东昆仑库木库里盆地典型湖泊水量蒸发损失估算

doi:10.12118/j.issn.1000-6060.2024.166

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

• 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.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. 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/Abstract

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

李稚, 朱成刚, 汪家友, 刘永昌, 王川, 张雪琪, 韩诗茹, 方功焕. 东昆仑库木库里盆地典型湖泊水量蒸发损失估算[J]. 干旱区地理, 2024, 47(8): 1263-1276.

LI Zhi, ZHU Chenggang, WANG Jiayou, LIU Yongchang, WANG Chuan, ZHANG Xueqi, HAN Shiru, FANG Gonghuan. Estimation of evaporation loss from typical lakes in the Kumukuli Basin, East Kunlun Mountains[J]. Arid Land Geography, 2024, 47(8): 1263-1276.

图/表 12

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

参考文献 39

[1]	Steingruber S M, Bernasconi S M, Valenti G. Climate change-induced changes in the chemistry of a high-altitude mountain lake in the Central Alps[J]. Aquatic Geochemistry, 2021, 27(2): 105-126.
[2]	Zhao G, Gao H. Estimating reservoir evaporation losses for the United States: Fusing remote sensing and modeling approaches[J]. Remote Sensing of Environment, 2019(226): 109-124.
[3]	Wang W, Lee X, Xiao W, et al. Global lake evaporation accelerated by changes in surface energy allocation in a warmer climate[J]. Nature Geoscience, 2018, 11(6): 410-414.
[4]	Ali S, Ghosh N C, Singh R. Evaluating best evaporation estimate model for water surface evaporation in semi-arid region, India[J]. Hydrological Processes: An International Journal, 2008, 22(8): 1093-1106.
[5]	Morton F I. Climatological estimates of lake evaporation[J]. Water Resources Research, 1979, 15(1): 64-76.
[6]	艾尔肯·图尔荪, 玉素甫江·如素力, 崔一爽, 等. 2000—2019年新疆大型湖泊湖冰物候时空变化特征[J]. 干旱区地理, 2022, 45(5): 1440-1449.
	[Tuersun Aierken, Rusuli Yusufujiang, Cui Yishuang, et al. Temporal and spatial variations of lake ice phenology in large lakes of Xinjiang from 2000 to 2019[J]. Arid Land Geography, 2022, 45(5): 1440-1449.]
[7]	Cai Y, Ke C Q, Yao G H, et al. MODIS-observed variations of lake ice phenology in Xinjiang, China[J]. Climatic Change, 2020, 158(3): 575-592.
[8]	于革, 赖格英, 薛滨, 等. 中国西部湖泊水量对未来气候变化的响应——蒙特卡罗概率法在气候模拟输出的应用[J]. 湖泊科学, 2004, 16(3): 193-202.
	[Yu Ge, Lai Geying, Xue Bin, et al. Preliminary study on the responses of lake water from the western China to climate change in the future: Monte Carlo analysis applied in GCM simulations and lake water changes[J]. Journal of Lake Sciences, 2004, 16(3): 193-202.]
[9]	Zhang G, Bolch T, Yao T, et al. Underestimated mass loss from lake-terminating glaciers in the greater Himalaya[J]. Nature Geoscience, 2023, 16(4): 333-338.
[10]	Zhang H, Gorelick S M, Zimba P V, et al. A remote sensing method for estimating regional reservoir area and evaporative loss[J]. Journal of Hydrology, 2017, 555: 213-227.
[11]	Maestre-Valero J F, Martínez-Granados D, Martínez-Alvarez V, et al. Socio-economic impact of evaporation losses from reservoirs under past, current and future water availability scenarios in the semi-arid Segura Basin[J]. Water Resources Management, 2013(27): 1411-1426.
[12]	Friedrich K, Grossman R L, Huntington J, et al. Reservoir evaporation in the Western United States: Current science, challenges, and future needs[J]. Bulletin of the American Meteorological Society, 2018, 99(1): 167-187.
[13]	Li Z, Chen Y N, Shen Y J, et al. Analysis of changing pan evaporation in the arid region of Northwest China[J]. Water Resources Research, 2013, 49(4): 2205-2212.
[14]	McMahon T A, Peel M C, Lowe L, et al. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: A pragmatic synthesis[J]. Hydrology and Earth System Sciences, 2013, 17(4): 1331-1363.
[15]	Zhang Z, Zhang M, Cao C, et al. A dataset of microclimate and radiation and energy fluxes from the Lake Taihu eddy flux network[J]. Earth System Science Data, 2020, 12(4): 2635-2645.
[16]	Moran M S, Jackson R D. Assessing the spatial distribution of evapotranspiration using remotely sensed inputs[J]. Journal of Environmental Quality, 1991, 20(4): 725-737.
[17]	Harbeck G E. A practical field technique for measuring reservoir evaporation utilizing mass-transfer theory[M]. Washington: US Government Printing Office, 1962.
[18]	Penman H L. Natural evaporation from open water, bare soil and grass[J]. Proceedings Royal Society of London: Series A. Mathematical and Physical Sciences, 1948, 193(1032): 120-145.
