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Article(id=1322566783895196543, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322566763112415546, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2025.01.601, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1736265600000, receivedDateStr=2025-01-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793254093383, onlineDateStr=2026-10-29, pubDate=1768406400000, pubDateStr=2026-01-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793254093383, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793254093383, creator=13701087609, updateTime=1793254093383, updator=13701087609, issue=Issue{id=1322566763112415546, tenantId=1146029695717560320, journalId=1281212831689347082, year='2026', volume='46', issue='1', pageStart='1', pageEnd='138', issueExtLink='null', onlineDate='null', pubDate='1768406400000', pubDateStr='2026-01-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793254088427, creator='13701087609', updateTime=1793254088427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=131, endPage=138, ext={EN=ArticleExt(id=1322566785715524480, articleId=1322566783895196543, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Groundwater Storage Variation in Dongting Lake Basin Based on GRACE, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

We quantify surface water storage anomalies using GRACE satellite Mascon data and hydrological model data combining with water level and area data, estimate groundwater storage anomalies in Dongting Lake basin based on water balance equation, analyze the spatiotemporal variation characteristics and influencing factors of groundwater storage anomalies. The results show that after incorporating changes in surface water storage, the correlation between the inverted groundwater storage anomalies at the lake scale and the observed groundwater data improved from 0.40 to 0.65, reaching 0.75 in areas with dense groundwater wells. The overall groundwater storage anomalies in Dongting Lake basin show a downward trend, with a loss rate of-5.05 mm/a. Spatially, there is a significant difference between the northern and southern areas. Precipitation influences groundwater storage anomalies through its impact on surface water, with some lag due to hydrological processes, while human activities have a limited effect. This study reduces uncertainties generated in separating surface water storage, providing data support for groundwater dynamic research and water resource management in Dongting Lake basin.

, authors=Yanbo WANG1, Taoyong JIN1, *, Mengkui LI1, Pingping HUANG1, authorsList=Yanbo WANG, Taoyong JIN, Mengkui LI, Pingping HUANG, authorCompany=null, correspAuthors=Taoyong JIN, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322566787565212562, articleId=1322566783895196543, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=利用卫星重力研究洞庭湖流域地下水储量变化, columnId=1322566785874908034, journalTitle=大地测量与地球动力学, columnName=特色栏目: 地震大地测量学, runingTitle=null, highlight=null, articleAbstract=

采用GRACE卫星Mascon数据及水文模型数据，结合水位和面积数据量化湖泊地表水储量异常，利用水量平衡方程反演估算洞庭湖流域地下水储量异常，分析地下水储量异常时空变化特征及其影响因素。结果表明，加入地表水储量变化后，基于GRACE反演的湖泊尺度地下水储量异常与实测地下水数据的相关性从0.40提升至0.65, 在水井密集区域，两者相关性达到0.75。洞庭湖流域整体地下水储量异常呈下降趋势，亏损速率为—5.05 mm/a, 且在空间上南北差异明显。降水通过对地表水的影响进而驱动地下水储量异常变化，但受水文过程影响存在一定滞后性，而人为活动影响较小。通过减少地表水储量分离过程中的不确定性，可为洞庭湖流域的地下水动态研究和水资源管理提供数据支撑。

, authors=王彦博1, 金涛勇1, *, 李孟奎1, 黄坪坪1, authorsList=王彦博, 金涛勇, 李孟奎, 黄坪坪, authorCompany=null, correspAuthors=金涛勇, authorNote=

王彦博, 硕士生, 主要从事卫星重力与水文学研究, E-mail:

, correspAuthorsNote=
金涛勇, 博士, 教授, 主要从事卫星大地测量学研究, E-mail:
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王彦博, 硕士生, 主要从事卫星重力与水文学研究, E-mail:

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journalId=1281212831689347082, articleId=1322566783895196543, language=EN, label=Tab. 1, caption=

