1986—2019年黄河流域地表水体动态变化及其影响因素

doi:10.11821/dlxb202205008

地理学报 ›› 2022, Vol. 77 ›› Issue (5): 1153-1168.doi: 10.11821/dlxb202205008

1986—2019年黄河流域地表水体动态变化及其影响因素

李崇巍¹^,²(), 王志慧²^,³(), 汤秋鸿⁴, 胡青峰¹, 肖培青², 吕锡芝²^,³, 刘杨²

1.华北水利水电大学测绘与地理信息学院,郑州 450046
2.黄河水利科学研究院水利部黄土高原水土保持重点实验室,郑州 450003
3.黄河水利科学研究院河南省黄河流域生态环境保护与修复重点实验室,郑州 450003
4.中国科学院地理科学与资源研究所,北京 100101

收稿日期:2021-08-23 修回日期:2022-03-18 出版日期:2022-05-25 发布日期:2022-07-25
通讯作者: 王志慧(1985-), 男, 山西太原人, 博士, 高级工程师, 主要从事水循环与生态环境遥感研究。E-mail: wzh8588@aliyun.com
作者简介:李崇巍(1995-), 男, 黑龙江宝清人, 硕士生, 主要从事水文遥感研究。E-mail: lxy327115054@163.com
基金资助:
中国科协青年人才托举工程(2017QNRC023);国家自然科学基金项目(51779099);国家自然科学基金项目(42041006);中央级公益性科研院所基本科研业务费专项(HKY-JBYW-2020-09)

Dynamics of surface water area in the Yellow River Basin and its influencing mechanism during 1986-2019 based on Google Earth Engine

LI Chongwei¹^,²(), WANG Zhihui²^,³(), TANG Qiuhong⁴, HU Qingfeng¹, XIAO Peiqing², LYU Xizhi²^,³, LIU Yang²

1. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2. Key Laboratory of Soil and Water Conservation on the Loess Plateau of Ministy of Water Resources, Yellow River Instlitute of Hydraulic, Zhengzhou 450003, China
3. Henan Key Laboratory of Ecological Environment Protection and Restoration of the Yellow River Basin, Yellow River Instlitute of Hydraulic, Zhengzhou 450003, China
4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2021-08-23 Revised:2022-03-18 Published:2022-05-25 Online:2022-07-25
Supported by:
Young Elite Scientist Sponsorship by CAST(2017QNRC023);National Natural Science Foundation of China(51779099);National Natural Science Foundation of China(42041006);Special Research Fund of the YRIHR(HKY-JBYW-2020-09)

摘要/Abstract

摘要：

黄河流域水资源严重短缺,对地表水面积（SWA）开展动态监测有助于明晰地表水资源时空变化规律及其驱动机制。本文基于Google Earth Engine云平台技术,综合利用混合指数规则集、线性斜率、多元线性回归和偏微分分解等方法,揭示了黄河流域SWA的年际变化及其空间分异规律,厘定了降雨、温度、植被叶面积指数、前一年SWA和水利水保措施与人类用水活动等其他因素对SWA的影响量和相对影响率。结果表明：① 地表水体总体识别精度为97%。1986—2019年全流域永久性SWA年际增长速率49.82 km²/a,其中主河道区贡献83.2%,且2001年为SWA变化由减小到增加的转折点;季节性SWA年际减小速率-79.2 km²/a,其中子流域区贡献61.8%。② 除红碱淖SWA呈显著持续减小外,其他主要天然湖泊SWA均较为稳定;6个主河道大型水库中,小浪底和龙羊峡水库SWA增加趋势最为显著;在86个子流域中,50个子流域SWA呈增加趋势,主要分布于流域中下游。③ 非气象要素对SWA的影响均大于气象要素影响作用。降雨对SWA的增加作用最小,温度上升造成中游地区SWA减小,但却导致源区SWA增加。植被叶面积指数增加导致主河道区和子流域区SWA变化斜率分别增加10.12 km²/a和7.26 km²/a。其他因素对子流域区SWA增加呈负作用,这表明子流域内剧烈用水活动对SWA的减小作用大于水利水保措施对SWA的增加作用,但是分布于主河道中的大型梯级水库调蓄功能可显著提升其对主河道区SWA的增加作用。

关键词: 黄河流域, 地表水体面积, Google Earth Engine (GEE), 时空变化, 影响因素分析

Abstract:

The Yellow River Basin (YRB) has been facing severe water shortages, hence the monitoring of long-term dynamics of surface water area (SWA) is essential to better understand the spatial and temporal variation of surface water resources and its driving factors. In this study, the spatial and temporal change characteristics of SWA in the YRB were revealed, and then the impacts and relative impact rate of precipitation (Pre), temperature (Temp), leaf area index (LAI), SWA in the previous year (Pre_SWA) and residual factors (e.g. water conservation measures and human water use activities) on SWA were determined in the combination of water detection index, linear slope, multiple linear regression and partial differential decomposition. The results show that: (1) the overall accuracy of classification of surface water bodies is 97%. The increase rate of year-long SWA in the study area from 1986 to 2019 is 49.82 km²/a, of which 83.2% was contributed by the SWA increment from the main river channel area, and the year 2001 is the turning point of SWA trend from decreasing to increasing; the seasonal SWA decreased at a rate of -79.2 km²/a, of which 61.8% was contributed by the SWA decrease in the sub-basin areas. (2) The SWA changes of all major natural lakes are relatively stable, and the only decreasing trend of SWA was observed in the Hongjiannao lake; the SWA of Xiaolangdi and Longyangxia reservoirs changed significantly with an increasing trend among the large reservoirs in the main river channel, and SWA increasing trends can be observed in the 50 sub-basins located in the middle and lower reaches. (3) Precipitation had the least effect on the increasing trend of SWA, and warming caused a decrease of SWA in the middle reaches, but led to an increase of SWA in the source area. The impacts of vegetation greening on the SWA trend in the main channel area and sub-basin areas are 10.12 km²/a and 7.26 km²/a, respectively. Residual factors had a negative reffect on the SWA trend in the sub-basin areas, where the SWA reduction induced by human water use was much greater than the SWA increment induced by small water conservancy projects. However, residual factors had a positive effect on the SWA increase due to the great regulating storage capacity of large reservoirs in the main river channel area.

Key words: Yellow River Basin, surface water area, Google Earth Engine, spatio-temporal change, influencing factor

李崇巍, 王志慧, 汤秋鸿, 胡青峰, 肖培青, 吕锡芝, 刘杨. 1986—2019年黄河流域地表水体动态变化及其影响因素[J]. 地理学报, 2022, 77(5): 1153-1168.

LI Chongwei, WANG Zhihui, TANG Qiuhong, HU Qingfeng, XIAO Peiqing, LYU Xizhi, LIU Yang. Dynamics of surface water area in the Yellow River Basin and its influencing mechanism during 1986-2019 based on Google Earth Engine[J]. Acta Geographica Sinica, 2022, 77(5): 1153-1168.

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数据	时间范围	空间分辨率	时间分辨率
Landsat 5 TM	1986—2012	30 m	15 d
Landsat 7 ETM+	1999—2002	30 m	15 d
Landsat 8 OLI	2013—2019	30 m	15 d
ASTER DEM	-	30 m	-
GLASS	2000—2019	1 km	8 d
GranD(v1.3)	1986—2019	-	1 a
China lake dataset	1986—2019	-	1 a
降雨量、温度	1986—2019	295个站点	1 d

1986—2019年黄河流域地表水体动态变化及其影响因素

Dynamics of surface water area in the Yellow River Basin and its influencing mechanism during 1986-2019 based on Google Earth Engine

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