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干旱区地理 ›› 2024, Vol. 47 ›› Issue (8): 1263-1276.doi: 10.12118/j.issn.1000-6060.2024.166 cstr: 32274.14.ALG2024166

• 第三次新疆综合科学考察 • 上一篇    下一篇

东昆仑库木库里盆地典型湖泊水量蒸发损失估算

李稚1(), 朱成刚1(), 汪家友1,2, 刘永昌1,2, 王川1,2, 张雪琪1, 韩诗茹1,2, 方功焕1   

  1. 1.中国科学院新疆生态与地理研究所,荒漠与绿洲生态国家重点实验室/干旱区生态安全与可持续发展重点实验室,新疆 乌鲁木齐 830011
    2.中国科学院大学,北京 100049
  • 收稿日期:2024-03-13 修回日期:2024-03-26 出版日期:2024-08-25 发布日期:2024-09-02
  • 通讯作者: 朱成刚(1976-),男,高级工程师,主要从事生态水文过程研究. E-mail: zhuchg@ms.xjb.ac.cn
  • 作者简介:李稚(1987-),女,研究员,主要从事干旱区水循环与干旱演变研究. E-mail: liz@ms.xjb.ac.cn
  • 基金资助:
    第三次新疆科学考察——昆仑山北坡水资源开发潜力及利用途径科学考察项目(2021xjkk0100);新疆天山英才青年科技拔尖项目(2022TSYCCX0042)

Estimation of evaporation loss from typical lakes in the Kumukuli Basin, East Kunlun Mountains

LI Zhi1(), ZHU Chenggang1(), WANG Jiayou1,2, LIU Yongchang1,2, WANG Chuan1,2, ZHANG Xueqi1, HAN Shiru1,2, FANG Gonghuan1   

  1. 1. State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
    2. University of Chinese Academy of Science, Beijing 100049, China
  • Received:2024-03-13 Revised:2024-03-26 Published:2024-08-25 Online:2024-09-02

摘要:

湖泊水量蒸发损失估算对于应对干旱区水资源短缺及湖泊生态环境保护具有重要意义。计算分析了过去20 a东昆仑库木库里盆地典型湖泊实际蒸散发(ET)的时空变化特征,并基于经验公式估算了湖泊蒸发损失水量,同时利用随机森林模型,识别了影响湖泊水量蒸发损失变化的潜在因子。研究发现:(1) 2001—2020年东昆仑库木库里盆地的阿牙克库木湖、阿其克库勒湖和鲸鱼湖的年ET整体呈现先增加后减少又缓慢增加的波动下降趋势,波峰和波谷均分别出现在2004年和2012年左右,空间上表现为ET整体下降而湖泊边缘呈上升趋势。(2) 3个典型湖泊的ET在年内呈倒U形变化,其中阿牙克库木湖的ET在6月达到峰值,其他2个湖泊的ET均在7月达到峰值。(3) 2001—2020年3个典型湖泊的蒸发水量均呈不显著的增加趋势,其中阿牙克库木湖的蒸发水量最高,平均约为10.33×108 m³·a-1,阿其克库勒湖的蒸发水量次之(4.54×108 m³·a-1),鲸鱼湖的蒸发水量最低(3.33×108·m³·a-1)。(4) 结合随机森林模型分析显示,湖泊面积是影响湖泊蒸发水量的重要因素,经向风速、最高气温和降水的增加等因素也是驱动蒸发变化的重要原因,累计贡献率超过45%。

关键词: 高山湖泊面积变化, 蒸发损失水量, 驱动因素, 库木库里盆地, 东昆仑

Abstract:

Estimating lake water evaporation losses is of significant importance for addressing water scarcity in arid regions and for the protection of lake ecosystems. This study analyzed the spatiotemporal variations of actual evapotranspiration (ET) in three typical lakes within the Kumukuli Basin of East Kunlun Mountains over the past 20 years. Using empirical formulas, we estimated the evaporation losses and applied a random forest model to identify potential factors influencing changes in lake water evaporation. This study examines the spatiotemporal variation in ET of typical lakes in the Kumukuli Basin of East Kunlun Mountains using PML-V2 data, estimates water loss due to lake evaporation through empirical formulas, and explores the influencing factors of lake ET changes using a random forest model. Key findings include (1) From 2001 to 2020, the annual ET of Ayakkum Lake, Aqqikkol Lake, and Whale Lake exhibited a fluctuating trend, initially increasing, then decreasing, and subsequently showing a gradual increase, with peak and trough occurring in 2004 and 2012, respectively. (2) The ET of the three lakes demonstrated an inverted U-shaped pattern within the year, with Ayakkum Lake peaking in June and the other two lakes in July. Aqqikkol Lake exhibited a relatively gentle increase, while Whale Lake saw a significant rise from May to July, reaching 48.45 mm·month-1. (3) During the same period, the evaporation water volume of the three lakes increased, with Ayakkum Lake recording the highest at 10.33×108 m³·a-1, followed by Aqqikkol Lake at 4.54×108 m³·a-1, and Whale Lake at 3.33×108 m³·a-1. (4) Analysis using the random forest model indicates that lake area significantly influences evaporation volume. Additional factors, including increases in wind speed, maximum temperature, and precipitation, also drive evaporation changes, contributing over 45% cumulatively.

Key words: high mountain lake area change, evaporation loss water volume, driving factors, Kumukuli Basin, East Kunlun Mountains