| 摘要: |
| 咸海流域是绿色丝绸之路经济带的重要枢纽。但是地表水的逐年减少，对咸海流域的生态环境产生了恶劣的影响，严重阻碍了绿色丝绸之路经济带的建设。本文基于Google Earth Engine（GEE）平台上的1992—2018年Landsat影像，采用水体频率的方法提取咸海流域地表水信息，对咸海流域地表水的时空变化进行了研究，并绘制分布图。同时采用LandTrendr算法生成了地表水面积变化图。通过线性回归模型分析咸海流域地表水面积变化与气候变化、人为因素之间的内在联系。结果表明：咸海流域的地表水面积呈现递减趋势，其中以咸海为主的全年水体面积减少最为明显。2010年之后，整个流域地表水的减少趋于稳定。影响咸海流域地表水变化的主要驱动因素为气候因素中的温度。本研究可为后续确定咸海流域的地表水减少的成因、影响，以及水资源保护研究提供有力的技术支持。同时也为绿色丝绸之路经济带的建设提供科学支持。 |
| 关键词: 地表水 水体频率 LandTrendr算法 Google Earth Engine 咸海流域 驱动因素分析 |
| DOI：10.7515/JEE212012 |
| CSTR：32259.14.JEE212012 |
| 分类号: |
| 基金项目:国家自然科学基金项目（41971310）；天津市自然科学基金项目（18JCYBJC90900） |
| 英文基金项目:National Natural Science Foundation of China (41971310); Natural Science Foundation of Tianjin, China (18JCYBJC90900) |
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| Long-term monitoring of surface water change of the Aral Sea Basin and the driving factors |
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SHI Jiancong, GUO Qiaozhen, ZHAO Shuang, SU Yiting, SHI Yanqing
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1. School of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, China
2. Coal Industry Taiyuan Design and Research Institute Group CO., Ltd., Taiyuan 030001, China
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| Abstract: |
| Background, aim, and scope The Aral Sea was once the fourth-largest inland lake in the world and the largest lake in the Aral Sea Basin. However, the surface water of the Aral Sea Basin has been decreasing since the 1960s because of the arid climate and rapid population growth. Afterward, the fragile ecosystem was facing more severe challenges. Consequently, the construction of the Green Silk Road Economic Belt was significantly hindered. Therefore, it is of great necessity for water protection and social development to monitor the change of surface water in the Aral Sea Basin. Materials and methods Based on the Landsat imagery between 1992 and 2018 from Google Earth Engine (GEE) platform, the water frequency was employed in this paper to investigate the surface water changes of the Aral Sea Basin, and the spatial and temporal distribution was mapped. Meanwhile, the LandTrendr algorithm was used to observe the annual descend of the surface water. The internal relationships among surface water area changes, climate change and human factors were analyzed with the linear regression model. The analysis suggested that the surface water area presented a decreasing trend from 1992 to 2018, especially the annual water area. However, the downward trend of surface water in the whole basin is likely to stabilize after 2010. Results (1) Year-long water area has the most significant decline trend. The seasonal water was usually susceptible to change in temperature and precipitation while its overall trend showed downward. (2) Anthropogenic factors had significant impacts on the surface water in the Aral Sea Basin. (3) According to the multiple linear regression model, precipitation has a positive correlation with the change of surface water, and the temperature has a significant negative correlation with the surface water. Discussion Climate (precipitation, evapotranspiration, temperature) and anthropogenic factors (area and yield of crop) had apparent influences on the surface water changes in the Aral Sea Basin. Specifically, the temperature had the most adverse effect. The yields and planting area of crop were negatively correlated with the change of surface water, reflecting the impact of human activities on the shrinking of surface water in the Aral Sea Basin. Therefore, in relevant areas, environmental protection policies should be made, and human activities should be rationally arranged to control the decrease in surface water of the Aral Sea Basin. Moreover, it provides an ecological foundation for the construction of the Green Silk Road Economic Belt and plays a crucial role in connecting Asia and Europe and improving the living standards of the people in Central Asia. Conclusions Temperature is the major factor reducing surface water in the Aral Sea Basin. Recommendations and perspectives This research can determine the reasons and influences of surface water reduction and offer powerful support to water protection. |
| Key words: surface water water frequency LandTrendr algorithm Google Earth Engine analysis of drive factors |
