 植被盖度 地表粗糙度 输沙量 生态效应 |
| 英文关键词 sandy grasslands vegetational coverage surface roughness length sand transported rate ecological effect |
| 作者 | 单位 | | 张华1,2, | 辽宁师范大学城市与环境学院,大连,116029 | | 李锋瑞2, | 中国科学院寒区旱区环境与工程研究所,兰州,730000 | | 伏乾科3, | 辽宁师范大学生命科学学院,大连,116029 | | 吕子君4 | 北京林业大学,北京,100083 |
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| 中文摘要 |
| 采用定位实测法,研究了科尔沁沙地沙质草地植被防风抗蚀的生态效应.结果表明:在春季风沙活动期,退化沙质草地不同恢复阶段的植被盖度存在很大差异,从流动沙地的0.3%增至固定沙地的16%.植被盖度的有序增加导致下垫面空气动力学粗糙度从流动沙地的0.013cm增至固定沙地的0.111cm,摩阻速度从0.272 m·s-1增至0.823 m·s-1,近地表20cm高度平均风速由7.0 m·s-1降至3.8 m·s-1,侵蚀风持续时数由2h降至1h.相应地,0～20cm气流层内的总输沙量由流动沙地的88.8 g·(h·cm2)-1降至固定沙地的1.6 g·(h·cm2)-1.回归分析表明,在退化沙质草地的恢复过程中,植被盖度VC与土壤风蚀率Q间具有良好的指数函数关系:Q=3.93+93.66e-0.60VC(R2=0.893,p<0.0001,n=40).
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| 英文摘要 |
| Through field observation of the variation of vegetational characteristics and sand transported rate in sandy grasslands at different levels of desertification during the spring erosive period, the ecological effects of windbreak and soil erosion reduction from sandy grasslands of the Horqin Sandy Land were studied. The results showed that vegetational coverage varied obviously from 0.3% in the shifting sand land of the severe desertification to 16% in the fixed sand land of the least desertification in mid May. Increases in vegetational coverage led to a corresponding increase in surface roughness length from 0.013 cm in the shifting sand land to 0.111 cm in the fixed sand land, thus resulting in an increase in friction velocity from 0.272 m·s-1 in the shifting sand land to 0.823 m·s-1 in the fixed sand land and a decrease in mean wind speed near the surfaces from 7.0 m·s-1 in the shifting sand land to 3.8 m·s-1 in the fixed sand land. This in turn led to a reduction in the total sand transported rate within the height of 0～20 cm from 88.8 g·(h·cm2)-1 in the shifting sand land to 1.6 g·(h·cm2)-1 in the fixed sand land. When the experimental data were analyzed by regressing the total sand transported rate ( Q ) against vegetational coverage (VC), a model of predictive regression was developed: Q=3.93+93.66e-0.60VC (R2=0 893, p<0 0001, n=40). |
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