[19]	Giadrossich F, Niedda M, Cohen D, et al. Evaporation in a Mediterranean environment by energy budget and Penman methods, Lake Baratz, Sardinia, Italy[J]. Hydrology and Earth System Sciences, 2015(19): 2451-2468.
[20]	El-Mahdy M E S, Abbas M S, Sobhy H M. Development of mass-transfer evaporation model for Lake Nasser, Egypt[J]. Journal of Water and Climate Change, 2021, 12(1): 223-237.
[21]	Gao H. Satellite remote sensing of large lakes and reservoirs: From elevation and area to storage[J]. Wiley Interdisciplinary Reviews: Water, 2015, 2(2): 147-157.
[22]	Rosenberry D O, Winter T C, Buso D C, et al. Comparison of 15 evaporation methods applied to a small mountain lake in the northeastern USA[J]. Journal of Hydrology, 2007, 340(3-4): 149-166.
[23]	McJannet D L, Cook F J, Burn S. Comparison of techniques for estimating evaporation from an irrigation water storage[J]. Water Resources Research, 2013, 49(3): 1415-1428.
[24]	柯长青. 湖泊遥感研究进展[J]. 海洋湖沼通报, 2004(4): 81-86.
	[Ke Changqing. A review of monitoring lake environment change by means of remote sensing[J]. Transactions of Oceanology and Limnology, 2004(4): 81-86.]
[25]	Zhang Y, Kong D, Gan R, et al. Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002—2017[J]. Remote Sensing Environment, 2019, 222, 165-182.
[26]	Lu H, Zhao R, Zhao L, et al. A contrarian growth: The spatiotemporal dynamics of open-surface water bodies on the northern slope of Kunlun Mountains[J]. Ecological Indicators, 2023, 157, 111249, doi: 10.1016/j.ecolind.2023.111249.
[27]	刘鸿波, 董理, 严若婧, 等. ERA5 再分析资料对中国大陆区域近地层风速气候特征及变化趋势再现能力的评估[J]. 气候与环境研究, 2021, 26(3): 299-311.
	[Liu Hongbo, Dong Li, Yan Ruojing, et al. Evaluation of near-surface wind speed climatology and long-term trend over China’s mainland region based on ERA5 reanalysis[J]. Climatic and Environmental Research, 2021, 26(3): 299-311.]
[28]	刘婷婷, 朱秀芳, 郭锐, 等. ERA5再分析降水数据在中国的适用性分析[J]. 干旱区地理, 2022, 45(1): 66-79.
	[Liu Tingting, Zhu Xiufang, Guo Rui, et al. Applicability of ERA5 reanalysis of precipitation data in China[J]. Arid Land Geography, 2022, 45(1): 66-79.]
[29]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[30]	Zhang Q, Zhang G, Zhang Y, et al. Coupling GEDI LiDAR and optical satellite for revealing large-scale maize lodging in Northeast China[J]. Earth’s Future, 2024, 12: e2023EF003590, doi: 10.1029/2023EF003590.
[31]	Mhawej M, Fadel A, Faour G. Evaporation rates in a vital lake: A 34-year assessment for the Karaoun Lake[J]. International Journal of Remote Sensing, 2020, 41(14): 5321-5337.
[32]	Zhang M, Chen F, Zhao H, et al. Recent changes of glacial lakes in the high mountain Asia and its potential controlling factors analysis[J]. Remote Sensing, 2021, 13(18): 3757, doi: 10.3390/rs13183757.
[33]	Zhao G, Li Y, Zhou L, et al. Evaporative water loss of 1.42 million global lakes[J]. Nature Communications, 2022, 13(1): 3686, doi: 10.1038/s41467-022-31125-6. pmid: 35764629
[34]	Wang B, Ma Y, Ma W, et al. Evaluation of ten methods for estimating evaporation in a small high-elevation lake on the Tibetan Plateau[J]. Theoretical and Applied Climatology, 2019, 136: 1033-1045.
[35]	Zhou W Y, Wang L Y, Li D, et al. Spatial pattern of lake evaporation increases under global warming linked to regional hydroclimate change[J]. Communications Earth & Environment, 2021, 1(1): 255, doi: 10.1038/s43247-021-00327-z.
[36]	Shi F, Li X, Zhao S, et al. Evaporation and sublimation measurement and modeling of an alpine saline lake influenced by freeze-thaw on the Qinghai-Tibet Plateau[J]. Hydrology and Earth System Sciences, 2024, 28(1): 163-178.
[37]	Wang L, Wang J, Wang L, et al. Lake evaporation and its effects on basin evapotranspiration and lake water storage on the inner Tibetan Plateau[J]. Water Resources Research, 2023, 59(10): e2022WR034030, doi: 10.1029/2022WR034030.
[38]	Zhang G, Bolch T, Chen W, et al. Comprehensive estimation of lake volume changes on the Tibetan Plateau during 1976—2019 and basin-wide glacier contribution[J]. Science of the Total Environment, 2021, 772: 145463, doi: 10.1016/j.scitotenv.2021.145463.
[39]	陈军, 汪永丰, 郑佳佳, 等. 中国阿牙克库木湖水量变化及其驱动机制[J]. 自然资源学报, 2019, 34(6): 1345-1356.
	[Chen Jun, Wang Yongfeng, Zheng Jiajia, et al. The changes in the water volume of Ayakekumu Lake based on satellite remote sensing data[J]. Journal of Natural Resources, 2019, 34(6): 1345-1356.]