The data used in this study

, figureFileSmall=null, figureFileBig=null, tableContent=
来源变量 分辨率时间跨度
空间时间
CSRMTWSA0.25°1个月2014-01—2021-12
GLDASSMSA0.25°1个月2014-01—2021-12
SWESA
CWSA
WGHM GWSA0.5°1个月2014-01—2019-12
SWSA
Sentinel-1A洞庭湖面积10 m7 d2015-01—2021-12
Landsat-8洞庭湖面积30 m16 d2014-01—2014-12
城陵矶站洞庭湖水位/1d2014-01—2021-12
IMERGMP0.1°1个月2014-01—2021-12
GLEAM v4.1aET0.1°1个月2014-01—2021-12
地下水位年鉴地下水位/1个月2014-01—2021-12
CMONOCGNSS/1 d2014-01-
2021-12
长江水利委员会GWSA/1 a2014-2021
湖南省水利厅人类用水/1 a2014-2021
), ArticleFig(id=1322566792204112835, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322566783895196543, language=CN, label=表1, caption=

本文所用数据

, figureFileSmall=null, figureFileBig=null, tableContent=
来源变量 分辨率时间跨度
空间时间
CSRMTWSA0.25°1个月2014-01—2021-12
GLDASSMSA0.25°1个月2014-01—2021-12
SWESA
CWSA
WGHM GWSA0.5°1个月2014-01—2019-12
SWSA
Sentinel-1A洞庭湖面积10 m7 d2015-01—2021-12
Landsat-8洞庭湖面积30 m16 d2014-01—2014-12
城陵矶站洞庭湖水位/1d2014-01—2021-12
IMERGMP0.1°1个月2014-01—2021-12
GLEAM v4.1aET0.1°1个月2014-01—2021-12
地下水位年鉴地下水位/1个月2014-01—2021-12
CMONOCGNSS/1 d2014-01-
2021-12
长江水利委员会GWSA/1 a2014-2021
湖南省水利厅人类用水/1 a2014-2021
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利用卫星重力研究洞庭湖流域地下水储量变化
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王彦博 1 , 金涛勇 1, * , 李孟奎 1 , 黄坪坪 1
大地测量与地球动力学 | 特色栏目: 地震大地测量学 2026,46(1): 131-138
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大地测量与地球动力学 |特色栏目: 地震大地测量学 2026 , 46 (1) : 131 -138
利用卫星重力研究洞庭湖流域地下水储量变化
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王彦博1 , 金涛勇1, * , 李孟奎1, 黄坪坪1
作者信息
  • 1 武汉大学测绘学院, 武汉, 430079
通讯作者:
金涛勇, 博士, 教授, 主要从事卫星大地测量学研究, E-mail:
作者简介:

王彦博, 硕士生, 主要从事卫星重力与水文学研究, E-mail:

Groundwater Storage Variation in Dongting Lake Basin Based on GRACE
Yanbo WANG1 , Taoyong JIN1, * , Mengkui LI1, Pingping HUANG1
Affiliations
  • 1 School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
出版时间: 2026-01-15 doi: 10.14075/j.jgg.2025.01.601
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摘要
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采用GRACE卫星Mascon数据及水文模型数据，结合水位和面积数据量化湖泊地表水储量异常，利用水量平衡方程反演估算洞庭湖流域地下水储量异常，分析地下水储量异常时空变化特征及其影响因素。结果表明，加入地表水储量变化后，基于GRACE反演的湖泊尺度地下水储量异常与实测地下水数据的相关性从0.40提升至0.65, 在水井密集区域，两者相关性达到0.75。洞庭湖流域整体地下水储量异常呈下降趋势，亏损速率为—5.05 mm/a, 且在空间上南北差异明显。降水通过对地表水的影响进而驱动地下水储量异常变化，但受水文过程影响存在一定滞后性，而人为活动影响较小。通过减少地表水储量分离过程中的不确定性，可为洞庭湖流域的地下水动态研究和水资源管理提供数据支撑。

