气候变暖、干旱加重江西省森林病虫灾害

Abstract

PDF HTML阅读 XML下载 导出引用 引用提醒 气候变暖、干旱加重江西省森林病虫灾害 DOI: 10.5846/stxb201508061661 作者: 作者单位: 江西农业大学林学院;中国科学院动物研究所,江西农业大学林学院,中国科学院动物研究所,江西农业大学林学院,江西农业大学林学院,江西省农业厅植保植检局,江西农业大学林学院 作者简介: 通讯作者: 中图分类号: S763.7 基金项目: 环保部专项资助项目（STSN-04-04） Global warming and droughts aggravates forest damage resulting from pests and diseases in Jiangxi Province Author: Affiliation: Department of Forestry,Jiangxi Agricultural University;Institute of Zoology,Chinese Academy of Sciences,Department of Forestry,Jiangxi Agricultural University,Institute of Zoology,Chinese Academy of Sciences,Department of Forestry,Jiangxi Agricultural University,Department of Forestry,Jiangxi Agricultural University,,Department of Forestry,Jiangxi Agricultural University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:近50年来，气候变化加剧、全球变暖明显、陆地表面趋向干旱，与此同时，森林病虫灾害损失不断增加，两者关系密切。以江西省为研究区域，采集1961-2010年气温、湿度、日照等气候要素及1992-2010年森林病虫灾害发生面积、程度数据，获知研究区过去50年年均气温、冬季均温都在以0.16℃/10a、0.27℃/10a速率在上升，而年均相对湿度则以-0.45%/10a的速率在下降，显示其气候变暖、干旱化趋势显著。同时，其森林病虫灾害面积则以58125hm2/10a速率扩增。研究以相关分析、主成分分析法对所选24个气候要素降维、筛选，以逐步回归法构建模型，以小波分析研究区内时空变化等分析两者关系。得到16（与病害发生）或17（与病虫害与虫害发生）个显著相关气候要素，最大正相关要素为夏季均温滑动均值等，而最大负相关要素三者均为温湿系数滑动均值（温湿系数=年均相对湿度/年均温）；获得4或5个主成分，而代表温度或温湿度联合变量特征主成分贡献率最高（病虫害、虫害：41.43%，病害：42%）；建立3个以森林病虫灾害为纵轴、温湿系数滑动均值作横轴且具预测能力的最优回归模型（Y病虫害=3.582×106-7.750×105 X、Y虫害=-6.375×105X+2.95×106与Y病害=-1.375×105X+6.321×105），其线性拟合度分别为77.9%、79.1%与56.7%，平均预测准确率分别为66.2%、68.6%、47.9%，而研究区温湿系数滑动均值过去50年在显著下降，并在1993年后转向负距平。这说明气候变暖与环境干旱化对区内森林病虫灾害发生具驱动作用，促使其发生加重，尤其在进入20世纪90年代后表现更烈。得知温湿系数滑动均值具29a周期变化，若以1993年为起始，不考虑非气候要素作用，则其病虫害加重态势将持续到2022年。在空间上，过去50年赣南温湿系数滑动均值最低，预判其森林病虫灾害将较其他区域发生更重，而赣东、北温湿系数滑动均值倾向率最高，预判其森林病虫灾害发生的变动性可能更大。以上结果说明，气候变暖与环境干旱加重江西省森林病虫灾害，赣南应为重点防治工作区域，要加强赣东、北的监测预警工作。 Abstract:Over the past 50 years, the climate has changed rapidly with global warming and land surface drying, which has been accompanied by increased forest loss and damage resulting from pests and diseases. Climate data (e.g., air temperature, relative humidity, and sunshine data) from 1961 to 2010 showed that the climate in the Jiangxi Province was warming significantly, with the annual mean temperature increasing by 0.16℃ per 10 years, and the winter mean temperature rising by 0.27℃ per 10 years. The Jiangxi Province climate was also drying throughout this period (annual mean relative humidity decreased -0.45% per 10 years). In addition, forest pests and disease occurrence from 1992 to 2010 showed that in Jiangxi, the area affected by of forest diseases and pests increased significantly, with 58,125 hm2 per 10 years. Pearson correlation and principal component analyses showed that 16 (for forest diseases) or 17 (for forest pests) climate elements were significantly related to the forest loss. From these individual elements, the most positively correlated was a 9-year sliding average of summer mean temperature, and the most negatively correlated component was a 9-year sliding average of hydrothermal coefficient (annual mean temperature/annual mean relative humidity). Amongst the four or five principal components, the variables temperature and temperature-humidity contributed most to explaining forest area loss (41.43% and 42.0%, respectively). In stepwise regression analyses, three optimal regression models (Total:Y=3.582×106 -7.750×105X, forest pest:Y=-6.375×105X+2.95×106, forest diseases:Y=-1.375×105X+6.321×105) were analyzed to describe the forest loss (Y) by the 9-year sliding average of temperature humidity coefficient (X). The three models showed a linear fit of 77.9%, 79.1%, and 56.7% and a prediction accuracy of 66.2%, 68.6%, and 47.9%, respectively. A declining trend in the sliding average of temperature humidity coefficient was observed over the past 50 years, for which the anomaly transferred from positive to negative in 1993. This indicates that climate warming and droughts could have aggravated the forest loss and damage over the past 50 years, especially after 1990s. A wavelet analysis showed a 29-year periodicity in the temperature humidity coefficient. If this anomaly started in 1993, the forest loss trend could potentially be relieved by the end of 2022. In Jiangxi, the temperature humidity coefficient significantly increases from the south to the north, suggesting that forest diseases and pest disasters should be more extreme in Gan Nan than in other areas. In addition, since the change rate of climate warming or drought was higher in Gan Dong and Gan Bei, a high variability of forest diseases and pest disasters can be expected there in the future. Overall, our results suggest that climate warming and environmental drought aggravates forest diseases and pest disasters in Jiangxi. They furthermore emphasize that Gan Nan could be a key area in preventing and controlling the effects of forest diseases and pest disasters, whereas the monitoring efforts in Gan Dong and Gan Bei should be increased. 参考文献 相似文献 引证文献