东昆仑库木库里盆地典型湖泊水量蒸发损失估算

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

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 39

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	车彦军, 张明军, 陈亚宁, 朱成刚, 刘玉婷. 近30 a东昆仑阿牙克库木湖水量变化及其补给关系研究[J]. 干旱区地理, 2024, 47(7): 1116-1126.
[2]	周成, 赵亚玲, 任敏敏, 靳轶婷, 吕丝丝. 黄河流域国家湿地公园时空分异、重心演化与驱动因素分析[J]. 干旱区地理, 2024, 47(3): 506-514.
[3]	王剑, 管瑶, 贺兴宏, 代云豪, 陈艺伟, 王育强, 李会文, 范德宝. 2000—2021年图木舒克市生态环境质量动态监测与评价[J]. 干旱区地理, 2024, 47(3): 465-473.
[4]	王慧娴, 杨蓓, 杨宁君, 李璐瑶. 黄河流域旅游经济差异及关键驱动因素研究[J]. 干旱区地理, 2024, 47(3): 515-527.
[5]	任子健, 王江林, 徐贺年, 秦春. 祁连山地区过去500 a年代际旱涝事件演化及驱动因素分析[J]. 干旱区地理, 2024, 47(2): 214-227.
[6]	马振邦,王思鉴,李薇,吕鹏,郭晓东. 1986—2020年甘肃省六盘山片区贫困演变及启示[J]. 干旱区地理, 2023, 46(9): 1545-1555.
[7]	马文江, 白妙琴, 阿迪力·艾合买提, 张德平, 杨忠娜. “双碳”目标下新疆粮食绿色全要素生产率的时空分异及驱动因素分析[J]. 干旱区地理, 2023, 46(12): 2029-2041.
[8]	卓静,胡皓,何慧娟,王智,杨承睿. 陕北黄土高原生态脆弱性时空变异及驱动因素分析[J]. 干旱区地理, 2023, 46(11): 1768-1777.
[9]	杨丽,付玉慧,王姣姣. “一带一路”沿线中国省份旅游信息流时空分布格局及驱动因素分析[J]. 干旱区地理, 2023, 46(10): 1714-1722.
[10]	陶泽涪,王世清,孙丕苓,李凯迪,田文,韩潇潇. 中国北方农牧交错带耕地时空分异及驱动因素[J]. 干旱区地理, 2022, 45(1): 153-163.
[11]	江颂,蒙吉军. 土地利用冲突研究进展：内容与方法[J]. 干旱区地理, 2021, 44(3): 877-887.
[12]	刘晶, 何伦志. 丝绸之路经济带核心区新型城镇化驱动因素量化分析与对策——基于LASSO的变量筛选[J]. 干旱区地理, 2019, 42(6): 1478-1485.
[13]	邹雅婧, 闫庆武, 谭学玲, 王瑾. 渭北矿区土壤侵蚀评估及驱动因素分析 [J]. 干旱区地理, 2019, 42(6): 1387-1394.
[14]	张馨. 中国能源消费碳排放的时空差异及驱动因素研究[J]. 干旱区地理, 2018, 41(5): 1115-1122.
[15]	白勇, 高瑞忠, 王喜喜, 刘廷玺, 王威娜. 北方半干旱快速城镇化流域径流变化的驱动因素分析[J]. 干旱区地理, 2018, 41(2): 282-289.