GRACE  /  地表水储量  /  地下水储量  /  洞庭湖流域  /  气候变化

We quantify surface water storage anomalies using GRACE satellite Mascon data and hydrological model data combining with water level and area data, estimate groundwater storage anomalies in Dongting Lake basin based on water balance equation, analyze the spatiotemporal variation characteristics and influencing factors of groundwater storage anomalies. The results show that after incorporating changes in surface water storage, the correlation between the inverted groundwater storage anomalies at the lake scale and the observed groundwater data improved from 0.40 to 0.65, reaching 0.75 in areas with dense groundwater wells. The overall groundwater storage anomalies in Dongting Lake basin show a downward trend, with a loss rate of-5.05 mm/a. Spatially, there is a significant difference between the northern and southern areas. Precipitation influences groundwater storage anomalies through its impact on surface water, with some lag due to hydrological processes, while human activities have a limited effect. This study reduces uncertainties generated in separating surface water storage, providing data support for groundwater dynamic research and water resource management in Dongting Lake basin.

GRACE  /  surface water storage  /  groundwater storage  /  Dongting Lake basin  /  climate change
王彦博, 金涛勇, 李孟奎, 黄坪坪. 利用卫星重力研究洞庭湖流域地下水储量变化. 大地测量与地球动力学, 2026 , 46 (1) : 131 -138 . DOI: 10.14075/j.jgg.2025.01.601
Yanbo WANG, Taoyong JIN, Mengkui LI, Pingping HUANG. Groundwater Storage Variation in Dongting Lake Basin Based on GRACE[J]. Journal of Geodesy and Geodynamics, 2026 , 46 (1) : 131 -138 . DOI: 10.14075/j.jgg.2025.01.601
正文
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定量监测地下水储量(GWS)在不同空间和时间尺度上的变化，对于水文循环研究、极端天气分析和水资源管理至关重要[1-2]。传统的地下水监测方法依赖于水井观测，但由于水井分布不均和比产率空间变化等因素，使其在大范围、长期监测中存在较大不确定性[3]。尽管陆面模型能够有效模拟大尺度、连续的地下水变化，但会低估GWS变化[4]。重力恢复与气候实验(GRACE)卫星任务已被广泛应用于监测陆地水储量(TWS)的时空变化[5]，基于水量平衡方程移去水文模型提供的地表水储量(SWS)、土壤水储量(SMS)、冠层含水量(CWS)、雪水当量(SWES)可以分离出GWS，进而估算GWS时空变化[4]。
当前GRACE卫星已成为全球和区域尺度GWS监测的有效手段，但大部分研究选择地表水资源匮乏、地下水抽取严重的区域，这些区域的SWS变化相较于SMS和GWS可以忽略不计。Feng等[1]结合水文模型模拟的土壤水储量评估华北平原地下水空间变化趋势和特征。使用单一的陆面模型评估地下水变化易产生较大不确定性，而使用多个水文模型的均值可以有效降低不确定性[6]。对于地表水丰富的区域，可以利用水文模型提供的SWS分离GWS[7]，或通过水文模型中的径流数据模拟地表水变化[3]。Chao等[8]采用多模型结合的方式精确估算长江流域GWS，并引入湖泊水储量，探讨其对流域水储量的贡献。但水文模型提供的SWS存在较大不确定性[4]，可能影响对GWS的精确评估。
洞庭湖流域是长江流域的重要子流域，遭遇过多次极端水文事件引发的管涌险情[2]。因此，研究洞庭湖流域地下水变化，对区域生态保护和水资源管理具有重要意义。为此，本文首先联合湖泊水位和面积数据计算洞庭湖流域SWS变化，然后利用水量平衡方程计算地下水储量异常(GWSA)，揭示洞庭湖流域GWSA时空变化特征，并与地下水井观测数据进行对比验证。在此基础上，分析气象因素及人为活动对GWSA的影响，以期为极端水文事件响应和水资源管理提供参考。
1 数据
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1.1 研究区域
洞庭湖流域面积264 000 km2，位于长江中游南岸，范围为107°45′~114°15′E、24°38′~30°24′N(图 1)。流域内约有1 057个湖泊和248个中大型水库，其中，洞庭湖为全国第二大淡水湖，也是长江流域重要的调蓄湖泊。洞庭湖流域大部分区域为碳酸岩区，岩层具有较大的孔隙度且地表土层较薄，这一地质特征会促进地下水与地表水、降水之间的快速循环，但也导致地层裂缝等地质空洞的形成，地下水在此聚集，当积聚的地下水压力增大时，就可能引发管涌事件。
1.2 水文气象数据
1.2.1 GRACE陆地水储量变化
本文使用德克萨斯大学空间研究中心(CSR)发布的GRACE时变重力场RL06v02 Mascon产品(CSRM)。CSRM以等效水高的形式提供月度陆地水储量异常(TWSA)数据，相比球谐系数方法，Mascon方法通过引入正则化和地球物理约束，在解算过程中直接抑制噪声，无需依赖额外的空间滤波处理，可显著减少由于滤波导致的信号泄漏误差，特别是在陆地与海洋过渡区域。该方法还允许根据研究区特点优化Mascon单元分布和约束策略，从而进一步提高精度[9]。对于CSRM数据中缺失的月份，采用改进的MSSA方法进行填充[10]。
1.2.2 水文气象模型
全球陆地数据同化系统(GLDAS)是由NASA和NOAA联合发布的数据集，其通过整合遥感数据、陆面模型数据与实际地面观测数据，可提供覆盖全球的高质量陆地状态和通量产品[11]。其中，整合了Noah陆面模型的SMS在中国区域与GRACE反演的TWS相关性最高[12]。因此，本文基于GLDAS Noah(v2.1)数据集，从中选取SMS、SWES、CWS数据。时间分辨率为1个月，空间分辨率为0.25°。
气候变化会影响流域GWS[13]，结合降水、蒸散数据有助于深入分析洞庭湖流域地下水的气候影响因素。降水数据来自全球卫星降水计划(GPM)，选取0.1°月度IMERG产品(v07)。IMERG作为GPM生成的3级产品，极大推动了卫星遥感在降水反演领域的应用[14]。通过GLEAM(global land evaporation Amsterdam model)(v4.1a)全球土地蒸散模型提供的空间分辨率为0.1°的月度蒸散数据辅助分析洞庭湖流域GWS时空变化[15]。
1.2.3 洞庭湖水位面积数据
为获得洞庭湖湖泊水储量变化，采用城陵矶水文站提供的实测水位数据，以及卫星遥感获得的湖泊面积变化序列进行计算。其中，洞庭湖湖泊水位数据采用城陵矶水文站提供的日分辨率水位数据，将其取月均值；湖面面积数据采用Sentinel-1A和Landsat-8卫星遥感数据。Sentinel-1A可提供2015年以后高精度的水域月度面积产品，空间分辨率为10 m；Landsat-8可提供2014年湖泊面积数据，空间分辨率为30 m。
1.2.4 验证数据
为验证利用卫星重力获得的GWS变化，本文收集《中国地质环境监测地下水位年鉴》中的实测地下水数据，包含洞庭湖流域内共35个监测井2014—2021年每月的水位变化。监测井空间分布如图 1所示，主要分布于湖南省邵阳、湘潭、岳阳和郴州市内，在贵州省内仅有2口监测井。选用2个独立来源的数据集评估本文获取的GWS：1)《长江流域水资源公报》和《湖南省水资源公报》发布的2004—2021年年度地下水资源统计数据，包括洞庭湖流域总地下水储量及流域人类用水量；2)WGHM(v2.2d)水文模型提供的2004—2019年月度(空间分辨率为0.5°)GWSA[16]。此外，本文还使用研究范围内中国大陆构造环境监测网络(CMONOC)提供的GNSS地表垂直位移数据，以辅助验证地下水变化的可靠性。
因收集的地下水数据时间跨度为2014-01—2021-12，本文将研究时间聚焦于该阶段，除WGHM模型外其他数据均选择该时间段，详细数据信息见表 1。
2 方法
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2.1 GRACE计算GWS变化
GWS变化可利用水平衡方程进行估计。根据陆地水平衡方程可知，TWS是SMS、SWES、SWS、CWS和GWS的总和，表达式为：
$\begin{gathered}\Delta \mathrm{TWS}=\Delta \mathrm{SMS}+\Delta \mathrm{SWES}+\Delta \mathrm{SWS}+ \\\Delta \mathrm{CWS}+\Delta \mathrm{GWS}\end{gathered}$
因此，GWSA可表示为：
$\begin{aligned}\Delta \mathrm{GWSA}= & \Delta \mathrm{TWSA}-\Delta \mathrm{SMSA}-\Delta \mathrm{SWESA}- \\& \Delta \mathrm{SWSA}-\Delta \mathrm{CWSA}\end{aligned}$
其中，TWSA来自GRACE CSRM，SMSA、CWSA、SWESA来自GLDAS Noah，SWSA可通过湖泊水位和面积变化数据计算得到。在将GLDAS各变量代入式(2)之前，还需对上述变量作与GRACE数据相同的处理：在全球范围作60阶球谐展开并进行300 km高斯滤波，最后去除2004-2009年平均值。
2.2 SWS变化计算
联合遥感卫星获得的面积数据和城陵矶水文站提供的水位数据计算SWS变化。其中，水体面积提取采用Landsat-8卫星光学影像和Sentinel-1A卫星SAR影像。在对光学影像进行提取时，主要是基于水体与其他地物的光谱反射差异，采用归一化水体指数(NDWI)增强水体信息[17]；在对SAR影像进行提取时，主要是基于水体与其他地物的灰度差异，通过微波后向散射强度来反映。由于Sentinel-1A卫星的极化数据有限，本文结合SDWI指数[18]和原有双极化信息，以增强水体与地物的差异。然后采用OTSU全局阈值法[19]，提取2014-2021年洞庭湖不同时间的水体变化，最终得到水体淹没范围变化图，并计算湖泊面积。
通过面积和水位数据计算SWS变化。假设湖泊为倒金字塔形状，在2次连续测量之间，湖泊体积遵循以下公式变化[20]：
$\Delta V=\frac{\left(H_{i+1}-H_i\right)\left(S_{i+1}+S_i+\sqrt{S_{i+1} \times S_i}\right)}{3}$
式中，ΔV是每月相对水量变化；Hi、Hi+1分别为i月、i+1月的水位；Si、Si+1分别为i月、i+1月的水体面积。以城陵矶站水位为例，ΔV即为洞庭湖水储量变化。
首先采用线性插值方法将水位数据转换为与遥感面积数据相同的时间分辨率；然后计算每月的平均水位和面积，以此为基础，应用式(3)计算洞庭湖每月SWS变化；最后按照洞庭湖湖泊格网在整个洞庭湖流域格网的占比，利用面积加权方式将洞庭湖SWS变化转化得到整个流域的格网SWS变化。
2.3 实测GWS变化及格网化
基于水井观测的地下水位，通过下式近似得到GWS变化：
$\Delta \mathrm{GWS}=S_y \cdot \Delta \mathrm{GWL}$
式中，ΔGWL为地下水位变化；Sy为比产率。对于所有地下水井数据，首先取月均值，然后减去2014-2021年平均值。GRACE CSRM数据已减去2004-2009年平均值，而由于缺失对应时间段的水井数据，减去2014-2021年平均值可去除现有数据范围内由长期气候变化或大尺度水文循环所产生的系统性信号，有助于捕捉水文变化特征。由于缺少实测资料以确定流域的比产率，参考Richey等[21]的方法，选择中国科学院资源与环境科学数据中心提供的沉积土壤质地图，计算得到流域内比产率Sy为0.015。
使用TP插值实现GWSA的格网化，其基本原理为：当某一格网点(x0, y0)在时间t0存在缺失值时，在空间范围D和时间范围t内搜索非缺失值数据点，并将(x0, y0, t0)处的缺失值表示为寻找到的N个非缺失值的线性组合[22]：
$g w_{\mathrm{est}}=\frac{\sum\limits_{k=1}^n w_k g w_k}{\sum\limits_{k=1}^n w_k}$
$w_k=\frac{2-\left[\frac{\left(x_k-x_0\right)^2+\left(y_k-y_0\right)^2}{D^2}+\frac{\left(t_k-t_0\right)^2}{T^2}\right]}{2+\left[\frac{\left(x_k-x_0\right)^2+\left(y_k-y_0\right)^2}{D^2}+\frac{\left(t_k-t_0\right)^2}{T^2}\right]}$
式中，wk表示基于水井数据与缺失格网点的时空差距的权重值，距离和时间间隔越近，权重越高，使得插值结果能够充分保留邻近的时空信息；D设置为200 km，步长为1个月。
3 结果与讨论
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3.1 GWS变化精度分析
为验证遥感面积数据的可靠性，将城陵矶站实测水位数据与遥感面积数据进行对比。由图 2(a)可知，实测水位与遥感面积之间的相关系数为0.98(p＜0.01)，两者相关性良好。利用实测水位和面积变化计算得到洞庭湖尺度的SWSA与WGHM模型的相关性系数达到0.78，总体趋势基本一致，但实测数据计算的SWSA具有更好的季节性变化特征(图 2(b))。按照洞庭湖在整个洞庭湖流域格网中的面积占比，采用面积加权方式将计算的洞庭湖范围内SWSA分配到洞庭湖流域各格网上。
利用水量平衡方程从GRACE反演的TWSA中分离SMSA、CWSA、SWESA以及SWSA后得到GWSA，其中SMSA、CWSA、SWESA由GLDAS模型提供，SWSA由实测水位面积计算得到。在洞庭湖范围内，本文对考虑与未考虑SWSA计算的GWSA进行比较，并与WGHM模型提供的GWSA和水井数据计算的GWSA进行对比。结果表明，基于GLDAS模型计算的GWSA与实测水井结果相比，相关系数、纳什效率系数和均方根误差分别为0.40、0.14和129.3 mm；基于实测水位面积计算SWSA，由此得到的GWSA与实测水井结果相比，相关系数、纳什效率系数和均方根误差分别为0.65、0.31和117.03 mm；WGHM模型提供的GWSA与实测水井结果相比，相关系数、纳什效率系数和均方根误差分别为0.41、0.03和135.35 mm。可以看出，采用实测水位面积计算的GWSA与实测水井结果更为符合，相关性从0.40提升到0.65。说明引入实测湖泊数据能够有效提高GWSA估算精度，表明考虑地表水动态变化对于GRACE GWSA分析具有重要作用。
图 3(a)为基于GRACE计算的格网GWSA与实测水井计算的格网化GWSA相关性分布，可以看出，相关性呈现从东南到西北逐渐递减的趋势，且与实测水井分布位置具有较强关系。由于水井聚集在流域东部，本文聚焦于该区域，如图 3(a)虚线部分所示。在东部水井密集区，GRACE GWSA与实测GWSA的相关性约为0.75，而在流域西部等水井稀少的区域，相关性则普遍较低，可能是实测水井数据格网化中产生较大误差，可靠性较低。
基于实测水井数据计算格网化GWSA，再进一步计算整个洞庭湖流域实测GWSA的月均变化，并将其与GRACE反演结果、WGHM模型结果和水文公报结果进行对比，结果如图 3(b)所示。可以看出，在洞庭湖流域水井密集区，GRACE GWSA与实测GWSA的相关系数为0.75(p < 0.01)，两者一致性良好。GRACE GWSA的年平均值与水文公报的总地下水资源量的相关系数为0.72，说明GRACE GWSA结果较优。对于WGHM GWSA，其与实测GWSA的相关系数为0.67，整体存在1个月的时延。2014-2016年，流域GWSA整体逐年上升，但在WGHM GWSA中难以体现。相比之下，WGHM虽能反映总体趋势，但会低估GWS变化，且未能捕捉部分高频信号，导致在振幅上存在差异[23]。
此外，本文还将GRACE GWSA与GNSS地表垂直位移进行对比，结果如图 3(c)所示。可以看出，站点HNLY地表垂直位移的下降速率与对应格网点GRACE GWSA的长期变化趋势相近，二者相关系数为0.96，进一步证明了GRACE GWSA的可靠性。
3.2 GWS变化时空特征分析
从图 3(c)可以看出，2014-2021年，研究区GWSA呈现长期下降趋势，年变化率约为-5.05 mm/a(p < 0.01)，并呈现出明显的季节性波动。其中2014-2016年，研究区GWSA逐渐上升，并于2016年达到峰值；2017-2018年，GWSA大幅下降，并在2018年出现谷值；2019-2021年，GWSA呈现先大幅上升、后大幅下降的变化趋势。
使用改进的Mann-Kendall方法[24]计算整片流域中各格网点在研究时段内的变化趋势(图 4(a))，并给出周年项振幅的空间分布(图 4(b))。可以看出，GWS在空间上表现出明显的纵向分区特征，其平均年变化速率呈现由西北向东南逐渐下降的趋势。洞庭湖的分隔作用导致GWS变化在不同区域存在显著差异：在靠近洞庭湖区域，GWS总体呈盈余趋势，变化速率约为1.10~3.26 mm/a；而靠近江西省区域则表现出下降趋势，变化速率约为-2.2 ~-0.9 mm/a。流域内GWS整体呈下降趋势。从图 4(b)可以看出，洞庭湖区域地下水周年项振幅较大，波动剧烈。从时间序列中移去长期趋势、周年、半周年等季节性信号后，得到GWSA的低频变化部分，可反映流域内发生的极端水文事件影响，结果如图 4(c)所示。可以看出，在2018年和2021年发生极端干旱事件，而在2016年和2019年发生洪水事件。
3.3 GWS变化影响因素分析
根据流域水量平衡方程，降水作为流域的最大水量输入，是包括GWS在内的TWS的重要来源，同时也主导了其季节和年际变化[25]。本文从湖泊和流域两个空间尺度分析降水和蒸散对洞庭湖和洞庭湖流域GWS变化的影响。
图 5(a)为2014-2021年洞庭湖的SWSA、GWSA和降水时序变化，可以看出，SWSA和GWSA在每年雨季达到峰值，尤其是2017和2020年，最大降雨量超过500 mm。最低SWSA往往出现在8—9月，这是浅层含水层的典型特征[26]。在此期间，GWS和SWS之间存在时延，可能与GWS和SWS之间的补给机制有关。地表水体的干旱状态需要经过2~3个月才能在地下水系统中得到反映，从而导致地下水枯竭后难以迅速恢复[3]。自2017年起，GWSA年平均值大幅增加，而且降水也出现较大程度的增加，表明充足的降水能够有效促进洞庭湖区域地下水的恢复。
图 5(b)为洞庭湖流域的GWSA、降水和蒸散时序变化。其中，降水与GWSA的相关系数为0.61，且与蒸散变化的季节性波动具有较好的一致性。但降水与GWSA的振幅并不完全一致，表明并非所有降水都转化为地下水。这主要是因为GWS变化不仅受降水影响，还受到地下水、地表径流和土壤水之间复杂水文过程的调节作用。若降水与地下水之间滞后时间较长，可能是由于地下水的补给量较低、地下水层水位较深或地下水损耗过大，从而导致补给所需的时间延长[27]。
由于2016年和2018年洞庭湖流域GWS发生剧烈波动，为了进一步研究造成空间波动的影响因素，具体分析2016-06-11及2018-06-11逐月的GWS变化、降水和蒸散变化的空间分布，结果如图 6、7所示。
由图 6可知，GWS与降雨和蒸散密切相关。从6月份开始，雨带在湘江、资江、沅江及洞庭湖区之间频繁摆动，带来大量降水，特别是在湘潭、岳阳、益阳等地区的月降水量超过400 mm，使得GWSA和蒸散显著增加。大部分地区在6月份的月降水量超过300 mm(图 6(b1))，强降水持续1个月后，GWSA剧烈上升，特别是在洞庭湖附近区域，GWSA增幅远高于其他地区。同时，7—8月份蒸散作用显著增加，整个流域的平均月蒸散量超过140 mm(图 6(c2)、6(c3))。8—9月份的反复强降水使得洞庭湖流域的GWSA维持在较高水平(图 6(b3)、6(b4))。进入冬季后，降水对GWSA的影响逐渐减弱，10月份的降水仅对衡阳一带的GWSA下降速率产生轻微影响。
由图 7可知，2018年洞庭湖流域的降水量显著低于2016年。值得注意的是，虽然7月份流域西部降水强度较高，但由于蒸散较强，GWSA未立即出现明显变化，而是在9—10月份才表现出相应的增加。此外，全年较低的降水量及较高的蒸散使得流域东部的GWSA维持在较低水平。总体而言，伴随着降水带的移动，GWSA也表现出相应的变化趋势。
此外，为分析人为活动对GWSA的潜在影响，对2014-2021年的洞庭湖流域GWS与4类用水(工业用水、农业用水、居民生活用水和生态环境用水)量进行比较分析。将4类人类用水量转化为整个流域的等效水高，并与GWS年均值进行对比，计算二者之间的相关性。结果表明，工业用水、农业用水、居民生活用水和生态环境用水的年变化率分别为0.06 mm/a、0.35 mm/a、0.28 mm/a和0.23 mm/a，表明人类用水的年变化幅度较为平稳，变化幅度较小；4类人类用水量与GWS之间的相关系数分别为-0.04、-0.05、0.18和-0.12，均呈现较低的相关性。研究表明[28]，农业用水对地下水的影响较土地利用更为显著，因此土地利用与GWS的相关性应更低。开采地下水是人类影响流域GWS最直接的方式，湖南省水资源公报显示，洞庭湖流域地下水开采量年均值为13.34亿m3(等效水高为9.14 mm)，集中在湘阴及汨罗等位于流域东南部的县市，且该区域GWS在空间分布上逐年下降(图 4(a))。在2018年沅江市第三水厂启用前后，沅江市市区的地下水开采量从6万m3/d下降至2万m3/d，沅江市周边的地下水位也逐渐上升[29]，说明开采量与地下水年变化率相关。结果表明，人类用水对GWS变化的影响相对有限，进一步验证了洞庭湖流域GWS变化主要受气候变化控制。
4 结语
收起
本文基于GRACE时变重力场确定TWS变化，结合卫星遥感、实测数据及水文模型输出的多源数据，计算洞庭湖流域的SWSA，并通过水量平衡方程得到GWSA。研究评估2014-2021年洞庭湖流域GWS时空变化特征，并分析其影响因素，得到以下结论：
1) 湖泊水储量是水量平衡方程中不可忽略的输出项，引入该变量后，在湖泊尺度上，GRACE GWSA与实测GWSA的相关系数和纳什效率系数从0.40和0.14提高至0.65和0.31，均方根误差从129.30 mm降低至117.03 mm；在水井密集的流域东部，GRACE GWSA与实测GWSA的相关系数达到0.75，表明GRACE GWSA具有较高的可靠性。
2) 在2014-2021年，洞庭湖流域GWSA的下降速率为-5.05 mm/a(p < 0.01)，在空间上表现出明显的纵向分区特征，平均年变化速率由西北向东南逐渐下降。
3) GWS变化主要受气候变化控制，洞庭湖流域GWS随降水带的移动表现出相应的空间变化趋势，而人类活动对地下水储量的影响有限。
基金
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  • 国家自然科学基金(42192534)
  • 湖北省自然科学基金(2022CFA090)
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2026年第46卷第1期
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doi: 10.14075/j.jgg.2025.01.601
  • 接收时间：2025-01-08
  • 首发时间：2026-10-29
  • 出版时间：2026-01-15
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  • 收稿日期：2025-01-08
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国家自然科学基金(42192534)
湖北省自然科学基金(2022CFA090)
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    1 武汉大学测绘学院, 武汉, 430079

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金涛勇, 博士, 教授, 主要从事卫星大地测量学研究, E-mail:
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